Head of Computational Chemistry San Francisco, CA

General Proximity is a seed‑stage startup developing the next generation of induced proximity medicines (IPMs). Our OmniTAC drug discovery engine furnishes molecules that co‑opt existing cellular machinery to overcome therapeutic challenges, which have remained unapproachable to other modalities for decades.

We are seeking a first‑rate computational chemist to help us pioneer this uncharted frontier of drug discovery.

A long‑standing challenge in drug discovery is the development of molecules capable of modulating difficult or "undruggable" targets. Disease‑causing proteins can be dysfunctional in many different ways, but our armamentarium for fixing them is quite limited. The most common mechanism of action for FDA‑approved drugs is inhibition, but there are many other possible perturbation types whose potential remains unrealized.

General Proximity is a seed‑stage drug discovery company developing a novel platform technology to solve this problem. We make bifunctional drugs that induce the modification of drug targets by existing cellular machinery (rather than through direct modulation by the drug, the classical approach).

Historically, the development of technologies that allow one to push new buttons in biology has been an incredibly fertile field for the discovery of new medicines, and our technology holds the same promise.

We are seeking an experienced Head of Computational Chemistry to build and lead our computational chemistry, cheminformatics, and molecular design capabilities. This role will drive small‑molecule drug discovery programs by providing strategic and practical modeling support, implementing modern computational workflows, and building the cheminformatics and AI‑enabled infrastructure needed to empower medicinal chemists and project teams.

The successful candidate will be both a scientific leader and a hands‑on drug designer: someone who can partner closely with medicinal chemists, structural biologists, biologists, and DMPK scientists to guide compound design from hit identification through lead optimization and candidate selection. They will also deploy practical tools that improve decision‑making, accelerate design‑make‑test‑analyze cycles, and make computational and AI‑driven methods accessible to bench chemists.

The ideal candidate is a computational drug hunter who combines deep technical expertise with practical medicinal chemistry judgment. This person should not be an isolated modeler, but a true project partner who sits with chemistry teams, understands the design problem, proposes molecules, helps interpret data, and builds tools that make the broader organization faster and smarter.

This role is ideal for someone who has worked in a pharma or biotech computational chemistry group and wants to build a modern, AI‑enabled computational platform from the ground up while remaining directly involved in molecule design.

This is an on‑site position with flexible working hours.

• Provide hands‑on computational chemistry support to small‑molecule discovery programs from target evaluation, hit identification, hit‑to‑lead, and lead optimization through candidate nomination.
• Apply structure‑based and ligand‑based design approaches to guide compound design, including docking, molecular dynamics, pharmacophore modeling, QSAR, scaffold hopping, virtual screening, FEP/free‑energy methods, and multi‑parameter optimization.
• Use structural biology data, including X‑ray structures, cryo‑EM structures, homology models, and Alpha Fold‑derived models, to generate actionable design hypotheses.
• Partner with the medicinal chemistry team to interpret SAR, optimize potency, selectivity, physicochemical properties, ADME/PK, develop ability, and synthetic feasibility.
• Lead computational design discussions with project teams and translate complex modeling results into clear, practical medicinal chemistry recommendations.
• Support portfolio prioritization by evaluating target tractability, ligandability, binding‑site quality, chemical matter, and develop ability risks.

• Build and maintain a…