Computational Theoretical Chemist I

Company Overview We are the only AI-native biotech, pioneering small and large molecule therapeutics discovery by integrating massive multimodal data, frontier AI models, and high-throughput lab automation into an infrastructure for AI-enabled drug discovery. We hire top 1% talent to join our interdisciplinary team of scientists, engineers, researchers, operators, innovators, drug developers, business professionals, and technologists. Join us to build the world’s first AI infrastructure for tech-enabled drug discovery and to deliver a pipeline of diverse drug modalities for all major disease areas. Computation is revolutionizing drug discovery. Advances in big chemical data, massive computing power, artificial intelligence, and molecular dynamics simulations are changing the way we develop new drugs. At 1910 , we put computation at the heart of drug discovery, blending expertise in computational chemistry, structural biology, pharmacology, data science, and software engineering to develop drugs for previously undruggable targets. Role Description Own computational theoretical 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 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 Validate a cellular hit in a clinically relevant animal model of disease Update provisional patents with the animal model data 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 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 Proficiency in analyzing molecular properties such as solvation free energy, dipole moments, vibrational frequencies, electrostatic potential, charge distribution, and more Deep knowledge of implicit and explicit solvent models, with extensive experience modeling solvent effects on molecular systems and chemical reactions in various environments 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) Ability to optimize computational simulation protocols for efficient resource usage Proven experience working with small organic molecules and large biomolecular systems (e.g., peptides, proteins, etc.) for property prediction, conformatio