AI-driven drug discovery platform with computational biology simulation
Deep Origin builds computational tools for drug discovery, combining physics-based molecular simulation (GROMACS, OpenMM) with machine learning (scikit-learn, RDKit) on managed cloud infrastructure (AWS, Kubernetes, Slurm). Heavy hiring in research (7 roles) and senior/VP leadership (9 roles combined) reflects a company scaling AI-driven workflows into biotech R&D pipelines, not just infrastructure—the pain-point list centers on reducing failure rates, accelerating timelines, and eliminating animal testing through better in-silico prediction.
Deep Origin develops computational platforms for drug discovery, targeting biotech researchers and pharmaceutical R&D teams. The product suite spans molecular modeling (lead optimization, hit discovery, target identification), AI integration into discovery workflows, and managed cloud compute infrastructure (bare-metal clusters via Slurm, AWS EKS). Founded in 2022 and headquartered in South San Francisco, the company operates 51–200 employees and is hiring aggressively across research, engineering, and product roles, with presence in the US, Armenia, and Portugal. Their stated focus is reducing drug development cost and failure rates through computational biology and AI.
Deep Origin uses AWS infrastructure (EKS, RDS, IAM), Kubernetes, Terraform, Slurm for job scheduling, and molecular simulation tools (GROMACS, OpenMM, RDKit, PyMOL). ML/data stack includes scikit-learn, NumPy, SciPy, Pandas, and Elasticsearch. Backend uses Python, C/C++, Node.js, TypeScript, and Julia.
Deep Origin is headquartered in South San Francisco, California. The company hires in the United States, Armenia, and Portugal.
Deep Origin's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.