Scientist/Sr. Scientist, Computational Chemistry

General Proximity • San Francisco, MS

1d • Onsite

About The Position

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.

Requirements

• PhD in Computational Chemistry, Medicinal Chemistry, Chemical Physics, Biophysics, Cheminformatics, Physical Organic Chemistry, or a related discipline.
• A minimum of 3 years of relevant experience in pharma, biotech, or a drug discovery-focused research environment.
• Track record of using computational chemistry to impact small-molecule drug discovery programs, ideally through hit-to-lead or lead optimization.
• Hands-on expertise in structure-based drug design, ligand-based design, docking, molecular dynamics, virtual screening, QSAR, FEP/free-energy methods, pharmacophore modeling, and multi-parameter optimization.
• Strong working knowledge of medicinal chemistry principles, SAR interpretation, physicochemical property optimization, ADME/PK concepts, and developability considerations.
• Practical experience with cheminformatics platforms, chemical databases, chemical data curation, compound registration systems, and project-facing visualization tools.
• Experience with AI/ML applications in molecular design, including predictive modeling, generative chemistry, active learning, or AI-enabled compound prioritization.
• Strong programming or scripting ability, preferably Python, with experience using cheminformatics toolkits such as RDKit and modern data science workflows.
• Ability to communicate complex computational concepts clearly to medicinal chemists, biologists, and non-specialist stakeholders.
• Ability to collaborate within cross-functional teams and influence project decisions through strong scientific input.

Nice To Haves

• Experience working in a biotech or fast-moving discovery organization
• Experience implementing user-friendly modeling tools for medicinal chemists
• Familiarity with cloud-based or high-performance computing environments
• Experience with automated DMTA workflows, electronic lab notebooks, compound management systems, assay-data systems, and integrated discovery platforms
• Experience supporting discovery across multiple modalities, such as covalent inhibitors, bifunctional molecules, and molecular glues
• Familiarity with synthetic accessibility prediction, retrosynthesis tools, reaction enumeration, and library design

Responsibilities

• 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 AlphaFold-derived models, to generate actionable design hypotheses.
• Partner with the medicinal chemistry team to interpret SAR, optimize potency, selectivity, physicochemical properties, ADME/PK, developability, 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 developability risks.
• 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, ChemAxon, KNIME, Pipeline Pilot, RDKit, DataWarrior, Spotfire, and related systems.
• Apply user-friendly AI/ML-enabled molecular design tools, including generative chemistry, predictive ADME/Tox models, property prediction, active learning, virtual screening, and decision-support systems.
• Help incorporate AI tools into the DMTA cycle, including compound prioritization, library design, synthetic route ideation, molecular-property prediction, and design hypothesis generation.
• Support AI literacy across chemistry and project teams by helping colleagues understand appropriate use, limitations, and interpretation of predictive models.
• Help develop workflows that allow medicinal chemists to use modeling and AI tools without requiring deep computational expertise.
• Contribute to the computational chemistry approach for projects and align it with discovery program needs.
• Serve as a subject-matter resource for computational chemistry, cheminformatics, AI-enabled design, and molecular modeling.
• Support collaborations with CROs, software vendors, academic groups, and computational chemistry consultants where appropriate.
• Represent computational chemistry in project team meetings and program discussions.
• Maintain awareness of emerging computational, AI, and cheminformatics technologies and recommend adoption where scientifically and operationally justified.

Benefits

• Strong equity incentives
• Top tier medical, dental, and vision coverage + One Medical membership
• 401(k) retirement plans
• Education and health/fitness incentive programs
• Meditation retreats—do a ten-day Vipassana retreat without counting towards vacation days
• Reading budget! We will buy you books.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree