Easy to use computational biology tools for drug discovery
Tamarind Bio is a website and API which allows scientists to use computational biology tools at scale using a simple interface. On Tamarind, scientists can use ML models like AlphaFold to design and simulate molecules by simply selecting inputs instead of or setting up a high performance computing environment or dealing with DevOps. Our tools are used by over a thousand researchers, including many biotechs, the NIH, and universities. We currently focus on tools in structural biology, including protein structure prediction, protein design, and molecular docking. Get in touch at founders@tamarind.bio
Active Founders
Deniz Kavi
Making great software for life scientists.
Sherry Liu
I'm currently building Tamarind Bio, a no code platform for bioinformatics. I studied Computer Science at Stanford. Previously, I've worked as a software engineer intern at AWS, bioinformatics researcher at NC State, and machine learning researcher at Stanford.
Company Launches
TL;DR
In addition to our no-code web platform (https://www.tamarind.bio/), Tamarind Bio now offers an API for computational teams to use cutting-edge bioinformatics tools for drug discovery without setting up any computing infrastructure or scaling up GPUs. We provide protein structure prediction (e.g. AlphaFold), protein design(RFdiffusion), and molecular docking tools at large scale and easily integrated into your workflow. Additionally, we deploy your custom models for internal use, provide virtual screening services, and provide pipelines to feed the results of models as input to the next downstream tool.
The Problem
Computational biology thrives on doing in silico experiments at large scales. However, it’s tedious to allocate tens/hundreds of GPUs for compute-heavy machine learning tools, make sure they are running as intended, and analyze disparate results. Whether it’s AlphaFold for protein structure prediction, RFdiffusion for binder design or DiffDock for small molecule docking, deploying these tools scalably and connecting them to downstream pipelines steals time away from developing models and doing better science.
The Solution
Tamarind is launching an API for state-of-the-art computational tools in protein design, structure prediction, and docking. Simply submit your job and check back to receive your result. Download these results, keep them in S3, or generate an analysis.
Tamarind now integrates seamlessly with existing computational workflows: we’ll work with you to deploy your custom models and pipelines or run on your existing cloud computing infrastructure.
We are Deniz and Sherry! We met as undergrads at Stanford, where we studied Computer Science and conducted comp bio research, experiencing the inefficiencies of using bioinformatics tools firsthand. Today, 1000+ scientists from institutions including Stanford, Harvard, and Oxford regularly use Tamarind to accelerate their work, as well as many YC companies.
Let us handle the DevOps, while you focus on your science!
Asks
Try Tamarind out! Use our web platform now or email us to learn more about using the API. Documentation: https://www.tamarind.bio/api-docs
- Computational Scientists at small/medium biotech companies in Drug Discovery, Protein/Enzyme Engineering, and Virtual Screening | + adjacent fields like Antibody Discovery
- Software engineers deploying bioinformatics models
- Anyone who might benefit from easy-to-use bioinformatics or cheminformatics
Follow us on LinkedIn to stay updated! Get in touch: founders@tamarind.bio