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ShallowBench: Benchmarking Generative Drug Design Models on Shallow-Pocket Targets

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arXiv:2606. 06717v1 Announce Type: new 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.

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Authors: Saket Reddy, Shiwei Liu
Released: 2026-06-08 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because ShallowBench: Benchmarking Generative Drug Design Models on Shallow-Pocket Targets overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)", clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)". Matching terms: under, alpha, eval, rate, binding, full, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.06717

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