Richard Zhu

What I investigated: I used the EvE Bio "pharmome" — a single-laboratory dataset measuring 1,397 approved drugs against 256 human targets (kinases, GPCRs, nuclear receptors) under one consistent framework — to ask two questions heterogeneous public data cannot answer cleanly. First, do modern computational methods (the Boltz-2 affinity predictor and molecular docking) recover the comparative quantities that drive drug-discovery decisions? Second, mined as a screen, does the dataset reveal credible drug-repurposing hypotheses? What I found: Boltz-2 (a state of the art AI model for predicting binding affinity) recovers not just potency but genuine cross-target selectivity (ρ = 0.78 after removing drug and target baselines), and partially recovers resistance-mutation effects. In other words, Boltz-2 is able to figure out, out of a set of proteins, which will actually selectively be bound by a particular drug, as well as how this binding changes when mutations occur in the protein (a very common scenario in cancer that eventually leads to drug mutation). I find that Boltz-2 successfully predicts changes in binding affinity for mutations that physically reshape the drug-binding pocket, failing for allosteric ones. A memorization control showed this resistance signal is the least explained by training-data overlap. Mining the EvE Bio dataset also revealed two exposure-validated, docking-supported repurposing hypotheses that extend to whole drug classes: HIV protease inhibitors like saquinavir binding to the metabolic enzyme LRH-1 to explore metabolic effects of these drugs, and BTK inhibitors like zanubrutinib binding to the immune protein LTB4R. Moreover, we present an additional drug-specific repurposing hypothesis of the drug nintedanib being used against the drug-resistant PDGFRα-D842V mutant (common in many gastrointestinal stromal tumors). Why it matters: Systematically collected, internally consistent drug-target binding datasets like the EvE Bio pharmome can effectively act as computational benchmarks to help us test biological AI models on key drug discovery questions like selectivity and mutation effect prediction, as well as help us develop novel drug repurposing hypotheses.

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