Senior AI scientist, statistician, software developer, bioinformatician and writer

Ziaurrehman Tanoli, Adrià Fernández-Torras, Umut Onur Özcan, Aleksandr Kushnir, Kristen Michelle Nader, Yojana Gadiya, Laura Fiorenza, Aleksandr Ianevski, Markus Vähä-Koskela, Mitro Miihkinen, et al. (2025). Computational drug repurposing: approaches, evaluation of in silico resources and case studies . Nature Reviews Drug Discovery.

Ziaurrehman Tanoli, Adria Fernandez-Torras, UMUT ONUR ÖZCAN, Aleksandr Kushnir, Kristen Nader, Yojana Gadiya, Laura Fiorenza, Aleksandr Ianevski, Markus Vähä-Koskela, Mitro Miihkinen, et al.(2025). Computational drug repurposing . Nature Reviews Drug Discovery. Nature Research

Validation guidelines for drug-target prediction methods @article{466b38a0df6f496f9fe852d2efa015e3, title = "Validation guidelines for drug-target prediction methods", abstract = "Introduction: Mapping the interactions between pharmaceutical compounds and their molecular targets is a fundamental aspect of drug discovery and repurposing. Drug-target interactions are important for elucidating mechanisms of action and optimizing drug efficacy and safety profiles. Several computational methods have been developed to systematically predict drug-target interactions. However, computational and experimental validation of the drug-target predictions greatly vary across the studies. Areas covered: Through a PubMed query, a corpus comprising 3,286 articles on drug-target interaction prediction published within the past decade was covered. Natural language processing was used for automated abstract classification to study the evolution of computational methods, validation strategies and performance assessment metrics in the 3,286 articles. Additionally, a manual analysis of 259 studies that performed experimental validation of computational predictions revealed prevalent experimental protocols. Expert opinion: Starting from 2014, there has been a noticeable increase in articles focusing on drug-target interaction prediction. Docking and regression stands out as the most commonly used techniques among computational methods, and cross-validation is frequently employed as the computational validation strategy. Testing the predictions using multiple, orthogonal validation strategies is recommended and should be reported for the specific target prediction applications. Experimental validation remains relatively rare and should be performed more routinely to evaluate biological relevance of predictions.", keywords = "computational validation, drug repurposing, Drug-target interaction prediction, experimental validation, target activity mapping, 317 Pharmacy", author = "Ziaurrehman Tanoli and Aron Schulman and Tero Aittokallio", note = "Publisher Copyright: {\textcopyright} 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.", year = "2025", month = jan, day = "2", doi = "10.1080/17460441.2024.2430955", language = "English", volume = "20", pages = "31--45", journal = "Expert opinion on drug discovery", issn = "1746-0441", publisher = "Informa healthcare", number = "1", } . Expert opinion on drug discovery.

PGxDB @article{e22485dfe62649019ad52320a17235bd, title = "PGxDB: an interactive web-platform for pharmacogenomics research", abstract = "Pharmacogenomics, the study of how an individual's genetic makeup influences their response to medications, is a rapidly evolving field with significant implications for personalized medicine. As researchers and healthcare professionals face challenges in exploring the intricate relationships between genetic profiles and therapeutic outcomes, the demand for effective and user-friendly tools to access and analyze genetic data related to drug responses continues to grow. To address these challenges, we have developed PGxDB, an interactive, web-based platform specifically designed for comprehensive pharmacogenomics research. PGxDB enables the analysis across a wide range of genetic and drug response data types- informing cell-based validations and translational treatment strategies. We developed a pipeline that uniquely combines the relationship between medications indexed with Anatomical Therapeutic Chemical (ATC) codes with molecular target profiles with their genetic variability and predicted variant effects. This enables scientists from diverse backgrounds- including molecular scientists and clinicians- to link genetic variability to curated drug response variability and investigate indication or treatment associations in a single resource. With PGxDB, we aim to catalyze innovations in pharmacogenomics research, empower drug discovery, support clinical decision-making, and pave the way for more effective treatment regimens. PGxDB is a freely accessible database available at https://pgx-db.org/", keywords = "Drug, Implementation, Information, 3111 Biomedicine, 1182 Biochemistry, cell and molecular biology", author = "Nguyen, {Trinh Trung Duong} and Ziaurrehman Tanoli and Saad Hassan and {\"O}zcan, {Umut Onur} and Jimmy Caroli and Kooistra, {Albert J.} and Gloriam, {David E.} and Hauser, {Alexander S.}", year = "2025", month = jan, doi = "10.1093/nar/gkae1127", language = "English", volume = "53", pages = "D1486--D1497", journal = "Nucleic Acids Research", issn = "0305-1048", publisher = "Oxford University Press", number = "D1", } . Nucleic Acids Research.

Aron Schulman, Juho Rousu, Tero Aittokallio, Ziaurrehman Tanoli, Xin Gao (2024). Attention-based approach to predict drug–target interactions across seven target superfamilies . Bioinformatics.

Attention-based approach to predict drug–target interactions across seven target superfamilies @article{fe6b4e5031c24aaf9a0e740f4580d81f, title = "Attention-based approach to predict drug–target interactions across seven target superfamilies", abstract = "Motivation: Drug–target interactions (DTIs) hold a pivotal role in drug repurposing and elucidation of drug mechanisms of action. While single-targeted drugs have demonstrated clinical success, they often exhibit limited efficacy against complex diseases, such as cancers, whose development and treatment is dependent on several biological processes. Therefore, a comprehensive understanding of primary, secondary and even inactive targets becomes essential in the quest for effective and safe treatments for cancer and other indications. The human proteome offers over a thousand druggable targets, yet most FDA-approved drugs bind to only a small fraction of these targets. Results: This study introduces an attention-based method (called as MMAtt-DTA) to predict drug–target bioactivities across human proteins within seven superfamilies. We meticulously examined nine different descriptor sets to identify optimal signature descriptors for predicting novel DTIs. Our testing results demonstrated Spearman correlations exceeding 0.72 (P<0.001) for six out of seven superfamilies. The proposed method outperformed fourteen state-of-the-art machine learning, deep learning and graph-based methods and maintained relatively high performance for most target superfamilies when tested with independent bioactivity data sources. We computationally validated 185 676 drug–target pairs from ChEMBL-V33 that were not available during model training, achieving a reasonable performance with Spearman correlation >0.57 (P<0.001) for most superfamilies. This underscores the robustness of the proposed method for predicting novel DTIs. Finally, we applied our method to predict missing bioactivities among 3492 approved molecules in ChEMBL-V33, offering a valuable tool for advancing drug mechanism discovery and repurposing existing drugs for new indications.", keywords = "317 Pharmacy, 113 Computer and information sciences", author = "Aron Schulman and Juho Rousu and Tero Aittokallio and Ziaurrehman Tanoli", note = "Publisher Copyright: {\textcopyright} The Author(s) 2024.", year = "2024", month = aug, doi = "10.1093/bioinformatics/btae496", language = "English", volume = "40", journal = "Bioinformatics", issn = "1367-4803", publisher = "Oxford University Press", number = "8", } . Bioinformatics.

Mining drug-target interactions from biomedical literature using chemical and gene descriptions-based ensemble transformer model @article{a71ee7c0422f46a395c2e87b4d929181, title = "Mining drug-target interactions from biomedical literature using chemical and gene descriptions-based ensemble transformer model", abstract = "Drug-target interactions (DTIs) play a pivotal role in drug discovery, as it aims to identify potential drug targets and elucidate their mechanism of action. In recent years, the application of natural language processing (NLP), particularly when combined with pre-trained language models, has gained considerable momentum in the biomedical domain, with the potential to mine vast amounts of texts to facilitate the efficient extraction of DTIs from the literature. In this article, we approach the task of DTIs as an entity-relationship extraction problem, utilizing different pre-trained transformer language models, such as BERT, to extract DTIs. Our results indicate that an ensemble approach, by combining gene descriptions from the Entrez Gene database with chemical descriptions from the Comparative Toxicogenomics Database (CTD), is critical for achieving optimal performance. The proposed model achieves an F1 score of 80.6 on the hidden DrugProt test set, which is the top-ranked performance among all the submitted models in the official evaluation. Furthermore, we conduct a comparative analysis to evaluate the effectiveness of various gene textual descriptions sourced from Entrez Gene and UniProt databases to gain insights into their impact on the performance. Our findings highlight the potential of NLP-based text mining using gene and chemical descriptions to improve drug-target extraction tasks. Datasets utilized in this study are accessible at https://dtis.drugtargetcommons.org/.", keywords = "3111 Biomedicine", author = "Jehad Aldahdooh and Ziaurrehman Tanoli and Jing Tang", year = "2024", month = jul, day = "24", doi = "10.1093/bioadv/vbae106", language = "English", volume = "4", journal = "Bioinformatics advances", issn = "2635-0041", publisher = "Oxford University Press", number = "1", } . Bioinformatics advances.

RepurposeDrugs @article{e2390e7cd79a422792fe2fbf015afd89, title = "RepurposeDrugs: an interactive web-portal and predictive platform for repurposing mono- and combination therapies", abstract = "RepurposeDrugs (https://repurposedrugs.org/) is a comprehensive web-portal that combines a unique drug indication database with a machine learning (ML) predictor to discover new drug-indication associations for approved as well as investigational mono and combination therapies. The platform provides detailed information on treatment status, disease indications and clinical trials across 25 indication categories, including neoplasms and cardiovascular conditions. The current version comprises 4314 compounds (approved, terminated or investigational) and 161 drug combinations linked to 1756 indications/conditions, totaling 28 148 drug–disease pairs. By leveraging data on both approved and failed indications, RepurposeDrugs provides ML-based predictions for the approval potential of new drug–disease indications, both for mono- and combinatorial therapies, demonstrating high predictive accuracy in cross-validation. The validity of the ML predictor is validated through a number of real-world case studies, demonstrating its predictive power to accurately identify repurposing candidates with a high likelihood of future approval. To our knowledge, RepurposeDrugs web-portal is the first integrative database and ML-based predictor for interactive exploration and prediction of both single-drug and combination approval likelihood across indications. Given its broad coverage of indication areas and therapeutic options, we expect it accelerates many future drug repurposing projects.", keywords = "clinical trial outcome prediction, clinical trials, computational drug repurposing, drug discovery, drug repositioning, drug–disease associations, repurposing drug combinations, 317 Pharmacy", author = "Aleksandr Ianevski and Aleksandr Kushnir and Kristen Nader and Mitro Miihkinen and Henri Xhaard and Tero Aittokallio and Ziaurrehman Tanoli", note = "Publisher Copyright: {\textcopyright} The Author(s) 2024. Published by Oxford University Press.", year = "2024", month = jul, day = "1", doi = "10.1093/bib/bbae328", language = "English", volume = "25", journal = "Briefings in Bioinformatics", issn = "1467-5463", publisher = "Oxford University Press", number = "4", } . Briefings in Bioinformatics.

Aleksandr Ianevski, Aleksandr Kushnir, Kristen Nader, Mitro Miihkinen, Henri Xhaard, Tero Aittokallio, Ziaurrehman Tanoli (2024). RepurposeDrugs: an interactive web-portal and predictive platform for repurposing mono- and combination therapies . Briefings in Bioinformatics.

Peptriever @article{c6267fbcf2184eebbc433bc9c2c341bb, title = "Peptriever: a Bi-Encoder approach for large-scale protein-peptide binding search", keywords = "1182 Biochemistry, cell and molecular biology", author = "Roni Gurvich and Gal Markel and Ziaurrehman Tanoli and Tomer Meirson", year = "2024", month = may, day = "2", doi = "10.1093/bioinformatics/btae303", language = "English", volume = "40", journal = "Bioinformatics", issn = "1367-4803", publisher = "Oxford University Press", number = "5", } . Bioinformatics.

Jehad Aldahdooh, Ziaurrehman Tanoli, Jing Tang, Yoshihiro Yamanishi (2024). Mining drug–target interactions from biomedical literature using chemical and gene descriptions-based ensemble transformer model . Bioinformatics Advances.

Ezequiel Anokian, Judith Bernett, Adrian Freeman, Markus List, Luc&#237;a Prieto Santamar&#237;a, Ziaurrehman Tanoli, Sarah Bonnin (2024). Machine Learning and Artificial Intelligence in Drug Repurposing—Challenges and Perspectives . Drug Repurposing.

“Be sustainable”: EOSC-Life recommendations for implementation of FAIR principles in life science data handling @article{69dbef22b4dc475caf4ffcad6fea6fce, title = "“Be sustainable”: EOSC-Life recommendations for implementation of FAIR principles in life science data handling", abstract = "The main goals and challenges for the life science communities in the Open Science framework are to increase reuse and sustainability of data resources, software tools, and workflows, especially in large-scale data-driven research and computational analyses. Here, we present key findings, procedures, effective measures and recommendations for generating and establishing sustainable life science resources based on the collaborative, cross-disciplinary work done within the EOSC-Life (European Open Science Cloud for Life Sciences) consortium. Bringing together 13 European life science research infrastructures, it has laid the foundation for an open, digital space to support biological and medical research. Using lessons learned from 27 selected projects, we describe the organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the life sciences. We show how EOSC-Life provides a model for sustainable data management according to FAIR (findability, accessibility, interoperability, and reusability) principles, including solutions for sensitive- and industry-related resources, by means of cross-disciplinary training and best practices sharing. Finally, we illustrate how data harmonisation and collaborative work facilitate interoperability of tools, data, solutions and lead to a better understanding of concepts, semantics and functionalities in the life sciences.", keywords = "113 Computer and information sciences", author = "Romain David and Arina Rybina and Jean-Marie Burel and Jean-Karim Heriche and Pauline Audergon and Jan-Willem Boiten and Frederik Coppens and Sara Crockett and Katrina Exter and Sven Fahrner and Maddalena Fratelli and Carole Goble and Philipp Gormanns and Tobias Grantner and Bj{\"o}rn Gr{\"u}ning and Gurwitz, {Kim Tamara} and Hancock, {John M} and Henriette Harmse and Petr Holub and Nick Juty and Geoffrey Karnbach and Emma Karoune and Antje Keppler and Jessica Klemeier and Carla Lancelotti and Jean-Luc Legras and Lister, {Allyson L} and Longo, {Dario Livio} and Rebecca Ludwig and B{\'e}n{\'e}dicte Madon and Marzia Massimi and Vera Matser and Rafaele Matteoni and Mayrhofer, {Michaela Th} and Christian Ohmann and Maria Panagiotopoulou and Helen Parkinson and Isabelle Perseil and Claudia Pfander and Roland Pieruschka and Michael Raess and Andreas Rauber and Richard, {Audrey S} and Paolo Romano and Antonio Rosato and Alex S{\'a}nchez-Pla and Susanna-Assunta Sansone and Ugis Sarkans and Beatriz Serrano-Solano and Jing Tang and Ziaurrehman Tanoli and Jonathan Tedds and Harald Wagener and Martin Weise and Westerhoff, {Hans V} and Rudolf Wittner and Jonathan Ewbank and Niklas Blomberg and Philip Gribbon", year = "2023", month = nov, day = "15", doi = "10.15252/embj.2023115008", language = "English", journal = "EMBO Journal", issn = "0261-4189", publisher = "Springer Science and Business Media Deutschland GmbH", } . EMBO Journal.

Swapnil Potdar, Filipp Ianevski, Aleksandr Ianevski, Ziaurrehman Tanoli, Krister Wennerberg, Brinton Seashore-Ludlow, Olli Kallioniemi, P&#228;ivi &#214;stling, Tero Aittokallio, Jani Saarela (2023). Breeze 2.0: an interactive web-tool for visual analysis and comparison of drug response data . Nucleic Acids Research.

Breeze 2.0 @article{4dfe908849ec46fab4ec2833910f42bb, title = "Breeze 2.0: an interactive web-tool for visual analysis and comparison of drug response data", abstract = "Functional precision medicine (fPM) offers an exciting, simplified approach to finding the right applications for existing molecules and enhancing therapeutic potential. Integrative and robust tools ensuring high accuracy and reliability of the results are critical. In response to this need, we previously developed Breeze, a drug screening data analysis pipeline, designed to facilitate quality control, dose-response curve fitting, and data visualization in a user-friendly manner. Here, we describe the latest version of Breeze (release 2.0), which implements an array of advanced data exploration capabilities, providing users with comprehensive post-analysis and interactive visualization options that are essential for minimizing false positive/negative outcomes and ensuring accurate interpretation of drug sensitivity and resistance data. The Breeze 2.0 web-tool also enables integrative analysis and cross-comparison of user-uploaded data with publicly available drug response datasets. The updated version incorporates new drug quantification metrics, supports analysis of both multi-dose and single-dose drug screening data and introduces a redesigned, intuitive user interface. With these enhancements, Breeze 2.0 is anticipated to substantially broaden its potential applications in diverse domains of fPM.", keywords = "1182 Biochemistry, cell and molecular biology", author = "Swapnil Potdar and Filipp Ianevski and Aleksandr Ianevski and Ziaurrehman Tanoli and Krister Wennerberg and Brinton Seashore-Ludlow and Olli Kallioniemi and P{\"a}ivi {\"O}stling and Tero Aittokallio and Jani Saarela", year = "2023", month = jul, day = "5", doi = "10.1093/nar/gkad390", language = "English", volume = "51", pages = "W57--W61", journal = "Nucleic Acids Research", issn = "0305-1048", publisher = "Oxford University Press", number = "W1", } . Nucleic Acids Research.

SynergyFinder Plus: Toward Better Interpretation and Annotation of Drug Combination Screening Datasets @article{04c71fe866a6421ca3ac67b89ffd09ba, title = "SynergyFinder Plus: Toward Better Interpretation and Annotation of Drug Combination Screening Datasets", abstract = "Combinatorial therapies have been recently proposed to improve the efficacy of anticancer treatment. The SynergyFinder R package is a software used to analyze pre-clinical drug combination datasets. Here, we report the major updates to the SynergyFinder R package for improved interpretation and annotation of drug combination screening results. Unlike the existing implementations, the updated SynergyFinder R package includes five main innovations. 1) We extend the mathematical models to higher-order drug combination data analysis and implement dimension reduction techniques for visualizing the synergy landscape. 2) We provide a statistical analysis of drug combination synergy and sensitivity with confidence intervals and P values. 3) We incorporate a synergy barometer to harmonize multiple synergy scoring methods to provide a consensus metric for synergy. 4) We evaluate drug combination synergy and sensitivity to provide an unbiased interpretation of the clinical potential. 5) We enable fast annotation of drugs and cell lines, including their chemical and target information. These annotations will improve the interpretation of the mechanisms of action of drug combinations. To facilitate the use of the R package within the drug discovery community, we also provide a web server at www.synergyfinderplus.org as a user-friendly interface to enable a more flexible and versatile analysis of drug combination data.", keywords = "SynergyFinder, Drug combinations, Synergy modeling, Drug discovery, Drug combination sensitivity analysis, 3111 Biomedicine, 113 Computer and information sciences", author = "Shuyu Zheng and Wenyu Wang and Jehad Aldahdooh and Alina Malyutina and Tolou Shadbahr and Ziaurrehman Tanoli and Alberto Pessia and Jing Tang", year = "2022", month = dec, day = "20", doi = "10.1016/j.gpb.2022.01.004", language = "English", volume = "20", pages = "587--596", journal = "Genomics, Proteomics & Bioinformatics", issn = "1672-0229", publisher = "Beijing Genomics Institute", number = "3", } . Genomics, Proteomics & Bioinformatics.

DrugRepo: a novel approach to repurposing drugs based on chemical and genomic features @article{027eeab2fab04d238cc1aa5d3d9cf84d, title = "DrugRepo: a novel approach to repurposing drugs based on chemical and genomic features", abstract = "The drug development process consumes 9–12 years and approximately one billion US dollars in costs. Due to the high finances and time costs required by the traditional drug discovery paradigm, repurposing old drugs to treat cancer and rare diseases is becoming popular. Computational approaches are mainly data-driven and involve a systematic analysis of different data types leading to the formulation of repurposing hypotheses. This study presents a novel scoring algorithm based on chemical and genomic data to repurpose drugs for 669 diseases from 22 groups, including various cancers, musculoskeletal, infections, cardiovascular, and skin diseases. The data types used to design the scoring algorithm are chemical structures, drug-target interactions (DTI), pathways, and disease-gene associations. The repurposed scoring algorithm is strengthened by integrating the most comprehensive manually curated datasets for each data type. At DrugRepo score ≥ 0.4, we repurposed 516 approved drugs across 545 diseases. Moreover, hundreds of novel predicted compounds can be matched with ongoing studies at clinical trials. Our analysis is supported by a web tool available at: http://drugrepo.org/.", keywords = "317 Pharmacy", author = "Yinyin Wang and Jehad Aldahdooh and Yingying Hu and Hongbin Yang and Markus V{\"a}h{\"a}-Koskela and Jing Tang and Ziaurrehman Tanoli", year = "2022", month = dec, doi = "10.1038/s41598-022-24980-2", language = "English", volume = "12", journal = "Scientific Reports", issn = "2045-2322", publisher = "Nature Research ", number = "1", } . Scientific Reports.

Using BERT to identify drug-target interactions from whole PubMed @article{43d886ee69ae4e41bbf896d73cdc0b69, title = "Using BERT to identify drug-target interactions from whole PubMed", abstract = "Drug-target interactions (DTIs) are critical for drug repurposing and elucidation of drug mechanisms, and are manually curated by large databases, such as ChEMBL, BindingDB, DrugBank and DrugTargetCommons. However, the number of curated articles likely constitutes only a fraction of all the articles that contain experimentally determined DTIs. Finding such articles and extracting the experimental information is a challenging task, and there is a pressing need for systematic approaches to assist the curation of DTIs. To this end, we applied Bidirectional Encoder Representations from Transformers (BERT) to identify such articles. Because DTI data intimately depends on the type of assays used to generate it, we also aimed to incorporate functions to predict the assay format.", keywords = "BERT, BERT for biomedical data, Bidirectional encoder representations from transformers, Bioactivity data, Biomedical text mining, Drug repurposing, Drug target interaction prediction, INFORMATION, Mining drug target interactions, PREDICTION, 3111 Biomedicine", author = "Jehad Aldahdooh and Markus V{\"a}h{\"a}-Koskela and Jing Tang and Ziaurrehman Tanoli", year = "2022", month = jun, day = "21", doi = "10.1186/s12859-022-04768-x", language = "English", volume = "23", journal = "BMC Bioinformatics", issn = "1471-2105", publisher = "BioMed Central", number = "1", } . BMC Bioinformatics.

Shuyu Zheng, Wenyu Wang, Jehad Aldahdooh, Alina Malyutina, Tolou Shadbahr, Ziaurrehman Tanoli, Alberto Pessia, Jing Tang (2022). SynergyFinder Plus: Toward Better Interpretation and Annotation of Drug Combination Screening Datasets . Genomics, Proteomics & Bioinformatics.

A community challenge for a pancancer drug mechanism of action inference from perturbational profile data @article{fb57a2baf58f4e14827bf900951d0d5a, title = "A community challenge for a pancancer drug mechanism of action inference from perturbational profile data", abstract = "The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with similar to 400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among similar to 1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action.", keywords = "CANCER, CONNECTIVITY MAP, IDENTIFICATION, PREDICT, SIGNATURES, SIMILARITY, SMALL MOLECULES, TARGET, 1182 Biochemistry, cell and molecular biology", author = "{Douglass Jr.}, {Eugene F.} and Allaway, {Robert J.} and Bence Szalai and Wenyu Wang and Tingzhong Tian and Adri{\`a} Fern{\'a}ndez-Torras and Ron Realubit and Charles Karan and Shuyu Zheng and Alberto Pessia and Ziaurrehman Tanoli and Mohieddin Jafari and Fangping Wan and Shuya Li and Yuanpeng Xiong and Miquel Duran-Frigola and Martino Bertoni and Pau Badia-i-Mompel and L{\'i}dia Mateo and Oriol Guitart-Pla and Verena Chung and Jing Tang and Jianyang Zeng and Patrick Aloy and Julio Saez-Rodriguez and Justin Guinney and Gerhard, {Daniela S.} and Andrea Califano", year = "2022", month = jan, day = "18", doi = "10.1016/j.xcrm.2021.100492", language = "English", volume = "3", journal = "Cell Reports Medicine", issn = "2666-3791", publisher = "Cell Press, Elsevier, Inc.", number = "1", } . Cell Reports Medicine.

Minimal information for chemosensitivity assays (MICHA): a next-generation pipeline to enable the FAIRification of drug screening experiments @article{7de88f8fa25642b1af263f1cd0f5f2e5, title = "Minimal information for chemosensitivity assays (MICHA): a next-generation pipeline to enable the FAIRification of drug screening experiments", abstract = "Chemosensitivity assays are commonly used for preclinical drug discovery and clinical trial optimization. However, data from independent assays are often discordant, largely attributed to uncharacterized variation in the experimental materials and protocols. We report here the launching of Minimal Information for Chemosensitivity Assays (MICHA), accessed via https://micha-protocol.org. Distinguished from existing efforts that are often lacking support from data integration tools, MICHA can automatically extract publicly available information to facilitate the assay annotation including: 1) compounds, 2) samples, 3) reagents and 4) data processing methods. For example, MICHA provides an integrative web server and database to obtain compound annotation including chemical structures, targets and disease indications. In addition, the annotation of cell line samples, assay protocols and literature references can be greatly eased by retrieving manually curated catalogues. Once the annotation is complete, MICHA can export a report that conforms to the FAIR principle (Findable, Accessible, Interoperable and Reusable) of drug screening studies. To consolidate the utility of MICHA, we provide FAIRified protocols from five major cancer drug screening studies as well as six recently conducted COVID-19 studies. With the MICHA web server and database, we envisage a wider adoption of a community-driven effort to improve the open access of drug sensitivity assays.", keywords = "3121 General medicine, internal medicine and other clinical medicine, drug discovery, drug sensitivity assays, data integration tools, FAIR research data", author = "Ziaurrehman Tanoli and Jehad Aldahdooh and Farhan Alam and Yinyin Wang and Umair Seemab and Maddalena Fratelli and Petr Pavlis and Marian Hajduch and Florence Bietrix and Philip Gribbon and Andrea Zaliani and Hall, {Matthew D} and Min Shen and Kyle Brimacombe and Evgeny Kulesskiy and Jani Saarela and Krister Wennerberg and Markus V{\"a}h{\"a}-Koskela and Jing Tang", year = "2022", month = jan, doi = "10.1093/bib/bbab350", language = "English", volume = "23", journal = "Briefings in Bioinformatics", issn = "1477-4054", publisher = "Oxford University Press", number = "1", } . Briefings in Bioinformatics.

Anna Cicho{\'{n}}ska and Balaguru Ravikumar and Robert J. Allaway and Fangping Wan and Sungjoon Park and Olexandr Isayev and Shuya Li and Michael Mason and Andrew Lamb and Ziaurrehman Tanoli and Minji Jeon and Sunkyu Kim and Mariya Popova and Stephen Capuzzi and Jianyang Zeng and Kristen Dang and Gregory Koytiger and Jaewoo Kang and Carrow I. Wells and Timothy M. Willson and Mehmet Tan and Chih-Han Huang and Edward S. C. Shih and Tsai-Min Chen and Chih-Hsun Wu and Wei-Quan Fang and Jhih-Yu Chen and Ming-Jing Hwang and Xiaokang Wang and Marouen Ben Guebila and Behrouz Shamsaei and Sourav Singh and Thin Nguyen and Mostafa Karimi and Di Wu and Zhangyang Wang and Yang Shen and Hakime Öztürk and Elif Ozkirimli and Arzucan Özgür and Hansaim Lim and Lei Xie and Georgi K. Kanev and Albert J. Kooistra and Bart A. Westerman and Panagiotis Terzopoulos and Konstantinos Ntagiantas and Christos Fotis and Leonidas Alexopoulos and Dimitri Boeckaerts and Michiel Stock and Bernard De Baets and Yves Briers and Yunan Luo and Hailin Hu and Jian Peng and Tunca Dogan and Ahmet S. Rifaioglu and Heval Atas and Rengul Cetin Atalay and Volkan Atalay and Maria J. Martin and Minji Jeon and Junhyun Lee and Seongjun Yun and Bumsoo Kim and Buru Chang and G{\'{a}}bor Turu and {\'{A}}d{\'{a}}m Mis{\'{a}}k and Bence Szalai and L{\'{a}}szl{\'{o}} Hunyady and Matthias Lienhard and Paul Prasse and Ivo Bachmann and Julia Ganzlin and Gal Barel and Ralf Herwig and Davor Or{\v{s}}oli{\'{c}} and Bono Lu{\v{c}}i{\'{c}} and Vi{\v{s}}nja Stepani{\'{c}} and Tomislav {\v{S}}muc and Tudor I. Oprea and Avner Schlessinger and David H. Drewry and Gustavo Stolovitzky and Krister Wennerberg and Justin Guinney and Tero Aittokallio and and and and and and and and and and and and and and and and and and and(2021). Crowdsourced mapping of unexplored target space of kinase inhibitors . Nature Communications. 12. (1). Springer Science and Business Media {LLC}

Artificial intelligence, machine learning, and drug repurposing in cancer @article{900e385c47c44079afab0f51a1e475cb, title = "Artificial intelligence, machine learning, and drug repurposing in cancer", abstract = "Introduction: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artificial intelligence (AI) approaches have been developed for systematic identification of drug repurposing leads based on big data resources, hence further accelerating and de-risking the drug development process by computational means.Areas covered: The authors focus on supervised ML and AI methods that make use of publicly available databases and information resources. While most of the example applications are in the field of anticancer drug therapies, the methods and resources reviewed are widely applicable also to other indications including COVID-19 treatment. A particular emphasis is placed on the use of comprehensive target activity profiles that enable a systematic repurposing process by extending the target profile of drugs to include potent off-targets with therapeutic potential for a new indication.Expert opinion: The scarcity of clinical patient data and the current focus on genetic aberrations as primary drug targets may limit the performance of anticancer drug repurposing approaches that rely solely on genomics-based information. Functional testing of cancer patient cells exposed to a large number of targeted therapies and their combinations provides an additional source of repurposing information for tissue-aware AI approaches.", keywords = "Drug repurposing, precision oncology, machine learning, artificial intelligence, target repositioning, 113 Computer and information sciences", author = "Ziaurrehman Tanoli and Markus V{\"a}h{\"a}-Koskela and Tero Aittokallio", year = "2021", month = sep, day = "2", doi = "10.1080/17460441.2021.1883585", language = "English", volume = "16", pages = "977--989", journal = "Expert opinion on drug discovery", issn = "1746-0441", publisher = "Informa healthcare", number = "9", } . Expert opinion on drug discovery.

Ziaurrehman Tanoli and Jehad Aldahdooh and Farhan Alam and Yinyin Wang and Umair Seemab and Maddalena Fratelli and Petr Pavlis and Marian Hajduch and Florence Bietrix and Philip Gribbon and Andrea Zaliani and Matthew D Hall and Min Shen and Kyle Brimacombe and Evgeny Kulesskiy and Jani Saarela and Krister Wennerberg and Markus Vähä-Koskela and Jing Tang(2021). Minimal information for chemosensitivity assays (MICHA): a next-generation pipeline to enable the FAIRification of drug screening experiments . Briefings in Bioinformatics. Oxford University Press ({OUP})

Crowdsourced mapping of unexplored target space of kinase inhibitors <head> <META HTTP-EQUIV="Refresh" CONTENT="0;URL=/servlet/useragent"> </head> . Nature Communications.

Crowdsourced mapping of unexplored target space of kinase inhibitors @article{2f56b6ea8fa8467ead790b1917014431, title = "Crowdsourced mapping of unexplored target space of kinase inhibitors", abstract = "Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts.", keywords = "DRUG, PHARMACOLOGY, PREDICTION, DISCOVERY, PACKAGE, 317 Pharmacy", author = "{IDG-DREAM Drug-Kinase Binding} and Anna Cichonska and Balaguru Ravikumar and Allaway, {Robert J.} and Fangping Wan and Sungjoon Park and Olexandr Isayev and Shuya Li and Michael Mason and Andrew Lamb and Ziaurrehman Tanoli and Minji Jeon and Sunkyu Kim and Mariya Popova and Stephen Capuzzi and Jianyang Zeng and Kristen Dang and Gregory Koytiger and Jaewoo Kang and Wells, {Carrow I.} and Willson, {Timothy M.} and Oprea, {Tudor I.} and Avner Schlessinger and Drewry, {David H.} and Gustavo Stolovitzky and Krister Wennerberg and Justin Guinney and Tero Aittokallio", year = "2021", month = jun, day = "3", doi = "10.1038/s41467-021-23165-1", language = "English", volume = "12", journal = "Nature Communications", issn = "2041-1723", publisher = "Nature Publishing Group", } . Nature Communications.

Identification of Celecoxib targeted proteins using label-free thermal proteome profiling on rat hippocampus @article{015b62b6d8f449749219c5636d477469, title = "Identification of Celecoxib targeted proteins using label-free thermal proteome profiling on rat hippocampus", abstract = "Celecoxib is one of the most common medicines for treating inflammatory diseases. Recently, it has been shown that celecoxib is associated with implications in complex diseases such as Alzheimer{\textquoteright}s disease and cancer, as well as with cardiovascular risk assessment and toxicity, suggesting that celecoxib may affect multiple unknown targets. In this project, we detected targets of celecoxib within the nervous system using a label-free TPP (Thermal Proteome Profiling) method. First, proteins of the rat hippocampus were treated with multiple drug concentrations and temperatures. Next, we separated the soluble proteins from the denatured and sedimented total protein load by ultracentrifugation. Subsequently, the soluble proteins were analyzed by nano-liquid chromatography-mass spectrometry to determine the identity of the celecoxib targeted proteins based on structural changes by thermal stability variation of targeted proteins towards higher solubility in the higher temperatures. In the analysis of the soluble protein extract at 67 centigrade, 44 proteins were uniquely detected in drug-treated samples out of all 478 identified proteins at this temperature. Rab4a, one out of these 44 proteins, has previously been reported as one of the celecoxib off-targets in the rat CNS. Furthermore, we provide more molecular details through biomedical enrichment analysis to explore the potential role of all detected proteins in the biological systems. We show that the determined proteins play a role in the signaling pathways related to neurodegenerative disease - and cancer pathways. Finally, we fill out molecular supporting evidence for using celecoxib towards the drug repurposing approach by exploring drug targets. Significance Statement In this study, we determined forty-four off-target proteins of celecoxib, a non-steroidal anti-inflammatory, and one of the most common medicines for treating inflammatory diseases. We showed that these proteins play a role in the signaling pathways related to neurodegenerative disease and cancer pathways. Finally, we provided molecular supporting evidence for using celecoxib towards the drug repurposing approach by exploring drug targets.", keywords = "1182 Biochemistry, cell and molecular biology", author = "Elham Gholizadeh and Reza Karbalaei and Ali Khaleghian and Mona Salimi and Kambiz Gilany and Rabah Soliymani and Ziaurrehman Tanoli and Hassan Rezadoost and Marc Baumann and Mohieddin Jafari and Jing Tang", year = "2021", month = may, day = "1", doi = "10.1124/molpharm.120.000210", language = "English", volume = "99", pages = "308--318", journal = "Molecular pharmacology : an international journal", issn = "0026-895X", publisher = "Elsevier Inc. ", number = "5", } . Molecular pharmacology : an international journal.

(2021). Exploration of databases and methods supporting drug repurposing: a comprehensive survey . Briefings in bioinformatics.

(2021). Multi-modal meta-analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor . Molecular systems biology.

Multi-modal meta-analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor @article{9ea8f97177e641bab7806b1df419418f, title = "Multi-modal meta-analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor", abstract = "Molecular and functional profiling of cancer cell lines is subject to laboratory-specific experimental practices and data analysis protocols. The current challenge therefore is how to make an integrated use of the omics profiles of cancer cell lines for reliable biological discoveries. Here, we carried out a systematic analysis of nine types of data modalities using meta-analysis of 53 omics studies across 12 research laboratories for 2,018 cell lines. To account for a relatively low consistency observed for certain data modalities, we developed a robust data integration approach that identifies reproducible signals shared among multiple data modalities and studies. We demonstrated the power of the integrative analyses by identifying a novel driver gene, ECHDC1, with tumor suppressive role validated both in breast cancer cells and patient tumors. The multi-modal meta-analysis approach also identified synthetic lethal partners of cancer drivers, including a co-dependency of PTEN deficient endometrial cancer cells on RNA helicases.", keywords = "3122 Cancers, cancer driver, data integration, multi-omics data, reproducibility, synthetic lethality", author = "Alok Jaiswal and Prson Gautam and Pietil{\"a}, {Elina A} and Sanna Timonen and Nora Nordstr{\"o}m and Yevhen Akimov and Nina Sipari and Ziaurrehman Tanoli and Thomas Fleischer and Kaisa Lehti and Krister Wennerberg and Tero Aittokallio", year = "2021", month = mar, doi = "10.15252/msb.20209526", language = "English", volume = "17", journal = "Molecular Systems Biology", issn = "1744-4292", publisher = "Springer Science and Business Media Deutschland GmbH", number = "3", } . Molecular Systems Biology.

(2021). Identification of Celecoxib-Targeted Proteins Using Label-Free Thermal Proteome Profiling on Rat Hippocampus . Molecular pharmacology.

(2021). Artificial intelligence, machine learning, and drug repurposing in cancer . Expert opinion on drug discovery.

Exploration of databases and methods supporting drug repurposing @article{9ec717fbb758475b943c0f9621caa9cd, title = "Exploration of databases and methods supporting drug repurposing: a comprehensive survey", abstract = "Drug development involves a deep understanding of the mechanisms of action and possible side effects of each drug, and sometimes results in the identification of new and unexpected uses for drugs, termed as drug repurposing. Both in case of serendipitous observations and systematic mechanistic explorations, confirmation of new indications for a drug requires hypothesis building around relevant drug-related data, such as molecular targets involved, and patient and cellular responses. These datasets are available in public repositories, but apart from sifting through the sheer amount of data imposing computational bottleneck, a major challenge is the difficulty in selecting which databases to use from an increasingly large number of available databases. The database selection is made harder by the lack of an overview of the types of data offered in each database. In order to alleviate these problems and to guide the end user through the drug repurposing efforts, we provide here a survey of 102 of the most promising and drug-relevant databases reported to date. We summarize the target coverage and types of data available in each database and provide several examples of how multi-database exploration can facilitate drug repurposing.", keywords = "318 Medical biotechnology", author = "Rehman, {Zia ur} and Umair Seemab and Andreas Scherer and Krister Wennerberg and Jing Tang and Markus V{\"a}h{\"a}-Koskela", note = "M1 - bbaa003", year = "2020", month = feb, day = "14", doi = "10.1093/bib/bbaa003", language = "English", volume = "22", pages = "1656--1678", journal = "Briefings in Bioinformatics", issn = "1477-4054", publisher = "Oxford University Press", number = "2", } . Briefings in Bioinformatics.

Interactive visual analysis of drug-target interaction networks using Drug Target Profiler, with applications to precision medicine and drug repurposing @article{028e99e2651d40a093d317e63df313e9, title = "Interactive visual analysis of drug-target interaction networks using Drug Target Profiler, with applications to precision medicine and drug repurposing", abstract = "Knowledge of the full target space of drugs (or drug-like compounds) provides important insights into the potential therapeutic use of the agents to modulate or avoid their various on- and off-targets in drug discovery and precision medicine. However, there is a lack of consolidated databases and associated data exploration tools that allow for systematic profiling of drug target-binding potencies of both approved and investigational agents using a network-centric approach. We recently initiated a community-driven platform, Drug Target Commons (DTC), which is an open-data crowdsourcing platform designed to improve the management, reproducibility and extended use of compound-target bioactivity data for drug discovery and repurposing, as well as target identification applications. In this work, we demonstrate an integrated use of the rich bioactivity data from DTC and related drug databases using Drug Target Profiler (DTP), an open-source software and web tool for interactive exploration of drug-target interaction networks. DTP was designed for network-centric modeling of mode-of-action of multi-targeting anticancer compounds, especially for precision oncology applications. DTP enables users to construct an interaction network based on integrated bioactivity data across selected chemical compounds and their protein targets, further customizable using various visualization and filtering options, as well as cross-links to several drug and protein databases to provide comprehensive information of the network nodes and interactions. We demonstrate here the operation of the DTP tool and its unique features by several use cases related to both drug discovery and drug repurposing applications, using examples of anticancer drugs with shared target profiles. DTP is freely accessible at http://drugtargetprofiler.fimm.fi/.", keywords = "DATABASE, IMATINIB, INHIBITOR, KINASE, MUTATION, PREDICTION, SENSITIVITY, TOOL, drug mode-of-action, drug repurposing, drug-target interactions, network visualization, precision oncology, 1182 Biochemistry, cell and molecular biology", author = "Ziaurrehman Tanoli and Zaid Alam and Aleksandr Ianevski and Krister Wennerberg and Markus V{\"a}h{\"a}-Koskela and Tero Aittokallio", year = "2020", month = jan, doi = "10.1093/bib/bby119", language = "English", volume = "21", pages = "211--220", journal = "Briefings in Bioinformatics", issn = "1477-4054", publisher = "Oxford University Press", number = "1", } . Briefings in Bioinformatics.

(2019). Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen . Nature Communications.

(2019). Cartography of rhodopsin-like G protein-coupled receptors across vertebrate genomes . Scientific Reports.

DrugComb @article{2655f78a96a64733a732ac10bdb017bd, title = "DrugComb: an integrative cancer drug combination data portal", abstract = "Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal called DrugComb (https://drugcomb.fimm.fi) where the results of drug combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided a web server to analyze and visualize users{\textquoteright} own drug combination screening data. The users can also effectively participate a crowdsourcing data curation effect by depositing their data at DrugComb. To initiate the data repository, we collected 437 932 drug combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity of drug combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future drug combination discovery.", keywords = "IDENTIFY, SCREEN, SYNERGY, 3122 Cancers, 113 Computer and information sciences", author = "Bulat Zagidullin and Jehad Aldahdooh and Shuyu Zheng and Wenyu Wang and Yinyin Wang and Joseph Saad and Alina Malyutina and Mohieddin Jafari and Ziaurrehman Tanoli and Alberto Pessia and Jing Tang", year = "2019", month = jul, day = "2", doi = "10.1093/nar/gkz337", language = "English", volume = "47", pages = "W43--W51", journal = "Nucleic Acids Research", issn = "0305-1048", publisher = "Oxford University Press", number = "W1", } . Nucleic Acids Research.

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen @article{10779a778b764133bda813f9373c833c, title = "Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen", abstract = "The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca{\textquoteright}s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.", keywords = "ANDROGEN RECEPTOR, BREAST-CANCER, CELL, GENE, INHIBITION, LANDSCAPE, MUTATIONS, PATHWAY, RESISTANCE, RESOURCE, 317 Pharmacy, 3122 Cancers", author = "Menden, {Michael P.} and Dennis Wang and Mason, {Mike J.} and Bence Szalai and Bulusu, {Krishna C.} and Yuanfang Guan and Thomas Yu and Jaewoo Kang and Minji Jeon and Russ Wolfinger and Tin Nguyen and Mikhail Zaslavskiy and Jordi Abante and Abecassis, {Barbara Schmitz} and Nanne Aben and Delasa Aghamirzaie and Tero Aittokallio and Akhtari, {Farida S.} and Bissan Al-lazikani and Tanvir Alam and Amin Allam and Chad Allen and {de Almeida}, {Mariana Pelicano} and Doaa Altarawy and Vinicius Alves and Alicia Amadoz and Benedict Anchang and Antolin, {Albert A.} and Ash, {Jeremy R.} and Aznar, {Victoria Romeo} and Wail Ba-alawi and Moeen Bagheri and Vladimir Bajic and Gordon Ball and Ballester, {Pedro J.} and Delora Baptista and Christopher Bare and Mathilde Bateson and Andreas Bender and Denis Bertrand and Bhagya Wijayawardena and Boroevich, {Keith A.} and Evert Bosdriesz and Salim Bougouffa and Gergana Bounova and Thomas Brouwer and Barbara Bryant and Manuel Calaza and Alberto Calderone and Stefano Calza and Stephen Capuzzi and Jose Carbonell-Caballero and Daniel Carlin and Hannah Carter and Luisa Castagnoli and Remzi Celebi and Gianni Cesareni and Hyeokyoon Chang and Guocai Chen and Haoran Chen and Huiyuan Chen and Lijun Cheng and Ariel Chernomoretz and Davide Chicco and Kwang-Hyun Cho and Sunghwan Cho and Daeseon Choi and Jaejoon Choi and Kwanghun Choi and Minsoo Choi and Cock, {Martine De} and Elizabeth Coker and Isidro Cortes-Ciriano and Mikl{\'o}s Cserz{\"o} and Cankut Cubuk and Christina Curtis and Daele, {Dries Van} and Dang, {Cuong C.} and Tjeerd Dijkstra and Joaquin Dopazo and Sorin Draghici and Anastasios Drosou and Michel Dumontier and Friederike Ehrhart and Fatma-Elzahraa Eid and Mahmoud ElHefnawi and Haitham Elmarakeby and {van Engelen}, Bo and Engin, {Hatice Billur} and {de Esch}, Iwan and Chris Evelo and Falcao, {Andre O.} and Sherif Farag and Carlos Fernandez-Lozano and Kathleen Fisch and Asmund Flobak and Chiara Fornari and Foroushani, {Amir B. K.} and Fotso, {Donatien Chedom} and Denis Fourches and Stephen Friend and Arnoldo Frigessi and Feng Gao and Xiaoting Gao and Gerold, {Jeffrey M.} and Pierre Gestraud and Samik Ghosh and Jussi Gillberg and Antonia Godoy-Lorite and Lizzy Godynyuk and Adam Godzik and Anna Goldenberg and David Gomez-Cabrero and Mehmet Gonen and {de Graaf}, Chris and Harry Gray and Maxim Grechkin and Roger Guimera and Emre Guney and Benjamin Haibe-Kains and Younghyun Han and Takeshi Hase and Di He and Liye He and Heath, {Lenwood S.} and Hellton, {Kristoffer H.} and Manuela Helmer-Citterich and Hidalgo, {Marta R.} and Daniel Hidru and Hill, {Steven M.} and Sepp Hochreiter and Seungpyo Hong and Eivind Hovig and Ya-Chih Hsueh and Zhiyuan Hu and Huang, {Justin K.} and Huang, {R. Stephanie} and L{\'a}szl{\'o} Hunyady and Jinseub Hwang and Hwang, {Tae Hyun} and Woochang Hwang and Yongdeuk Hwang and Olexandr Isayev and {Don{\textquoteright}t Walk}, {Oliver Bear} and John Jack and Samad Jahandideh and Jiadong Ji and Yousang Jo and Kamola, {Piotr J.} and Kanev, {Georgi K.} and Loukia Karacosta and Mostafa Karimi and Samuel Kaski and Marat Kazanov and Khamis, {Abdullah M.} and Khan, {Suleiman Ali} and Kiani, {Narsis A.} and Allen Kim and Jinhan Kim and Juntae Kim and Kiseong Kim and Kyung Kim and Sunkyu Kim and Yongsoo Kim and Yunseong Kim and Kirk, {Paul D. W.} and Hiroaki Kitano and Gunter Klambauer and David Knowles and Melissa Ko and Alvaro Kohn-Luque and Kooistra, {Albert J.} and Kuenemann, {Melaine A.} and Martin Kuiper and Christoph Kurz and Mijin Kwon and {van Laarhoven}, Twan and Astrid Laegreid and Simone Lederer and Heewon Lee and Jeon Lee and Lee, {Yun Woo} and Eemeli Lepp_aho and Richard Lewis and Jing Li and Lang Li and James Liley and Lim, {Weng Khong} and Chieh Lin and Yiyi Liu and Yosvany Lopez and Joshua Low and Artem Lysenko and Daniel Machado and Neel Madhukar and Maeyer, {Dries De} and Malpartida, {Ana Belen} and Hiroshi Mamitsuka and Francesco Marabita and Kathleen Marchal and Pekka Marttinen and Daniel Mason and Alireza Mazaheri and Arfa Mehmood and Ali Mehreen and Magali Michaut and Miller, {Ryan A.} and Costas Mitsopoulos and Dezso Modos and Moerbeke, {Marijke Van} and Keagan Moo and Alison Motsinger-Reif and Rajiv Movva and Sebastian Muraru and Eugene Muratov and Mushthofa Mushthofa and Niranjan Nagarajan and Sigve Nakken and Aritro Nath and Pierre Neuvial and Richard Newton and Zheng Ning and Niz, {Carlos De} and Baldo Oliva and Catharina Olsen and Antonio Palmeri and Bhawan Panesar and Stavros Papadopoulos and Jaesub Park and Seonyeong Park and Sungjoon Park and Yudi Pawitan and Daniele Peluso and Sriram Pendyala and Jian Peng and Livia Perfetto and Stefano Pirro and Sylvia Plevritis and Regina Politi and Hoifung Poon and Eduard Porta and Isak Prellner and Kristina Preuer and Pujana, {Miguel Angel} and Ricardo Ramnarine and Reid, {John E.} and Fabien Reyal and Sylvia Richardson and Camir Ricketts and Linda Rieswijk and Miguel Rocha and Carmen Rodriguez-Gonzalvez and Kyle Roell and Daniel Rotroff and {de Ruiter}, {Julian R.} and Ploy Rukawa and Benjamin Sadacca and Zhaleh Safikhani and Fita Safitri and Marta Sales-Pardo and Sebastian Sauer and Moritz Schlichting and Seoane, {Jose A.} and Jordi Serra and Ming-Mei Shang and Alok Sharma and Hari Sharma and Yang Shen and Motoki Shiga and Moonshik Shin and Ziv Shkedy and Kevin Shopsowitz and Sam Sinai and Dylan Skola and Petr Smirnov and Soerensen, {Izel Fourie} and Peter Soerensen and Je-Hoon Song and Song, {Sang Ok} and Othman Soufan and Andreas Spitzmueller and Boris Steipe and Chayaporn Suphavilai and Tamayo, {Sergio Pulido} and David Tamborero and Jing Tang and Zia-ur-Rehman Tanoli and Marc Tarres-Deulofeu and Jesper Tegner and Liv Thommesen and Tonekaboni, {Seyed Ali Madani} and Hong Tran and Troyer, {Ewoud De} and Amy Truong and Tatsuhiko Tsunoda and G{\'a}bor Turu and Guang-Yo Tzeng and Lieven Verbeke and Santiago Videla and Consortium, {AstraZeneca-Sanger Drug Combination DREAM}", year = "2019", month = jun, day = "17", doi = "10.1038/s41467-019-09799-2", language = "English", volume = "10", journal = "Nature Communications", issn = "2041-1723", publisher = "Nature Publishing Group", number = "1", } . Nature Communications.

(2019). DrugComb: an integrative cancer drug combination data portal . Nucleic acids research.

Cartography of rhodopsin-like G protein-coupled receptors across vertebrate genomes @article{14501843104042ebb571cc7580a7db09, title = "Cartography of rhodopsin-like G protein-coupled receptors across vertebrate genomes", abstract = "We conduct a cartography of rhodopsin-like non-olfactory G protein-coupled receptors in the Ensembl database. The most recent genomic data (releases 90-92, 90 vertebrate genomes) are analyzed through the online interface and receptors mapped on phylogenetic guide trees that were constructed based on a set of similar to 14.000 amino acid sequences. This snapshot of genomic data suggest vertebrate genomes to harbour 142 clades of GPCRs without human orthologues. Among those, 69 have not to our knowledge been mentioned or studied previously in the literature, of which 28 are distant from existing receptors and likely new orphans. These newly identified receptors are candidates for more focused evolutionary studies such as chromosomal mapping as well for in-depth pharmacological characterization. Interestingly, we also show that 37 of the 72 human orphan (or recently deorphanized) receptors included in this study cluster into nineteen closely related groups, which implies that there are less ligands to be identified than previously anticipated. Altogether, this work has significant implications when discussing nomenclature issues for GPCRs.", keywords = "MOLECULAR EVOLUTION, EXPRESSION ANALYSIS, HORMONE-RECEPTORS, GENE DUPLICATION, XENOPUS-LAEVIS, ZEBRAFISH, CLONING, IDENTIFICATION, FAMILY, REPERTOIRE, 1184 Genetics, developmental biology, physiology, 1182 Biochemistry, cell and molecular biology", author = "Maiju Rinne and Zia-Ur-Rehman Tanoli and Asifullah Khan and Henri Xhaard", year = "2019", month = may, day = "7", doi = "10.1038/s41598-018-33120-8", language = "English", volume = "9", journal = "Scientific Reports", issn = "2045-2322", publisher = "Nature Research ", } . Scientific Reports.

(2018). Interactive visual analysis of drug-target interaction networks using Drug Target Profiler, with applications to precision medicine and drug repurposing . Briefings in bioinformatics.

Drug Target Commons 2.0 @article{d17e98aaa8704c0ca269e37374aa05c6, title = "Drug Target Commons 2.0: a community platform for systematic analysis of drug target interaction profiles", abstract = "Drug Target Commons (DTC) is a web platform (database with user interface) for community-driven bioactivity data integration and standardization for comprehensive mapping, reuse and analysis of compound-target interaction profiles. End users can search, upload, edit, annotate and export expert-curated bioactivity data for further analysis, using an application programmable interface, database dump or tab-delimited text download options. To guide chemical biology and drug-repurposing applications, DTC version 2.0 includes updated clinical development information for the compounds and target gene-disease associations, as well as cancer-type indications for mutant protein targets, which are critical for precision oncology developments.", keywords = "ACUTE MYELOID-LEUKEMIA, METASTATIC MELANOMA, THERAPEUTIC TARGET, IMATINIB MESYLATE, DISCOVERY, KIT, INFORMATION, SORAFENIB, RESOURCE, MUTATION, 3111 Biomedicine", author = "ZiaurRehman Tanoli and Zaid Alam and Markus V{\"a}h{\"a}-Koskela and Balaguru Ravikumar and Alina Malyutina and Alok Jaiswal and Jing Tang and Krister Wennerberg and Tero Aittokallio", year = "2018", month = sep, day = "13", doi = "10.1093/database/bay083", language = "English", journal = "Database-The journal of biological databases and curation", issn = "1758-0463", publisher = "Oxford University Press", } . Database-The journal of biological databases and curation.

Zia ur Rehman, Adnan Idris, Asifullah Khan(2018). Multi-Dimensional Scaling based grouping of known complexes and intelligent protein complex detection . Computational Biology and Chemistry. 74. p. 149--156. Elsevier {BV}

Drug Target Commons @article{4fa33441781c4404ba27d953865d3989, title = "Drug Target Commons: A Community Effort to Build a Consensus Knowledge Base for Drug-Target Interactions", abstract = "Knowledge of the full target space of bioactive substances, approved and investigational drugs as well as chemical probes, provides important insights into therapeutic potential and possible adverse effects. The existing compound-target bioactivity data resources are often incomparable due to non-standardized and heterogeneous assay types and variability in endpoint measurements. To extract higher value from the existing and future compound target-profiling data, we implemented an open-data web platform, named Drug Target Commons (DTC), which features tools for crowd-sourced compound-target bioactivity data annotation, standardization, curation, and intra-resource integration. We demonstrate the unique value of DTC with several examples related to both drug discovery and drug repurposing applications and invite researchers to join this community effort to increase the reuse and extension of compound bioactivity data.", keywords = "DISCOVERY, CANCER, INFORMATION, DATABASE, BINDING, PROBES, TOOL, 3111 Biomedicine, 1182 Biochemistry, cell and molecular biology", author = "Jing Tang and Zia-ur-Rehman Tanoli and Balaguru Ravikumar and Zaid Alam and Anni Rebane and Markus V{\"a}h{\"a}-Koskela and Gopal Peddinti and {van Adrichem}, {Arjan J.} and Janica Wakkinen and Alok Jaiswal and Ella Karjalainen and Prson Gautam and Liye He and Elina Parri and Suleiman Khan and Abhishekh Gupta and Mehreen Ali and Laxman Yetukuri and Anna-Lena Gustavsson and Brinton Seashore-Ludlow and Anne Hersey and Leach, {Andrew R.} and Overington, {John P.} and Gretchen Repasky and Krister Wennerberg and Tero Aittokallio", year = "2018", month = feb, day = "15", doi = "10.1016/j.chembiol.2017.11.009", language = "English", volume = "25", pages = "224--+", journal = "Cell chemical biology", issn = "2451-9448", publisher = "Cell Press", number = "2", } . Cell chemical biology.

Jing Tang, Zia-ur-Rehman Tanoli, Balaguru Ravikumar, Zaid Alam, Anni Rebane, Markus V&#228;h&#228;-Koskela, Gopal Peddinti, Arjan J. van Adrichem, Janica Wakkinen, Alok Jaiswal, et al.(2018). Drug Target Commons: A Community Effort to Build a Consensus Knowledge Base for Drug-Target Interactions . Cell Chemical Biology. 25. (2). p. 224--229.e2. Elsevier {BV}

(2018). Drug Target Commons 2.0: a community platform for systematic analysis of drug–target interaction profiles . Database.

Adnan Idris, Aksam Iftikhar, Zia ur Rehman(2017). Intelligent churn prediction for telecom using GP-AdaBoost learning and PSO undersampling . Cluster Computing. Springer Nature

Chen He, Luana Micallef, Zia-ur-Rehman Tanoli, Samuel Kaski, Tero Aittokallio, Giulio Jacucci(2017). MediSyn: uncertainty-aware visualization of multiple biomedical datasets to support drug treatment selection . BMC Bioinformatics. 18. (S10). Springer Nature

Chen He, Luana Micallef, Ziaurrehman Tanoli, Samuel Kaski, Tero Aittokallio, Giulio Jacucci(2017). MediSyn . BMC Bioinformatics. 18. BioMed Central

Zia-ur-Rehman, Asifullah Khan(2012). Identifying GPCRs and their Types with Chou's Pseudo Amino Acid Composition: An Approach from Multi-scale Energy Representation and Position Specific Scoring Matrix . Protein & Peptide Letters. 19. (8). p. 890--903. Bentham Science Publishers Ltd.

Zia-ur-Rehman, Asifullah Khan(2011). Prediction of GPCRs with Pseudo Amino Acid Composition: Employing Composite Features and Grey Incidence Degree Based Classification . Protein & Peptide Letters. 18. (9). p. 872--878. Bentham Science Publishers Ltd.

Zia ur-Rehman, Asifullah Khan(2011). G-protein-coupled receptor prediction using pseudo-amino-acid composition and multiscale energy representation of different physiochemical properties . Analytical Biochemistry. 412. (2). p. 173--182. Elsevier {BV}

Trinh Trung Duong Nguyen, Ziaurrehman Tanoli, Saad Hassan, Umut &#214;zcan, Jimmy Caroli, Albert Kooistra, David Gloriam, Alexander Hauser (2024). PGxDB: An interactive web-platform for pharmacogenomics research .

Cichonska A, Ravikumar B, Allaway RJ, Park S, Wan F, Isayev O, Li S, Mason M, Lamb A, Tanoli Z, et al.(2020). Crowdsourced mapping extends the target space of kinase inhibitors .

Zia-ur Rehman, Muhammad Tayyeb Mirza, Asifullah Khan, Henri Xhaard(2013). Predicting G-Protein-Coupled Receptors Families Using Different Physiochemical Properties and Pseudo Amino Acid Composition . G Protein Coupled Receptors - Modeling, Activation, Interactions and Virtual Screening. p. 61--79. Elsevier

Ezequiel Anokian, Judith Bernett, Adrian Freeman, Markus List, Luc&#237;a Prieto Santamar&#237;a, Ziaurrehman Tanoli, Sarah Bonnin (2024). Machine Learning and Artificial Intelligence in drug repurposing – challenges and perspectives .

Aron Schulman, Juho Rousu, Tero Aittokallio, Ziaurrehman Tanoli (2024). Attention-based approach to predict drug-target interactions across seven target superfamilies .

Ezequiel Anokian, Judith Bernett, Adrian Freeman, Markus List, Luc&#237;a Prieto Santamar&#237;a, Ziaurrehman Tanoli, Sarah Bonnin (2024). Machine Learning and Artificial Intelligence in drug repurposing – challenges and perspectives .

Jehad Aldahdooh, Ziaurrehman Tanoli, Jing Tang (2023). Mining drug-target interactions from biomedical literature using chemical and gene descriptions-based ensemble transformer model .

Yinyin Wang, Jehad Aldahdooh, Yingying Hu, Hongbin Yang, Markus V&#228;h&#228;-Koskela, Jing Tang, Ziaurrehman Tanoli(2022). DrugRepo: A novel approach to repurpose a huge collection of compounds based on chemical and genomic features . Cold Spring Harbor Laboratory

Jehad Aldahdooh and Markus Vähä-Koskela and Jing Tang and Ziaurrehman Tanoli(2021). Using BERT to identify drug-target interactions from whole PubMed . []. Cold Spring Harbor Laboratory

Shuyu Zheng, Wenyu Wang, Jehad Aldahdooh, Alina Malyutina, Tolou Shadbahr, Ziaurrehman Tanoli, Alberto Pessia, Jing Tang(2021). SynergyFinder Plus: Toward Better Interpretation and Annotation of Drug Combination Screening Datasets . Cold Spring Harbor Laboratory

Anna Cichonska, Balaguru Ravikumar, Robert J Allaway, Sungjoon Park, Fangping Wan, Olexandr Isayev, Shuya Li, Michael Mason, Andrew Lamb, Ziaurrehman Tanoli, et al. (2020). Crowdsourced mapping extends the target space of kinase inhibitors .

Ziaurrehman Tanoli (2023). RepurposeDrugs: an interactive web-based database and machine learning predictor for repurposing mono- and combination therapies .

Ziaurrehman Tanoli (2023). RepurposeDrugs: an interactive web-based database and machine learning predictor for repurposing mono- and combination therapies .

Ziaurrehman Tanoli (2023). RepurposeDrugs: an interactive web-based database and machine learning predictor for repurposing mono- and combination therapies . RExPO22 Conference.

R-BERT-CNN @inproceedings{7e4465a7126d4c74869b98d5617348d8, title = "R-BERT-CNN: Drug-target interactions extraction from biomedical literature", abstract = "In this research, we present our work participation for the DrugProt task of BioCreative VII challenge. Drug-target interactions (DTIs) are critical for drug discovery and repurposing, which are often manually extracted from the experimental articles. There are >32M biomedical articles on PubMed and manually extracting DTIs from such a huge knowledge base is challenging. To solve this issue, we provide a solution for Track 1, which aims to extract 10 types of interactions between drug and protein entities. We applied an Ensemble Classifier model that combines BioMed-RoBERTa, a state of art language model, with Convolutional Neural Networks (CNN) to extract these relations. Despite the class imbalances in the BioCreative VII DrugProt test corpus, our model achieves a good performance compared to the average of other submissions in the challenge, with the micro F1 score of 55.67% (and 63% on BioCreative VI ChemProt test corpus). The results show the potential of deep learning in extracting various types of DTIs.", keywords = "3111 Biomedicine, Drug-target interaction, Drug discovery, relation extraction, text mining", author = "Jehad Aldahdooh and Ziaurrehman Tanoli and Jing Tang", year = "2021", month = nov, day = "2", language = "English", pages = "102--106", booktitle = "Proceedings of the BioCreative VII Challenge Evaluation Workshop", note = "BioCreative VII challenge and workshop ; Conference date: 08-11-2021 Through 10-11-2021", } . Proceedings of the BioCreative VII Challenge Evaluation Workshop.

Ziaurrehman Tanoli, Muhammad Tayyeb Mirza, Asifullah Khan, Henri Xhaard(2013). Predicting G-Protein-Coupled Receptors Families Using Different Physiochemical Properties and Pseudo Amino Acid Composition . G Protein coupled receptors. p. 61--79. Elsevier Academic Press