Sr. Scientist / Principal Scientist, Computational Chemistry

About the Role:

We are seeking a high-impact Computational Chemist to join our integrated discovery team. In this role, you will be the computational engine of our programs, combining physics-based modeling, machine learning and structural biology to generate the quantitative predictions and develop necessary workflows to drive small molecule drug discovery. You will serve as a core strategic partner to medicinal chemists and biologists, focusing on compound design and tool development to impact discovery pipeline and address unmet computational needs.

Key Responsibilities:

Integrate physics-based simulations with ML predictions to achieve the quantitative accuracy required to prioritize compounds for synthesis

Collaborate with a team of interdisciplinary scientists to develop actionable hypotheses and design computational experiments

Design and prioritize chemical matter specifically aimed at hitting key program milestones, such as establishing in vivo POC, achieving selectivity windows, or optimizing ADMET profiles for candidate selection

Develop, validate and deploy computational workflows to optimize the  "Design-Make-Test-Analyze" cycles and address gaps

Required Qualifications:

Ph.D. in Computational Chemistry, Biophysics, or a related field

1-3+ years of experience in a biotech or pharma setting performing computational support for small molecule drug discovery

Advanced knowledge of physics-based and ML computational chemistry packages including knowing when and how to deploy various tools for maximum project impact

Exceptional ability to communicate the "why" behind a design to a diverse scientific audience

Design experience working in concert with medicinal chemistry teams to design synthesizable compounds that efficiently work towards defined goals of activity, affinity, selectivity, properties, etc

A proven track record for innovation in structure-based small molecule drug discovery including developing and validating new workflows and techniques or expansions of existing ones

Preferred Qualifications:

Experience working with structural biology teams to extract the most information possible from cryo-EM and x-ray crystallography experiments and using this to accelerate programs using structure-based drug discovery techniques

Proven experience using ML to scale physics-based insights, specifically in the context of large-scale virtual screening or FEP-guided lead optimization

A proven track record for innovation in structure-based small molecule drug discovery including developing and validating new workflows and techniques or expansions of existing ones

Skills & Competencies:

Expert level use of structure-based small molecule drug discovery software tools including protein preparation, docking, FEP, QM, conformer selection. (Schrodinger suite, OpenEye, MOE, etc)

Ability to work directly in a Linux-based environment

Familiarity with cloud computing infrastructure (AWS, GCS) is a plus

Python scripting and prototyping experience including knowledge of key packages (RDKit, scikit-learn, numpy, pandas, pytorch, etc)