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

• URL: http://arxiv.org/abs/2606.14159v1
• Date: Fri, 12 Jun 2026 06:33:55 GMT
• Title: Curvature-Guided Geometric Representation for Protein-Ligand Binding Affinity Prediction
• Abstract summary: RicciBind is a representation framework that integrates curvature-guided hierarchical structure learning with optimal transport (OT)-based cross-domain alignment.<n>Our experiments demonstrate that RicciBind achieved superior predictive performance and generalization across PLA benchmarks and virtual screening tasks.
• Score: 38.825664822163816
• License: http://creativecommons.org/licenses/by/4.0/
• 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 and globally coordinated cross-molecular interactions, limiting their ability to model complex binding mechanisms. Here, we propose RicciBind, a geometric representation framework that integrates curvature-guided hierarchical structure learning with optimal transport (OT)-based cross-domain alignment to model molecular interactions. Specifically, RicciBind leverages Ricci curvature to capture local interaction tightness within molecular structures, enhancing structural awareness and organizing atomic interactions into curvature-aware hierarchical representations. An OT-based cluster matching mechanism then aligns protein and ligand clusters across heterogeneous domains under geometric constraints, enabling globally consistent correspondences and revealing higher-order interaction patterns beyond local neighborhoods. By coupling curvature-guided structure encoding with OT-driven cross-domain alignment, RicciBind effectively models complex interaction semantics and substantially improves both the accuracy and interpretability of binding affinity prediction. Extensive experiments demonstrate that RicciBind achieved superior predictive performance and generalization across PLA benchmarks and virtual screening tasks. Ablation studies further confirmed the essential role of Ricci curvature in enhancing molecular interaction representations.

Related papers

• Curvature-Informed Potential Energy Surface for Protein-Ligand Binding Affinity Prediction [30.613275405893265]
  We propose a curvature-informed potential energy surface (CPES) graph neural network for protein-ligand binding affinity prediction.<n>CPES incorporates physics-informed curvature representations to model conformational flexibility.<n>CPES achieves improved predictive performance and offers physical interpretability.
  arXiv  Detail & Related papers  (2026-06-12T07:53:26Z)
• Demystifying Multimodal Biomolecular Co-design With Intrinsic Geodesic Coupling [25.990662798924475]
  Biomolecules play a central role in biological systems, arising from the tight interplay between sequence and three-dimensional structure.<n>Recent generative models for biomolecular co-design aim to capture this interplay by jointly modeling coupled modalities.<n>Existing approaches largely adopt a parallel execution of marginal generative processes, implicitly enforcing fixed synchronous coupling.<n>We argue that a critical but overlooked degree of freedom lies in how these marginal processes are temporally coupled during training and generation.
  arXiv  Detail & Related papers  (2026-06-01T03:25:17Z)
• Hierarchical Contrastive Learning for Multi-Domain Protein-Ligand Binding [13.923026855785173]
  We introduce HCLBind, a self-supervised framework for predicting protein-ligand affinity.<n>We show that HCLBind effectively learns discriminative interface features and provides robust uncertainty estimation.<n> Experiments on PDBBind demonstrate that HCLBind effectively learns discriminative interface features and provides robust uncertainty estimation.
  arXiv  Detail & Related papers  (2026-05-19T14:33:02Z)
• Beyond Point-Wise Matching: Structural Representation Alignment for Accelerating Diffusion Transformers [93.3976834364707]
  We propose sREPA, a structural REPresentation Alignment framework to enforce consistency in the relational geometry of feature maps.<n>By encouraging the model to internalize holistic spatial layouts and structural correlations from pre-trained features, sREPA achieves faster and more stable convergence.
  arXiv  Detail & Related papers  (2026-05-16T12:01:04Z)
• K-Models: a Flexible and Interpretable Method for Ordinal Clustering with Application to Antigen-Antibody Interaction Profiles [65.21921642886367]
  K-Models is a novel framework that integrates ordinal constraints and estimates key underlying elements of the random process generating the observed functional profiles.<n>The proposed method is evaluated through simulations and real-world applications.
  arXiv  Detail & Related papers  (2026-05-14T13:35:44Z)
• Curvature-Aware PCA with Geodesic Tangent Space Aggregation for Semi-Supervised Learning [52.452902154360565]
  GTSA-PCA is a geometric extension of Principal Component Analysis.<n>It integrates curvature awareness and geodesic consistency within a unified spectral framework.<n>Our results position GTSA-PCA as a principled bridge between statistical and geometric approaches to dimensionality reduction.
  arXiv  Detail & Related papers  (2026-04-20T20:36:36Z)
• Aligned Manifold Property and Topology Point Clouds for Learning Molecular Properties [55.2480439325792]
  This work introduces AMPTCR, a molecular surface representation that combines local quantum-derived scalar fields and custom topological descriptors within an aligned point cloud format.<n>For molecular weight, results confirm that AMPTCR encodes physically meaningful data, with a validation R2 of 0.87.<n>In the bacterial inhibition task, AMPTCR enables both classification and direct regression of E. coli inhibition values.
  arXiv  Detail & Related papers  (2025-07-22T04:35:50Z)
• KEPLA: A Knowledge-Enhanced Deep Learning Framework for Accurate Protein-Ligand Binding Affinity Prediction [60.23701115249195]
  KEPLA is a novel deep learning framework that integrates prior knowledge from Gene Ontology and ligand properties to enhance prediction performance.<n> Experiments on two benchmark datasets demonstrate that KEPLA consistently outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-06-16T08:02:42Z)
• Representational Alignment with Chemical Induced Fit for Molecular Relational Learning [8.210187365702119]
  Molecular Learning Learning (MRL) is widely applied in natural sciences to predict between molecular pairs by extracting structural features.<n> aligning substructure representations by attention mechanisms lacks guidance from chemical knowledge.<n>ReAlignFit dynamically aligns substructure representation in MRL by introducing chemical Induced Fit-based inductive bias.
  arXiv  Detail & Related papers  (2025-02-07T09:29:21Z)
• State-specific protein-ligand complex structure prediction with a multi-scale deep generative model [68.28309982199902]
  We present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures. Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.
  arXiv  Detail & Related papers  (2022-09-30T01:46:38Z)
• Predicting Protein-Ligand Binding Affinity via Joint Global-Local Interaction Modeling [34.18714126231236]
  We propose a novel global-local interaction framework to predict protein-ligand binding affinity. Our framework embeds the long-range interactions globally and aggregates local short-range interactions, respectively. Experiments demonstrate that our GLI framework outperforms state-of-the-art methods with simple neural network architectures and moderate computational costs.
  arXiv  Detail & Related papers  (2022-09-18T10:17:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.