Scientist​/Sr. Scientist, Computational Chemistry

Scientist/Sr. Scientist, 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.

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 exceptional computational chemist to support our computational chemistry, cheminformatics, and molecular design efforts. This role will help drive small-molecule drug discovery programs by providing practical modeling support, applying modern computational workflows, and using the cheminformatics and AI-enabled tools that empower medicinal chemists and project teams.

The successful candidate will be 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 apply 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 strong 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 contributes 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 work with modern, AI-enabled computational methods while remaining directly involved in molecule design.

Computational Chemistry and Molecular Design

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.

Contribute to 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.

Cheminformatics and Data Infrastructure

Use and help improve chem and bioinformatics tools that support compound registration, structure-searching, SAR analysis, property visualization, compound triage, library design, and project decision-making.

Apply tools for chemical data handling, including similarity and substructure searching, R-group analysis, matched molecular pairs, reaction enumeration, compound clustering, property prediction, and visualization.

Work with internal or external engineering and data science teams to integrate chemical, biological, DMPK, structural, and assay data into usable project dashboards and design tools.

Follow best practices for chemical data quality, assay data curation, compound annotation, metadata standards, and reproducible computational workflows.

Use commercial and open-source computational tools, including platforms such as Schrödinger, MOE, CCDC tools, Chem Axon, KNIME, Pipeline Pilot, RDKit, Data Warrior, Spotfire, and related systems.

AI/ML and Digital Chemistry Tools

Apply user-friendly AI/ML-enabled molecular design tools,…