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. 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
The continuous advancements in generative AI are pushing the boundaries of drug discovery, prompting specialized benchmarks as models mature.
This development addresses a critical limitation in AI-driven drug design, potentially unlocking previously 'undruggable' disease targets and accelerating therapeutic development for complex diseases.
The ability to effectively design drugs for shallow-pocket targets will broaden the scope of AI's applicability in pharmaceuticals, moving beyond easy-to-bind disease mechanisms.
- · Pharmaceutical R&D
- · Oncology patients
- · AI drug discovery companies
- · Synthetic biology research
- · Traditional drug discovery pipelines (comparatively)
- · Diseases with only shallow-pocket targets (less 'undruggable' over time)
New drug candidates for historically challenging cancer targets will enter discovery pipelines more rapidly.
The cost and timeline for developing drugs for complex diseases like cancer could significantly decrease, leading to more accessible treatments.
The success in shallow-pocket design could spur further AI specialization in biomolecular engineering, accelerating progress in synthetic biology and personalized medicine.
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Read at arXiv cs.LG