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New benchmark ShallowBench targets AI drug design for shallow pockets

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ShallowBench, a new benchmark designed to evaluate generative AI models used in drug design, specifically focusing on targets with shallow binding pockets. Existing models perform poorly on these challenging targets, which are common in areas like oncology. ShallowBench, comprising 5,780 targets, aims to drive innovation in AI architectures and loss functions to improve drug discovery for historically difficult-to-target proteins. AI

IMPACT Highlights limitations in current generative AI for drug design, spurring development of new models for challenging biological targets.

RANK_REASON The cluster contains a research paper introducing a new benchmark for AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark ShallowBench targets AI drug design for shallow pockets

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The cluster contains a research paper introducing a new benchmark for AI models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Saket Reddy, Shiwei Liu ·

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

    arXiv:2606.06717v1 Announce Type: cross 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, su…

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