ShallowBench: Benchmarking Generative Drug Design Models on Shallow-Pocket Targets
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Computer Science > Machine Learning
Title:ShallowBench: Benchmarking Generative Drug Design Models on Shallow-Pocket Targets
Abstract:While generative AI models have demonstrated remarkable success in structure-based drug design, they predominantly rely on deep binding pockets and struggle to sample effective ligands for challenging low-pocketability targets, such as the historically "undruggable" oncology targets KRAS and MYC. To address this gap, we introduce ShallowBench, a strictly curated benchmark of 5,780 shallow-pocket targets extracted from CrossDocked2020. By computing the difference between an Alpha Shape "lid" volume and the underlying protein atom voxel volume, we successfully isolated targets with low concavity while ensuring sufficient surface area for binding. Evaluating various state-of-the-art generative models reveals weaker predicted binding affinity on these low-concavity interfaces. ShallowBench therefore provides a rigorous benchmark for generative biology models and highlights the necessity of new architectural innovations or loss functions capable of navigating these challenging targets.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM); Quantitative Methods (q-bio.QM) |
| Cite as: | arXiv:2606.06717 [cs.LG] |
| (or arXiv:2606.06717v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.06717
arXiv-issued DOI via DataCite (pending registration)
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