Tamarind Blog

Tamarind Blog

Thoughts from the Tamarind team on computational protein design and AI+life sciences

BindCraft2: Day One Access on Tamarind cover

BindCraft2: Day One Access on Tamarind

Introducing Tamarind Benchmarks cover

Introducing Tamarind Benchmarks

Introducing the Tamarind Molecular Model Router cover

Introducing the Tamarind Molecular Model Router

Introducing the Tamarind Protein Design Agent cover

Introducing the Tamarind Protein Design Agent

Introducing Pipelines 2.0 cover

Introducing Pipelines 2.0

The Open Models that Outperform AlphaFold3 cover

The Open Models that Outperform AlphaFold3

Building the Inference Layer for Agentic Biology with NVIDIA cover

Building the Inference Layer for Agentic Biology with NVIDIA

BoltzMol-1, BoltzProt-1, and the Boltz API are live on Tamarind, on day 0 cover

BoltzMol-1, BoltzProt-1, and the Boltz API are live on Tamarind, on day 0

Tamarind Bio Selected by Eli Lilly as Technology Partner for TuneLab2.0 cover

Tamarind Bio Selected by Eli Lilly as Technology Partner for TuneLab2.0

ESMFold2 and ESM-C: A World Model of Protein Biology, Now on Tamarind cover

ESMFold2 and ESM-C: A World Model of Protein Biology, Now on Tamarind

Introducing the Tamarind Bio Assay Portal cover

Introducing the Tamarind Bio Assay Portal

Introducing the Tamarind MCP Server cover

Introducing the Tamarind MCP Server

Raising $13.6M to Build the Infrastructure for AI-Powered Drug Discovery cover

Raising $13.6M to Build the Infrastructure for AI-Powered Drug Discovery

BoltzGen: Validated de novo binder design for diverse targets cover

BoltzGen: Validated de novo binder design for diverse targets

OpenFold3 and The Future of Protein Folding cover

OpenFold3 and The Future of Protein Folding

Predicting Antibody Properties & Developability cover

Predicting Antibody Properties & Developability

A New Best Structure Prediction Protocol cover

A New Best Structure Prediction Protocol

A Comprehensive Introduction to AI for Proteins (2026) cover

A Comprehensive Introduction to AI for Proteins (2026)

Boltz-2: State of the Art Structure and Binding Affinity Prediction cover

Boltz-2: State of the Art Structure and Binding Affinity Prediction

Boltzdesign1: Designing De Novo Binders to More Than Just Proteins cover

Boltzdesign1: Designing De Novo Binders to More Than Just Proteins

Computational De Novo Design of Antibodies and Nanobodies cover

Computational De Novo Design of Antibodies and Nanobodies

Computational De Novo Miniproteins As Therapeutics cover

Computational De Novo Miniproteins As Therapeutics

Results for minibinders used as drugs.

Chai-1r, AlphaFold3 Level Performance: Now Completely Open Source cover

Chai-1r, AlphaFold3 Level Performance: Now Completely Open Source

How to Use AlphaFold3 Commercially cover

How to Use AlphaFold3 Commercially

How Well Does AlphaFold3 Work for Antibodies and Nanobodies? cover

How Well Does AlphaFold3 Work for Antibodies and Nanobodies?

AlphaFold3 is by far the current state of the art model for structure prediction, how accurate is it for immune proteins?

Boltz-1: AlphaFold3 Level Performance, Truly Open Source and Commercially Available cover

Boltz-1: AlphaFold3 Level Performance, Truly Open Source and Commercially Available

Researchers from MIT and Genesis Therapeutics announce Boltz-1, a model that achieves AlphaFold3 level sucess in protein, protein-ligand complex structure prediction with no restrictions on use. Here we provide a webserver and API on Tamarind Bio for all to use.

Are Mini Proteins the Next Antibodies? cover

Are Mini Proteins the Next Antibodies?

Mini proteins have many benefits over traditional protein therapuetic modalities, are they on the rise?

Computational PPI Screening cover

Computational PPI Screening

AlphaFold predicts multimer structures well, here's how researchers use it for predicting protein-protein interactions

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