Deep Origin is seeking a Scientist or Senior Scientist with strong expertise in structure-based drug design, including docking, molecular dynamics (MD), and free energy perturbation (FEP), to support a transformative ARPA-H initiative. You’ll lead the design of robust simulation workflows and analyze protein-ligand structures across a large target panel to support predictive modeling for therapeutic discovery.

REQUIREMENTS

• Ph.D. in computational chemistry, structural biology, biophysics, or related field;
• 2+ years of postdoctoral or industry experience in structure-based modeling;
• Hands-on expertise with FEP (RBFE/ABFE), including best practices around setup, sampling, and analysis;
• Proficiency with one or more simulation platforms (e.g., Schrödinger FEP+, OpenMM, GROMACS, AMBER, NAMD);
• Strong understanding of protein-ligand binding, structure selection, and conformational variability;
• Programming experience in Python, and familiarity with tools like MDAnalysis, PyMOL APIs, or MDTraj;
• Fluent English for collaboration with an international team;
• Ability to work on US time zones when needed.

• Analyze tens to hundreds of protein targets relevant to ADMET and off-targets, focusing on conformations, binding site flexibility, and ligand-bound states to guide structure preparation and ensemble design;
• Run and refine docking, MD, and FEP (RBFE and ABFE) simulations using state-of-the-art tools;
• Apply methods such as restraints, alchemical transformations, and sampling strategies to ensure robust and reproducible FEP workflows;
• Curate, benchmark, and select optimal protein-ligand structures (e.g., from PDB) for predictive modeling;
• Evaluate multiple structural representations (e.g., different PDB IDs) to determine the best input per target;
• Collaborate with cheminformatics, ML, and experimental teams to integrate structure-based insights across discovery pipelines;
• Communicate progress, technical findings, and challenges across internal and external teams.