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General Proximity is a seed-stage biotech startup developing induced proximity medicines (IPMs)—a novel drug discovery platform that uses bifunctional molecules to co-opt cellular machinery to modulate previously "undruggable" targets. The company's OmniTAC engine generates molecules that overcome therapeutic challenges intractable to traditional modalities.
We seek an experienced computational chemist to drive small-molecule drug discovery programs. This is a hands-on role combining computational expertise with practical medicinal chemistry judgment. You will partner closely with medicinal chemists, structural biologists, and DMPK scientists from hit identification through lead optimization and candidate selection.
Key responsibilities include:
- Provide computational chemistry support across the full discovery pipeline: target evaluation, hit identification, hit-to-lead, and lead optimization.
- Apply structure-based and ligand-based design methods: docking, molecular dynamics, pharmacophore modeling, QSAR, scaffold hopping, virtual screening, FEP/free-energy calculations, and multi-parameter optimization.
- Leverage structural biology data (X-ray, cryo-EM, homology models, AlphaFold predictions) to generate actionable design hypotheses.
- Interpret SAR data and optimize potency, selectivity, physicochemical properties, ADME/PK, developability, and synthetic feasibility.
- Build and deploy cheminformatics and AI-enabled tools that accelerate design-make-test-analyze cycles and make computational methods accessible to bench chemists.
- Sit with chemistry teams, understand design problems, propose molecules, help interpret data, and contribute tools that make the organization faster and smarter.
Ideal candidate: experienced computational drug hunter from pharma or biotech with strong technical expertise, practical medicinal chemistry judgment, and desire to work with modern AI-enabled computational methods while remaining directly involved in molecule design. Not an isolated modeler, but a true project partner.
