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English(EN) Trustworthy Protein-Ligand Binding Affinity Prediction via Reliability-Aware Multi-Engine Fusion

新AI框架增强了可信的蛋白质-配体结合亲和力预测

研究人员开发了RELIABLE-BA,一个用于预测蛋白质-配体结合亲和力的新颖框架,该框架增强了计算药物发现的可靠性。这种证据方法将对接引擎建模为使用正态-逆伽马分布的专家,并根据分子上下文调整其不确定性。通过融合这些专家,并关注个体不确定性和引擎间分歧,RELIABLE-BA在实现具有竞争力的预测准确性的同时,显著提高了不确定性校准。这使得能够可靠地过滤高置信度配对,将预测误差降低高达25%,并为AI指导的药物发现提供了原则性的途径。 AI

影响 通过提高蛋白质-配体结合的预测准确性和不确定性校准,增强了AI驱动的药物发现的可靠性。

排序理由 该集群描述了一篇关于用于科学应用的新AI框架的详细研究论文。

在 Hugging Face Daily Papers 阅读 →

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新AI框架增强了可信的蛋白质-配体结合亲和力预测

报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Yongchan Hong, Defu Cao, Wenjin Liu, Thomas Ku, Jordy Homing Lam, Emily Nguyen, Willie Neiswanger, Vsevolod Katritch, Yan Liu ·

    通过可靠性感知多引擎融合实现可信的蛋白质-配体结合亲和力预测

    arXiv:2607.17601v1 Announce Type: cross Abstract: Accurate protein-ligand binding affinity prediction is central to computational drug discovery, yet modern docking engines frequently disagree without indicating which prediction to trust. Consensus scoring and ensemble methods im…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Trustworthy Protein-Ligand Binding Affinity Prediction via Reliability-Aware Multi-Engine Fusion

    Accurate protein-ligand binding affinity prediction is central to computational drug discovery, yet modern docking engines frequently disagree without indicating which prediction to trust. Consensus scoring and ensemble methods improve mean accuracy but treat all predictions iden…