SAIR: Accelerating Pharma R&D with AI-Powered Structural Intelligence

This summer, SandboxAQ released the Structurally Augmented IC50 Repository ( SAIR ) , the largest dataset of co-folded 3D protein-ligand structures paired with experimentally measured IC₅₀ labels, directly linking molecular structure to drug potency and overcoming a longstanding scarcity in training data. This dataset is now available on Hugging Face, and for the first time, researchers have open access to more than 5 million AI‑generated, high‑accuracy protein-ligand 3D structures, each paired with validated empirical binding potency data.

SAIR is an open-sourced dataset and is publicly available for free under a permissive CC BY 4.0 license, making it immediately actionable for commercial and non-commercial R&D pipelines. More than just a dataset, SAIR is a strategic asset that bridges the long-standing data gap in AI-powered drug design. It empowers pharmaceutical, biotech, and tech‑bio leaders to accelerate R&D, expand target horizons, and supercharge AI models – moving more of the costly, lengthy drug design and optimization from the wet lab to in silico . This means shorter hit‑to‑lead timelines, more efficient lead optimization, fewer dead‑end projects, and a more predictable path from initial idea to clinical candidate.

AI and computer-aided design have great potential in dramatically accelerating the development of new drugs. For decades, scientists have dreamed about AI that could identify or design a potent, non-toxic, and efficacious compound from a prompt describing the disease pathway, practically compressing years of drug R&D into a few minutes on a computer. However, this vision is bottlenecked by AI's ability to predict critical drug properties like potency, toxicity, etc., based solely on its molecular structure.

Consequently, the two teams were able to achieve > 95% GPU compute utilization for generating the SAIR dataset. This enabled us to create SAIR in three weeks – as opposed to the original estimate of three months (more than a 4X speed up) – and resulted in a highly optimized, GPU-native computational workflow that seamlessly integrates with today’s cutting-edge enterprise compute environments.

We can’t wait to see what researchers will build using SAIR, and SandboxAQ experts are here to support them throughout the discovery process.

Furthermore, traditional structure‑based discovery is often slowed early by the determination of reliable 3D structures. Three‑dimensional molecular structure dictates a molecule’s functionality, dynamics and interactions, which is especially important when a potential drug candidate is expected to bind to a human protein target.

Experimental methods, such as X-ray crystallography and cryo-EM, require extensive time and investment, and many promising disease targets still lack experimentally validated structural information. Computer simulations have helped lower the barrier of getting 3D structures and predicting binding affinity. However, earlier generations of algorithms for protein folding and docking (like AlphaFold and Vina respectively) only predict static snapshots of molecules and proteins (which, in reality, are inherently dynamic and shape-changing).

SAIR solves that constraint by compiling over 1 million unique computationally co-folded protein–ligand pairs , ultimately yielding 5.24 million distinct 3D complexes (five different co-folded structures per pair). Each structure is paired with a curated IC₅₀ measurement from ChEMBL or BindingDB , providing for the first time a scalable link between high-quality 3D structures and drug potency, and bridging the historic data gap that has hindered AI-driven discovery. Deep-learned affinity models such as Boltz-2, trained on similar data, have been shown to yield up to a 1,000x speed-up over the traditional, first-principle approach.