Computational Theoretical Chemist III

About the role

Own computational chemistry programs across therapeutic modalities, disease targets, and indications
Ensure effective collaboration with the Biology and Medicinal Chemistry teams by providing key computational chemistry insights to aid in the Hit-to-Lead and Lead Optimization phases of drug discovery operations
Ensure effective collaboration with the ML Engineering and AI Research team by providing key computational chemistry insights to aid in the development of AI/ML models for drug discovery as well as the incorporation of those models into drug discovery operations
Teach key computational chemistry principles to your cross-disciplinary colleagues from Medicinal Chemistry, AI Research, Machine Learning Engineering, Cell Biology, and Pharmacology
Manage day-to-day operations of the Computational Theoretical Chemistry Team, mentor junior staff, and represent the team in senior leadership meetings
Partner to improve 1910's existing process for progressing from computational hit to experimental hit to lead to drug candidate
Co-author provisional patents and peer-reviewed research papers
Progress a virtual hit to a biochemical/cellular hit
Validate a cellular hit in a clinically relevant animal model of disease
Nominate a lead candidate for progression into IND-enabling studies
Attend and present research at conferences and events related to computational modeling in drug discovery

Qualifications

• Ph.D. in computational chemistry or related discipline
• 3+ years of relevant industry experience within drug discovery or biotechnology
• Played a key role in advancing a drug discovery program from early research phases to clinical development.
• In-depth knowledge and hands-on experience with quantum chemical (QC) methods, including semi-empirical and density functional theory (DFT) approaches, molecular dynamics (MD) simulations, including both standard MD and enhanced sampling techniques such as metadynamics, umbrella sampling, and replica exchange MD, free energy simulations such as FEP and TI, and QM/MM methodologies for small and large molecular systems
• Strong understanding of key concepts, including potential energy surfaces (PES), intermolecular and intramolecular forces/interactions, force fields, molecular properties, thermodynamic properties, solvation models (implicit/explicit), and conformational sampling
• Extensive experience in using and troubleshooting software tools for QC calculations (e.g., ORCA, xTB, CREST, etc.), MD simulations (e.g., GROMACS, OpenMM, etc.), Drug Design Development Packages (e.g., EG, Schrodinger, MOE, CRESSET)
• Experience working with HPC Clusters and cloud-based services like (e.g., Microsoft AZURE, AWS)
• Proven experience working with small organic molecules and large biomolecular systems (e.g., peptides, proteins, etc.) for property prediction, conformational analysis, and structure-activity relationships (SAR)
• Hands-on experience with Python and Bash scripting for automating workflows and data analysis
• Familiarity with cheminformatics toolkits such as RDKit for molecular property prediction and data management
• Proven experience working with machine learning (ML) techniques applied to molecular property prediction, virtual screening, and related tasks

Skills

• Clear and effective communication of complex scientific ideas through reports, presentations, and publications

Benefits

Competitive compensation package
Above market benefits
Generous vacation and parental leave
Great colleagues