A Drug-Target Specificity Foundation Model for Off-target Prediction, Repurposing, and Generative Design

Abstract

Molecular recognition - which small molecule binds which protein, and with what selectivity - governs the efficacy, safety, and discovery of every therapeutic, yet binding specificity is still determined by experimental screening or by computational methods that first predict three-dimensional structure. Transformer softmax attention is mathematically isomorphic to the Boltzmann distribution governing molecular binding at thermal equilibrium1, an identity that prescribes a single sequence-native architecture: the Specificity Foundation Model (SFM), which computes molecular binding compatibility as a thermodynamic quantity directly from sequence2. The framework was recently realized as prototype encoders across six molecular-recognition domains3. Here we report the small molecule drug-target protein SFM (dtSFM) as the first instance to pair a full-scale encoder with a generative decoder, trained on publicly available data consisting of 714,747 measured drug-protein interactions spanning 522,776 compounds and 22,964 proteins. Throughout, we verify binding predictions with AlphaFold 34 as an orthogonal structural verifier that shares no architecture, training data, or representational basis with dtSFM. From this single dtSFM model we demonstrate the three sequence-native applications of drug discovery: off-target prediction, repurposing, and generative design. The dtSFM encoder retrieves a drugs target, and a targets drug, at 95% and 89% recall-at-10 in distribution, respectively. In the drug[->]target direction it screens off-targets at proteome scale, ranking the documented off-targets of clinical kinase inhibitors at a median of 30th out of 4,910 genes - the top 0.6% of the screen - when validated against a chemoproteomic panel5. In the target[->]drug direction it ranks the full 522,776-compound library against three immunology targets, identifying 46 novel candidates that pass AlphaFold-3 structural gating. The dtSFM cross-attentive decoder generates novel molecules for 16 targets, 850 of 1,200 (71%) designed candidates match the AlphaFold 3 structural confidence of the approved drug (iPTM [≥] 0.9 and interface PAE [≤] 1.67 [A]), with the best candidates reaching iPTM 0.95-0.99 and interface PAE 0.79-1.37 [A]. dtSFM brings computational thermodynamics to every stage where molecular recognition shapes drug discovery; experimental wet-lab validation is the immediate next step.