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)