Mithrl

Mithrl co-founder Vivek Adarsh says the company grew out of losing his grandfather to acute kidney disease about 15 years ago, when no FDA-approved drug specifically addressed the condition. His motivating question became what technology could do to get medicines to patients faster. Adarsh, who previously worked on NVIDIA’s CUDA compiler and distributed systems at HP Labs, co-founded the San Francisco company with Shara Balakrishnan in 2023; Balakrishnan leads the technical side.

Mithrl raised a $20 million Series A led by Obvious Ventures, with Headline, AGI House, and several pharmaceutical executives also participating. The round follows a $4 million seed led by Bonfire Ventures in November 2024. The roughly 30-person company plans to use the new money first to hire people for deployments inside large organizations, then to expand across therapeutic areas including oncology, immune-related disease, diabetes, metabolic disease, and cardiovascular disease.

Adarsh argues that AI companies have focused on generating ever more drug hypotheses, but pharmaceutical researchers are now overwhelmed by ideas they cannot validate or prioritize. Generating candidates has become cheap; wet-lab experiments still consume substantial time and money, and clinical trials cost much more. Mithrl therefore asks which hypotheses will survive downstream experiments and ultimately work in patients. Its stated mission is to help R&D teams move from an idea to an IND—the FDA filing needed to begin human testing—50% faster.

Mithrl describes its product as a scientific decision engine, not a foundation model like AlphaFold: it does not predict protein structures or generate molecular sequences. Its biomedical world model organizes decades of peer-reviewed research, public datasets, and partner data into relationships among genes, pathways, diseases, and drugs. The system distinguishes details such as gene-expression increases from increased protein activity, tracks whether a relationship was observed or inferred, assigns confidence scores, and shows the sources behind each recommendation. An agentic harness selects models and supplies context.

Mithrl deploys inside each customer’s environment, uses the frontier models the customer already trusts, and lets scientists add proprietary data and build internal biochemical agents. Forward-deployed scientists help customers standardize their knowledge bases. Mithrl claims 16 times more primary evidence per answer, a scientific-correctness score of 0.96 versus 0.60 without its knowledge layer, and 45% fewer tokens in one benchmark, though these figures were not independently verified. It says the platform is used by top-10 pharmaceutical companies, clinical-stage biotechs, and genomics partners; LFS Biosciences is the named collaborator. Customer discoveries have contributed to more than half a dozen patent filings.

Adarsh says pharmaceutical companies ultimately pay for uniqueness, especially patents, rather than productivity alone, and sees the market becoming more educated about AI. Investor Rohan Ganesh places Mithrl’s advantage in the middle of the stack: proprietary data, world models, and customer-specific infrastructure rather than molecular design. Brian compares it with infrastructure companies such as Argonne AI and with general research agents, while warning that frontier labs could make curated world models a feature. He also questions how a forward-deployed model scales, since it combines software with scientists inside each customer; patent filings are promising but are not granted patents or drugs.