Job Description

Assigned Job Details

Job Title : Senior Scientist Cheminformatics & Computational Chemistry
Location: South San Francisco, CA ,Hybrid
Rate : Best competitive rate

We are seeking a highly motivated Senior Scientist in Cheminformatics & Computational Chemistry to design, evaluate, and implement end-to-end computational workflows for small molecule drug discovery within the TuneLab platform.

This role sits at the intersection of cheminformatics, physics-based modeling, and machine learning , and requires strong hands-on expertise across the drug discovery pipeline-from virtual screening to lead optimization. You will collaborate closely with cross-functional teams to develop scalable, production-grade solutions that drive scientific innovation.

This is a high-impact contract role , requiring independent decision-making, including tool evaluation and build-vs-buy assessments.

• Design and implement computational workflows across the drug discovery lifecycle:
  • Virtual screening (ligand-based and structure-based)
  • Hit identification and expansion
  • Hit-to-lead selection
  • Lead optimization

• Apply and integrate advanced computational chemistry and cheminformatics methods, including:

Ultra-Large Library Search

• Substructure search
• Fingerprint and embedding-based similarity search
• Shape and pharmacophore-based screening

Molecular Enumeration

• Reaction-based enumeration
• Fragment-based design and expansion

Ligand-Based Modeling

• QSAR modeling
• Similarity analysis and clustering
• Active learning and iterative design

Structure-Based Modeling

• Docking, rescoring, and pose prediction
• Structure-aware search techniques

Physics-Based Methods

• Molecular Dynamics (MD) simulations
• Free Energy Perturbation (FEP) and related approaches

• Partner with:
  • Machine Learning teams to integrate predictive and generative models
  • Software Engineering teams to productionize workflows and ensure scalability
  • Scientific stakeholders to align solutions with drug discovery objectives

• MS or PhD in Cheminformatics, Computational Chemistry, Medicinal Chemistry, or a related field
• 5 10 years of relevant industry experience
• Strong understanding of small molecule drug discovery workflows
• Demonstrated expertise in:
  • Substructure and similarity search methods
  • Shape and pharmacophore modeling
  • Docking and structure-based design
  • QSAR and ligand-based modeling
  • Active learning methodologies
  • Physics-based simulations (MD, FEP)
• Hands-on experience with tools such as:
  • RDKit, OpenEye, or equivalent
  • Docking platforms (e.g., Glide, AutoDock, GOLD)
• Strong programming skills in Python

• 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 machine learning models into production environments
• Knowledge of workflow orchestration tools (e.g., Airflow, Nextflow)

Best,

Bhavani
Recruiter | IT & Digital Marketing

bhavani@nukasanigroup.com
P: 630.454.0067
540 W Galena Blvd, Suite 200
Aurora, IL 60506

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