Senior Scientist: Cheminformatics & Computational Chemistry

Job Description

Job Description

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