两种新的机器学习框架RicciBind和CPES被引入，用于预测蛋白质-配体结合亲和力，这是药物发现中的关键一步。RicciBind利用Ricci曲率和最优传输来模拟分子相互作用，增强了结构感知和全局对齐能力。CPES结合了源自势能面的物理信息曲率表示，以考虑分子柔性和结合诱导的构象变化。两种方法在基准数据集上的结合亲和力预测准确性和可解释性方面均有所提高。 AI
影响 这些新框架为药物发现提供了更高的准确性和可解释性，有望加速新疗法的开发。
排序理由 两篇arXiv论文为特定的科学问题引入了新颖的机器学习方法。
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arXiv:2606.14159v1 Announce Type: new Abstract: Protein-ligand binding affinity (PLA) prediction is critical in drug discovery. Despite the notable advancements in machine learning-based approaches, existing methods struggle to jointly characterize local geometric organization an…
arXiv:2606.14217v1 Announce Type: new Abstract: Accurate prediction of protein-ligand binding affinity is essential for structure-based drug discovery. Recent geometric deep learning methods have achieved promising performance by representing protein-ligand complexes as three-dim…
Accurate prediction of protein-ligand binding affinity is essential for structure-based drug discovery. Recent geometric deep learning methods have achieved promising performance by representing protein-ligand complexes as three-dimensional graphs. However, most existing approach…
Protein-ligand binding affinity (PLA) prediction is critical in drug discovery. Despite the notable advancements in machine learning-based approaches, existing methods struggle to jointly characterize local geometric organization and globally coordinated cross-molecular interacti…