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