Profile Details

Create Project

★★★★★

☆☆☆☆☆

USD 80 /hr

★★★★★

☆☆☆☆☆

Hire Simon C.

France

USD 80 /hr

Computational Biologist & AI Engineer in Drug Discovery with 4+ years of experience

Profile Summary

I develop machine learning solutions for computational drug discovery, specializing in protein design. **What I Can Help With:** - **Early-stage drug discovery**: You have a target protein but need to identify or optimize binding molecules (antibodies, small molecules, peptides) - **Hit-to-lead optimization**: You have experimental hits but need to predict which candidates are worth pursuing and how to improve them - **Internal tool development**: Your team needs custom ML pipelines for routine tasks (structure prediction, binding affinity screening, ADMET properties) - **Structural biology bottlenecks**: You have data but need help refining structures, analyzing conformations, or predicting binding sites - **Resistance prediction**: You're developing antivirals/antibiotics and need to anticipate resistance mutations before they emerge in patients - **Technology due diligence**: You're evaluating a biotech acquisition/investment and need independent assessment of their computational platform - **Competitive intelligence**: You want to analyze competitors' published structures or patents to inform your strategy - **Grant/publication support**: You need ML analysis for a paper or grant application in structural biology or drug discovery **Recent Projects & Experience:** At EPFL, I developed a conditional multimodal diffusion model for antibody discovery, achieving significant improvement in structural accuracy. At Roche, I created a knowledge graph neural network for protein-ligand prediction and screened 20k+ protein-ligand complexes. I've also built production-ready computational frameworks for HIV treatment optimization and viral genomics analysis. **Technical Expertise:** - Deep Learning: PyTorch, JAX, TensorFlow, PyTorch-Geometric, Lightning - Protein & Drug Design: AlphaFold, RFdiffusion, ProteinMPNN, RDKit, Rosetta, DiffDock - Computational Biology: CryoEM processing (cryoSPARC), Structure Analysis (PyMOL, Chimera, Coot), docking, virtual screening - Programming: Python, Julia, R, with strong software engineering practices **Academic Background:** - M.Sc. Systems Biology & Bioinformatics (ENS Paris, with honors) - M.Sc. Bioinformatics & Modeling (Sorbonne University) - B.Sc. Computer Science + B.Sc. Chemistry I bridge computational methods and biological insights, translating complex ML outputs into actionable results for research teams. Whether you need a quick analysis, pipeline development, or longer-term consulting, I deliver well-documented, reproducible solutions. Available for projects from short-term analyses (1-2 weeks) to extended collaborations (3-6 months).

Subject Matter Expertise

• ☆ Chemoinformatics
• ☆ Computational Chemistry
• ☆ Computational Biology
• ☆ Computer Science
• ☆ Software Engineering
• ☆ Software Architecture
• ☆ Data Analysis
• ☆ Computer Simulation/Computational Modeling
• ☆ Deep Learning
• ☆ Biomolecular Engineering
• ☆ Protein/Enzyme Engineering
• ☆ Antibody Engineering
• ☆ Drug Discovery
• ☆ High Throughput Screening (HTS)
• ☆ Drug Target Identification
• ☆ Hit-to-Lead & Lead Optimization
• ☆ Drug Design
• ☆ Structure-Activity Relationship (SAR) Studies
• ☆ Software Development
• ☆ Software QA & Testing

Services

• Writing
• Research
• Consulting
• Data & AI

Writing General Proofreading & Editing

Research Gap Analysis, Scientific and Technical Research, Systematic Literature Review

Consulting Scientific and Technical Consulting

Data & AI Predictive Modeling, Statistical Analysis, Image Processing, Image Analysis, Algorithm Design-Non ML, Algorithm Design-ML, Data Visualization, Big Data Analytics, Data Mining, Data Cleaning, Data Processing

Work Experience

AI Research Scientist

École Polytechnique Fédérale de Lausanne

January 2023 - April 2025

Computational Biologist Intern

Roche

July 2022 - October 2022

Master Thesis Student

ETH Zürich

January 2022 - July 2022

Research Intern

Wellcome Sanger Institute

July 2021 - September 2021

Research Intern

University of Glasgow

April 2020 - August 2020

Education

PhD in Bioinformatics (Doctoral School of Computational Biology)

École Polytechnique Fédérale de Lausanne

March 2023 - Present

M.Sc. "IMaLiS" in Systems Biology, Genomics & Bioinformatics (Département de Biologie)

École Normale Supérieure

September 2021 - June 2022

M.Sc. in Bioinformatics & Modelling

Sorbonne Université

September 2020 - August 2021

B.Sc. in Computer Science

Aix-Marseille Université

September 2017 - August 2020

Certifications

• Certification details not provided.

Publications

JOURNAL ARTICLE

Virus adaptation to heparan sulfate comes with capsid stability tradeoff @article{Tee_2024, title={Virus adaptation to heparan sulfate comes with capsid stability tradeoff}, volume={13}, ISSN={2050-084X}, url={http://dx.doi.org/10.7554/elife.98441}, DOI={10.7554/elife.98441}, journal={eLife}, publisher={eLife Sciences Publications, Ltd}, author={Tee, Han Kang and Crouzet, Simon and Muliyil, Arunima and Mathez, Gregory and Cagno, Valeria and Dal Peraro, Matteo and Antanasijevic, Aleksandar and Clément, Sophie and Tapparel, Caroline}, year={2024}, month=dec } . eLife.

Simon J. Crouzet, Anja Maria Lieberherr, Kenneth Atz, Tobias Nilsson, Lisa Sach-Peltason, Alex T. Müller, Matteo Dal Peraro, Jitao David Zhang (2024). G–PLIP: Knowledge graph neural network for structure-free protein–ligand bioactivity prediction . Computational and Structural Biotechnology Journal.

Deep Local Analysis evaluates protein docking conformations with locally oriented cubes <head> <META HTTP-EQUIV="Refresh" CONTENT="0;URL=/servlet/useragent"> </head> . Bioinformatics.

PREPRINT

Simon J. Crouzet, Anja Maria Lieberherr, Kenneth Atz, Tobias Nilsson, Lisa Sach-Peltason, Alex T. M&#252;ller, Matteo Dal Peraro, Jitao David Zhang (2023). G-PLIP: Knowledge graph neural network for structure-free protein-ligand bioactivity prediction .

Simon Crouzet, Yasser Mohseni Behbahani, Elodie Laine, Alessandra Carbone(2022). Deep Local Analysis evaluates protein docking conformations with Locally oriented Cubes . Cold Spring Harbor Laboratory

DISSERTATION THESIS

Crouzet, Simon(2022). Predicting HIV evolutionary pathways and designing optimal antiretroviral combinations with machine and deep learning approaches . Unpublished. Unpublished