Head of Computational Chemistry

Computational Chemistry

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.
• 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 developability risks.

Cheminformatics and Data Infrastructure

• Build and maintain a scalable chem and bioinformatics infrastructure to support compound registration, structure-searching, SAR analysis, property visualization, compound triage, library design, and project decision-making.
• Implement 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.
• Evaluate, implement, and maintain commercial and open-source computational tools, including platforms such as Schrodinger, MOE, CCDC tools, ChemAxon, KNIME, Pipeline Pilot, RDKit, DataWarrior, Spotfire, and related systems.

AI/ML and Digital Chemistry Tools

• Lead the practical implementation of 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.
• Identify opportunities to incorporate AI tools into the DMTA cycle, including compound prioritization, library design, synthetic route ideation, molecular-property prediction, and design hypothesis generation.
• Promote AI literacy across chemistry and project teams by training scientists on appropriate use, limitations, and interpretation of predictive models.
• Build workflows that allow medicinal chemists to use modeling and AI tools without requiring deep computational expertise.

Leadership and Strategy

• Define the computational chemistry strategy for the company and align it with discovery portfolio needs.
• Serve as the internal subject-matter expert for computational chemistry, cheminformatics, AI-enabled design, and molecular modeling.
• Establish external collaborations with CROs, software vendors, academic groups, AI drug discovery companies, and computational chemistry consultants where appropriate.
• Represent computational chemistry in portfolio reviews, program strategy discussions, investor diligence, scientific advisory board meetings, and external collaborations.
• Maintain awareness of emerging computational, AI, and cheminformatics technologies and recommend adoption where scientifically and operationally justified.

Qualifications

• PhD in Computational Chemistry, Medicinal Chemistry, Chemical Physics, Biophysics, Cheminformatics, Physical Organic Chemistry, or a related discipline
• A minimum of 15 years of relevant experience in pharma, biotech, or a drug discovery-focused research environment
• Demonstrated track record of using computational chemistry to impact small-molecule drug discovery programs, ideally through lead optimization, candidate selection, IND-enabling studies, or clinical development
• Deep 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, executives, and non-specialist stakeholders
• Demonstrated ability to lead cross-functional teams, mentor scientists, and influence project strategy without relying solely on formal authority

About You

• High Agency
• Enthusiastic
• Intensity and Grit
• Prosocial

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. 📚
• Located in the MBC BioLabs at 135 Mississippi Street, an entrepreneurial hub full of the best scientists and operators the Bay Area has to offer. Our lab is a very short walk from the 22nd St Caltrain Station and a number of wonderful restaurants and cafés.