Principal Computational Chemist (#2618)

Precision Life Sciences


Key Responsibilities:

–  Lead application of internal and external Machine Learning and Computational Chemistry tools on small molecule drug discovery projects

–  Drive Drug discovery programs forward by quickly developing scalable tools to solve project requirements

–  Develop large-scale virtual screening approaches with both ligand and structure-based methods

–  Work collaboratively with experts from other fields (e.g., computational biology, molecular biology, medicinal chemistry, machine learning) to drive pipeline integration into our in-silico discovery platform

Basic Qualifications:

–  MS/PhD degree in Chemistry, Biochemistry, Computational Chemistry, Chemical Engineering, or other related subjects

–  Minimum 3 years of post-graduate experience in small molecule drug discovery

–  Proven track record of leading successful in-silico discovery projects; strong background in rational drug design and cheminformatics

–  Extensive experience in large-scale virtual screening and hit identification using cheminformatics, structure and ligand-based methods

–  Good coding skills, including minimum 2 years of experience writing Python programs

–  Strong familiarity with Open-Source cheminformatics tools such as RDKit and Openbabel, in the domain of large-scale chemical similarity searches, clustering, and library design

–  Experience of applying large-scale virtual screening using shape/pharmacophore/EPS-matching tools, e.g. Panther, LIRA, and SenSaaS

–  A solid understanding of deep learning-based frameworks in structure-based domains ( e.g. DiffDock, E3Bind, UniMol, GNINA, KDEEP, EquiBind, dMaSIF)

–  Familiarity with good software development developing python packages, package management (pip, mamba, conda), CI/CD and other adjacent topics required for designing high quality codebases

–  Contribution, development, and maintenance of open-source packages used by the cheminformatics community

–  Excellent decision-making skills related to chemical space selection prior to launching a virtual screening campaign

–  Experience in leveraging experimental data for building and/or refining complex in-silico screening pipelines (e.g., SPR, TSA and cell-based assays readouts, including phenotypic screening)

–  Quick and scrappy learner who adapts well to a fast-moving environment and gets things done, combining creativity, problem-solving skills, and a can-do attitude to overcome obstacles

–  Understanding of business problems and how to build end-to-end analytics use cases tied to business value

–  Ability to provide thought leadership by researching best practices, conducting experiments, and collaborating with industry leaders

–  Excellent written and verbal communication skills along with a strong desire to work in cross-functional teams

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