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New ML frameworks boost protein-ligand binding affinity prediction

By PulseAugur Editorial · [4 sources] ·

Two new machine learning frameworks, RicciBind and CPES, have been introduced for predicting protein-ligand binding affinity, a crucial step in drug discovery. RicciBind utilizes Ricci curvature and optimal transport to model molecular interactions, enhancing structural awareness and global alignment. CPES incorporates physics-informed curvature representations derived from potential energy surfaces to account for molecular flexibility and binding-induced conformational changes. Both methods demonstrate improved accuracy and interpretability in predicting binding affinity on benchmark datasets. AI

IMPACT These new frameworks offer improved accuracy and interpretability for drug discovery, potentially accelerating the development of new therapeutics.

RANK_REASON Two arXiv papers introduce novel machine learning methods for a specific scientific problem.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

New ML frameworks boost protein-ligand binding affinity prediction

COVERAGE [4]

  1. arXiv cs.LG TIER_1 English(EN) · Shuai Li, Chuan-Xian Ren, Yuhao Li, Ziqi Huang, Yue Pan, Mingzhe Tang, Hong Yan ·

    Curvature-Guided Geometric Representation for Protein-Ligand Binding Affinity Prediction

    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…

  2. arXiv cs.LG TIER_1 English(EN) · Peng-Fei Sun, Chuan-Xian Ren, Hong Yan ·

    Curvature-Informed Potential Energy Surface for Protein-Ligand Binding Affinity Prediction

    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…

  3. arXiv cs.LG TIER_1 English(EN) · Hong Yan ·

    Curvature-Informed Potential Energy Surface for Protein-Ligand Binding Affinity Prediction

    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…

  4. arXiv cs.LG TIER_1 English(EN) · Hong Yan ·

    Curvature-Guided Geometric Representation for Protein-Ligand Binding Affinity Prediction

    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…

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