Job description
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.
- 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.
Nice to have:
- Experience benchmarking across multiple PDB entries or conformational states;
- Prior work integrating structural modeling into machine learning pipelines;
- Familiarity with MM/GBSA, docking scoring functions, or clustering methods;
- Experience using Unix-based HPC environments, workload managers (e.g., SLURM, etc.), and optionally AWS;
- Comfort managing large-scale simulation data for modeling or analysis.
Responsibilities:
- 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.
Why Join Deep Origin
Deep Origin builds modern infrastructure for computational science at the interface of biology, chemistry, and AI. As part of our ARPA-H program, you’ll shape the future of structure-based modeling for therapeutics.
We offer:
- A remote-first team across the US, Europe, and Armenia;
- Competitive salary and equity packages;
- Flexible working hours;
- A mission-driven, scientifically rigorous culture that values autonomy and impact.
