Data Science & AI Innovation Postdoctoral Fellow Foundational & Multimodal Proteochemometrics models
Core
Training and optimizing foundational multimodal proteochemometrics models to predict small molecule protein interactions for drug discovery, hit finding, and safety assessments.
Role type
Postdoctoral Research Fellow (AI/ML in Drug Discovery)
Builds
Predictive models for drug-target interactions and off-target safety assessments
Domain
Biopharma / Cheminformatics / Machine Learning
Deliverable
production ML models
Required skills
Python scientific and deep learning stacks, Linux high performance computing and cloud environments, cheminformatic workflows, training foundational models, processing huge datasets
Preferred skills
ligand protein docking, ligand protein co-folding, drug-target interaction models, synthons and transformations
Technologies
Python, Linux, cloud environments
Responsibilities
Train and optimize foundational structure affinity models on high volume experimental data; Fine-tune models on high-quality low volume affinity data; Evaluate model performance with and without fine-tuning; Apply models prospectively in small molecule hit-finding projects; Evaluate model performance to predict off-targets for pharmacology safety assessments
Seniority
Postdoctoral Fellow (early-career scientist immediately following PhD)
