Senior Scientist: Cheminformatics & Computational Chemistry

Senior Scientist: Cheminformatics & Computational Chemistry needs 10 years’ experience Senior Scientist: Cheminformatics & Computational Chemistry requires: Hybrid • PhD or MS in Cheminformatics, Computational Chemistry, Medicinal Chemistry, or related field • Strong understanding of small molecule drug discovery workflows • Demonstrated expertise in: o Substructure and similarity search (fingerprints, graph-based, embedding-based) o Shape and pharmacophore searching o Reaction-based and fragment-based enumeration o Docking and structure-based design o QSAR and ligand-based modeling o Active learning and iterative design strategies o Physics-based simulations (e.g., MD, FEP) • Hands-on experience with tools such as: o RDKit, OpenEye, or equivalent o Docking platforms (e.g., Glide, AutoDock, GOLD) • Strong programming skills in Python Preferred Qualifications • Experience working with ultra-large chemical libraries (e.g., Enamine REAL, WuXi Galaxy) • Familiarity with generative chemistry approaches (SMILES-, graph-, or diffusion-based models) • Experience integrating ML models into production workflows • Experience with workflow orchestration tools (e.g., Airflow, Nextflow) Senior Scientist: Cheminformatics & Computational Chemistry duties: End-to-End Workflow Development • Design and implement workflows spanning: o Virtual screening (ligand-based and structure-based) o Hit identification and hit expansion o Hit-to-lead selection o Lead optimization Method Development & Application • Apply and integrate core computational chemistry and cheminformatics methods, including: o Ultra-large library search: Substructure search Fingerprint and embedding-based similarity search Shape and pharmacophore-based screening o Molecular enumeration: Reaction-based enumeration Fragment-based design and expansion o Ligand-based modeling: QSAR, similarity, clustering, active learning loops o Structure-based modeling: Docking, rescoring, pose prediction, structure-aware search o Physics-based methods: Molecular dynamics (MD) Free energy perturbation (FEP) and related approaches Cross-functional Collaboration • Partner with: o Machine Learning teams to integrate predictive and generative models o Software Engineering teams to productionize workflows and ensure scalability o Scientific stakeholders to align workflows with drug discovery needs