Tamarind Bio Raises $13.6M to Run 200+ AI and Simulation Models in a No-Code Platform for Bench Scientists

Tamarind Bio has secured $13.6 million in Series A financing led by Dimension Capital with participation from Y Combinator to expand its platform for coordinating and deploying AI models in life-science research. The company reports adoption across roughly 100 biotech organizations, reportedly including eight of the top 20 pharmaceutical companies, like Bayer and Boehringer Ingelheim. 

See also: In our January overview of diffusion generative models in AI drug discovery, we covered how tools like RFdiffusion3 are being used for de novo protein design, and noted Tamarind as one of the no-code ‘diffusion portal’ platforms that makes such models easier to run without HPC setup.

Founded by Deniz Kavi and Sherry Liu, Tamarind grew out of workflow friction observed at Stanford School of Medicine. Kavi, then a software engineer, saw repeated back-and-forth between wet-lab scientists and computational colleagues whenever structure prediction or other modeling tasks were needed. 

Many AI tools required command-line use and cloud configuration, creating practical barriers for bench researchers. Drawing on Liu’s background in cloud computing at AWS, the pair set out to reduce that dependency and streamline how experimental teams access computational methods. Upon the initial launch, the company reported using 600 users in its first month.

Tamarind’s platform now hosts more than 200 models spanning antibodies, peptides, small molecules, enzymes, and radiopharmaceuticals, with applications in protein design, binding prediction, and molecular property analysis. Tamarind Bio is also a member of the OpenFold AI Research Consortium, a non-profit initiative developing open-source protein structure prediction and molecular modeling tools for drug discovery.

Tamarind provides a centralized cloud platform to run state-of-the-art AI and physics-based tools for molecular design without local setup or infrastructure management. It integrates widely used models such as AlphaFold, RFdiffusion, MPNN, and GROMACS into a managed interface with large-scale GPU orchestration.

The platform supports:

• Antibody and nanobody engineering: structure prediction, CDR redesign, binding pose prediction, and developability optimization.
• Protein and peptide design: large-scale interaction screening, cyclic peptide modeling, de novo binder generation, and molecular simulations.
• Enzyme and protein optimization: parallel testing of thousands of variants, stability and activity optimization, and free energy calculations.
• Small-molecule discovery: large-scale virtual screening, binding affinity prediction, molecular dynamics, and scaffold-based library generation.

Tamarind’s differentiation lies in model coordination and infrastructure abstraction. It handles compute provisioning, scaling, security, and data isolation, allowing biology teams to run complex AI workflows through a web interface or API while keeping full control over their data and any resulting outputs. Subscribing partners gain access to the full catalog of hosted models.

Topic: Biotech Ventures