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About the Role

General Proximity is seeking a first-rate computational chemist to pioneer the uncharted frontier of induced proximity medicines (IPMs). This role will support computational chemistry, cheminformatics, and molecular design efforts, driving small-molecule drug discovery programs.

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.

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.
• 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.
• Scientific contributions through publications, presentations, patents, open-source contributions, or demonstrated project impact.

Skills

• Computational Chemistry
• Cheminformatics
• Molecular Design
• Structure-based drug design
• Ligand-based design
• Docking
• Molecular dynamics
• Virtual screening
• QSAR
• FEP/free-energy methods
• Pharmacophore modeling
• Multi-parameter optimization
• Medicinal chemistry principles
• SAR interpretation
• Physicochemical property optimization
• ADME/PK concepts
• Developability considerations
• AI/ML applications in molecular design
• Predictive modeling
• Generative chemistry
• Active learning
• AI-enabled compound prioritization
• Python programming
• RDKit
• Data science workflows
• Communication
• Collaboration
• Cloud computing
• High-performance computing
• Automated DMTA workflows
• Electronic lab notebooks
• Compound management systems
• Assay-data systems
• Integrated discovery platforms
• Covalent inhibitors
• Bifunctional molecules
• Molecular glues
• Synthetic accessibility prediction
• Retrosynthesis tools
• Reaction enumeration
• Library design

Location

• MBC BioLabs at 135 Mississippi Street

Work Type

• Full-time

Experience Level

• Minimum of 3 years of relevant experience
• PhD

Education Level

• PhD in Computational Chemistry, Medicinal Chemistry, Chemical Physics, Biophysics, Cheminformatics, Physical Organic Chemistry, or a related discipline.

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

About the Company

• 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 believe time is our most valuable commodity, so we strive to create a culture that reflects this.
• We won’t drag you through unnecessary meetings or email you at 7 PM on a Saturday.
• When we're at work, we're there to get things done, and when we're off, we're really off.
• We like well-written documents over PowerPoints, OKRs over vague mission statements, and weekly one-on-ones over yearly reviews.
• We have a "writing-first" culture. We believe that clarity of writing reflects clarity of thinking and that the benefits of well-written documentation in a scientific environment are innumerable: democratization of ideation and decision-making, increased reproducibility, quicker scaling and onboarding, and better company-wide alignment, to name a few.
• As an early-stage startup, we value scientists with an independent, can-do attitude.
• The more hats you can wear, the better.
• Our job as employers is to put you in positive feedback loops so you can grow in the direction of your choosing.

Equal Opportunity

• Applications from candidates of diverse backgrounds, women, and members of underrepresented minority groups are particularly welcomed.