Research/Sr. Research Investigator, Computational Chemistry
Incyte
July 01, 2026
Remote friendly (Wilmington, DE)
United States
Clinical Research and Development
Key Responsibilities:
- Lead and execute computational modeling initiatives for multiple internal drug discovery programs, guiding decision-making from target validation through hit identification and lead optimization
- Apply and integrate computational chemistry methods (molecular docking, virtual screening, pharmacophore modeling, molecular dynamics (MD), quantum mechanics (QM), free energy calculations FEP/TI) to generate mechanistic insights and guide SAR
- Drive compound design by integrating structure-based (SBDD) and ligand-based (LBDD) approaches
- Develop and apply AI/ML models for molecular property prediction, compound prioritization, and de novo design
- Collaborate to interpret experimental data (biochemical, biophysical, structural) and refine hypotheses
- Build, maintain, and improve computational workflows and pipelines (automation, scalability, reproducibility)
- Evaluate and implement emerging computational technologies and methodologies
- Communicate findings via presentations, reports, and publications
- Support and manage external collaborations (data exchange, progress tracking, scientific alignment)
Qualifications:
- Ph.D. in Computational Chemistry, Chemistry, Biophysics, Chemical Biology, or related field
- Postdoctoral experience preferred (molecular simulation, CADD, drug discovery)
- Demonstrated experience applying computational methods to small molecule drug discovery with publications and/or impactful contributions
- Strong CADD foundation with experience in SBDD and LBDD for compound optimization
- Deep expertise in MD simulations (setup, execution, enhanced sampling, force field development/parameterization, trajectory analysis)
- Hands-on experience with docking, virtual screening, FEP/TI; platforms (e.g., Schrödinger, CCG MOE); Python/RDKit
- Experience with HPC and/or cloud; workflow automation and reproducible pipelines (e.g., KNIME, version control)
- Ability to integrate computational predictions with experimental data (crystallography, cryo-EM, HTS)
- AI/ML in drug discovery strongly preferred (QSAR, predictive modeling, generative models)
- ADME/PK and physicochemical property prediction strongly preferred
Key Competencies:
- Scientific rigor, initiative/curiosity/innovation, critical thinking
- Communicate and collaborate across multidisciplinary teams
- Translate computational insights into design decisions
- Manage multiple priorities in a fast-paced environment
- Lead and execute computational modeling initiatives for multiple internal drug discovery programs, guiding decision-making from target validation through hit identification and lead optimization
- Apply and integrate computational chemistry methods (molecular docking, virtual screening, pharmacophore modeling, molecular dynamics (MD), quantum mechanics (QM), free energy calculations FEP/TI) to generate mechanistic insights and guide SAR
- Drive compound design by integrating structure-based (SBDD) and ligand-based (LBDD) approaches
- Develop and apply AI/ML models for molecular property prediction, compound prioritization, and de novo design
- Collaborate to interpret experimental data (biochemical, biophysical, structural) and refine hypotheses
- Build, maintain, and improve computational workflows and pipelines (automation, scalability, reproducibility)
- Evaluate and implement emerging computational technologies and methodologies
- Communicate findings via presentations, reports, and publications
- Support and manage external collaborations (data exchange, progress tracking, scientific alignment)
Qualifications:
- Ph.D. in Computational Chemistry, Chemistry, Biophysics, Chemical Biology, or related field
- Postdoctoral experience preferred (molecular simulation, CADD, drug discovery)
- Demonstrated experience applying computational methods to small molecule drug discovery with publications and/or impactful contributions
- Strong CADD foundation with experience in SBDD and LBDD for compound optimization
- Deep expertise in MD simulations (setup, execution, enhanced sampling, force field development/parameterization, trajectory analysis)
- Hands-on experience with docking, virtual screening, FEP/TI; platforms (e.g., Schrödinger, CCG MOE); Python/RDKit
- Experience with HPC and/or cloud; workflow automation and reproducible pipelines (e.g., KNIME, version control)
- Ability to integrate computational predictions with experimental data (crystallography, cryo-EM, HTS)
- AI/ML in drug discovery strongly preferred (QSAR, predictive modeling, generative models)
- ADME/PK and physicochemical property prediction strongly preferred
Key Competencies:
- Scientific rigor, initiative/curiosity/innovation, critical thinking
- Communicate and collaborate across multidisciplinary teams
- Translate computational insights into design decisions
- Manage multiple priorities in a fast-paced environment