Research​/Sr. Research Investigator, Computational Chemistry

Overview

A global biopharmaceutical company on a mission to Solve On, Incyte follows science to find solutions for patients with unmet medical needs. Through the discovery, development, and commercialization of proprietary therapeutics, Incyte has established a portfolio of first‑class medicines for patients and a strong pipeline of products in Hematology, Oncology and Inflammation and Autoimmunity. Headquartered in Wilmington, Delaware, Incyte has operations in North America, Europe, and Asia.

We are seeking a highly motivated Computational Chemist to join our drug discovery team, focusing on computer‑aided drug discovery (CADD). The successful candidate will apply advanced computational approaches—including molecular docking, free energy perturbation (FEP), molecular dynamics (MD), AI/ML methods, and both structure‑based and ligand‑based drug design—to drive small molecule discovery across multiple therapeutic areas. This role will work at the interface of computational and experimental sciences, partnering closely with medicinal chemists, structural biologists, and data scientists to accelerate target validation, hit identification, lead optimization, and candidate selection.

The candidate is expected to generate hypotheses, influence design decisions, and contribute to advancing programs toward clinical development.

• 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 methodologies—including molecular docking, virtual screening, pharmacophore modeling, molecular dynamics (MD), quantum mechanics (QM) methods, and 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, contributing actionable insights to medicinal chemistry and advancing design cycles.
• Develop and apply AI/ML models for molecular property prediction, compound prioritization, and de novo design.
• Collaborate cross‑functionally to interpret experimental data (biochemical, biophysical, structural) and refine hypotheses to support decision‑making.
• Build, maintain, and improve computational workflows and pipelines, emphasizing automation, scalability, and reproducibility.
• Evaluate and implement emerging computational technologies and methodologies to enhance discovery capabilities.
• Communicate scientific findings and recommendations through presentations, reports, and publications.
• Support and manage external collaborations (e.g., data exchange, progress tracking, and scientific alignment) to ensure effective partnership and project advancement.

• Ph.D. in Computational Chemistry, Chemistry, Biophysics, Chemical Biology, or related field.
• Postdoctoral experience is preferred, particularly in molecular simulation, CADD, or drug discovery applications.
• Demonstrated experience applying computational methods to small molecule drug discovery, with a track record of scientific publications and/or impactful program contributions.
• Strong foundation in computer‑aided drug discovery (CADD) methodologies, with demonstrated experience applying structure‑based (SBDD) and ligand‑based (LBDD) approaches to drive compound optimization.
• Deep expertise in molecular dynamics (MD) simulations, including system setup, execution, enhanced sampling techniques, custom force field development/parameterization, and advanced trajectory analysis.
• Hands‑on experience with core computational chemistry methods (molecular docking, virtual screening, FEP/TI), industry‑standard platforms (e.g., Schrödinger, CCG MOE), and programming/cheminformatics tools (Python, RDKit).
• Experience working in high‑performance computing (HPC) and/or cloud environments, with familiarity in workflow automation and reproducible pipeline development (e.g., KNIME, version‑controlled environments).
• Ability to integrate computational predictions with experimental…