Scientist/Sr. Scientist, Computational Chemistry

General Proximity; San Francisco (CA); In loco; USD 180.000 - 240.000; Tempo pieno

Descrizione del lavoro

General Proximity is seeking an exceptional computational chemist to drive small‑molecule drug discovery using modern workflows and AI-enabled tools. You will collaborate closely with medicinal chemistry, biology, and DMPK teams to design from hits through lead optimization and candidate selection.

You will apply docking, MD, QSAR, FEP, and multi-parameter optimization, while leveraging AlphaFold models and cheminformatics platforms to accelerate decision-making in an AI‑driven environment.

Competenze

• PhD in Computational Chemistry, Medicinal Chemistry, Chemical Physics, Biophysics, Cheminformatics, Physical Organic Chemistry, or a related discipline.
• Minimum of 3 years of relevant pharma/biotech experience.
• Track record of using computational chemistry to impact small-molecule drug discovery programs.
• Hands-on expertise in structure-based and ligand-based design, docking, molecular dynamics, virtual screening, QSAR, FEP/free-energy methods, pharmacophore modeling, and multi-parameter optimization.

Mansioni

• Provide hands-on computational chemistry support for small-molecule discovery programs from target evaluation through lead nomination.
• Apply structure-based and ligand-based design approaches to guide compound design (docking, MD, pharmacophore modeling, etc.).
• Use X-ray/cryo-EM/homology/AlphaFold models to generate actionable design hypotheses.
• Partner with medicinal chemistry to interpret SAR and optimize potency, selectivity, physicochemical properties, ADME/PK, and developability.
• Translate complex modeling results into clear, practical medicinal chemistry recommendations.
• Support portfolio prioritization by evaluating tractability, ligandability, and developability risks.
• Contribute to AI-enabled design tools and data infrastructure.

Conoscenze

High Agency

Enthusiastic

Intensity and Grit

Prosocial

Hands-on drug design

Project partnership

Formazione

PhD in Computational Chemistry or related field

Strumenti

RDKit

Python

Descrizione del lavoro

Scientist/Sr. Scientist, Computational Chemistry

tl;dr

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.

Our Story

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 [1] , 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[ 2 , 3 , 4 ], and our technology holds the same promise.

The Position

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-analyzecycles, 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.

What You'll Do

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, pharmacophoremodeling, 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 withthemedicinal chemistry team to interpret SAR,optimizepotency, 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.

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, ChemAxon, KNIME, Pipeline Pilot, RDKit, DataWarrior, Spotfire, and related systems.

AI/ML and Digital Chemistry Tools

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.

Collaboration and Continuous Improvement

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.

About You

High Agency. Initiative, independence, and self‑accountability are some of our most valued traits.

Enthusiastic. We love people who are excited about what they are doing and are generally attempting to build a high‑energy team.

Intensity and Grit. Early‑stage startups are hard. Drug discovery is doubly so. We are looking for candidates who have a demonstrated ability to stick with complex problems for the long haul, with a team that has your back along the way.

Prosocial. We are here to create life‑saving medicines for the patients who need it most. You should be, too.

Qualifications & Nice‑To‑Haves

• 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