Senior Computational Chemist - Oncology Targeted Discovery

Responsibilities: Generate testable design hypotheses by integrating structural insights, SAR and biophysical data to accelerate decision-making in medicinal chemistry Apply docking, pharmacophore modeling, and data-driven design to prioritize potent, selective and developable compounds Build, validate and deploy AI/ML models to guide high-throughput experimental design and hit-to-lead success Develop computational methods for new modalities, including antibody–drug conjugates and bifunctional, proximity-inducing agents Collaborate with medicinal chemistry, biology, biophysics, DMPK and safety sciences to align models with project goals Present clear, compelling results and contribute to publications and scientific reputation Deliver rapid project acceleration while creating reusable workflows and best practices across programs

Key requirements: Ph.D. in computational chemistry or related field Minimum 1 year of experience Deep knowledge in quantum mechanics, molecular dynamics, structure-based or ligand-based drug design Experience with AI/ML toolkits for modelling chemical and biological data (RDKit, ChemProp, DeepChem, PyTorch, scikit-learn) Excellent problem-solving skills with a track record of publications and presentations cross-functional collaboration strong communication problem-solving quantum mechanics molecular dynamics structure-based drug design