SAIR

Binding affinity prediction is central to drug discovery: it tells us how strongly a candidate molecule (ligand) binds to a target protein, which is key for designing effective drugs. In theory, a ligand’s binding affinity is determined by the 3D interactions in the protein–ligand complex. However, deep learning models that use 3D structures have been limited by a lack of availability. Very few protein–ligand complexes have both a resolved 3D structure and a measured potency (IC50, Ki, etc.), so most AI approaches have had to rely on indirect data like sequences or 2D chemical structures.

One way to overcome this limitation is to generate synthetic training data using predicted structures. Recent advances in protein structure prediction (e.g. AlphaFold) mean we can computationally model protein–ligand complexes and use those for learning. Initial efforts like the PLINDER dataset demonstrated the promise of this approach. SAIR was created to dramatically expand on this idea – providing a massive repository of computationally folded protein–ligand structures with corresponding experimental affinity values. Our goal is to fill the data gap and catalyze more accurate and robust ML models for binding affinity prediction.

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