Tamarind Bio

About Tamarind Bio

Tamarind Bio provides a no-code and API-based platform that offers access to a wide array of bioinformatics tools for life scientists. Founded in 2023 and headquartered in San Francisco, the company aims to accelerate drug discovery by simplifying the use of complex computational models for protein and molecule design and simulation. The platform eliminates the need for researchers to write code or manage high-performance computing infrastructure, allowing them to focus on scientific research rather than DevOps.

The company was co-founded by Deniz Kavi (CEO) and Sherry Liu (CTO). Their journey began during their undergraduate studies in Computer Science at Stanford University, where they both conducted computational biology research and experienced the inefficiencies of existing bioinformatics tools firsthand. Kavi was effectively the "human back end" for his lab, manually running models, which highlighted the need for a more streamlined solution. This shared experience motivated them to create a platform that makes advanced computational tools accessible to a broader range of scientists.

Tamarind Bio operates on a Software-as-a-Service (SaaS) model, offering tiered subscriptions including Free, Premium, and Enterprise plans. The free tier is aimed at academics and individual scientists, while the premium and enterprise tiers cater to biotech and pharmaceutical companies with large-scale computational needs, offering API access and the ability to host custom models. The company serves a diverse client base, from academic institutions like Stanford and Harvard to large pharmaceutical companies, including eight of the top 20, such as Bayer and Boehringer Ingelheim. The platform is built on Amazon Web Services (AWS), which enables it to provide the scalable and secure infrastructure necessary to gain the trust of large enterprise clients.

The core of Tamarind Bio's offering is a web interface and a scalable API that provides access to over 250 state-of-the-art computational tools and AI models. Key features include tools for protein structure prediction (e.g., AlphaFold), de novo protein design (e.g., RFdiffusion), and molecular docking (e.g., DiffDock). This allows researchers to perform tasks like designing improved protein variants, predicting antibody-antigen binding, and screening large compound libraries efficiently. The platform handles the GPU orchestration and parallelization required to run these experiments at a massive scale. By February 2026, the company announced it had raised a total of $13.6 million, including a $12 million Series A round led by Dimension Capital with participation from Y Combinator, to further develop its AI infrastructure.