arXiv Machine Learning
Jun 15
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.
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.
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RAVEN: Frozen Random Graph Reservoirs with Physics-Informed Interaction Fingerprints for Protein-Ligand Binding Affinity Prediction
arXiv:2608. 09099v1 Announce Type: new Abstract: Quantitative estimation of protein-ligand binding affinity from three-dimensional complex structures is a fundamental task in structure-based computational chemistry and molecular modeling.
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ProtScape: A molecular structure and energy-aware representation for protein conformation generation
arXiv:2410.20317v2 Announce Type: replace Abstract: Molecular dynamics (MD) simulations are a principled but computationally expensive approach for studying protein conformational variability, making...
arXiv Machine Learning
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KEPLA: A Knowledge-Enhanced Deep Learning Framework for Accurate Protein-Ligand Binding Affinity Prediction
arXiv:2506. 13196v5 Announce Type: replace Abstract: Accurate prediction of protein-ligand binding affinity is critical for drug discovery.
arXiv Machine Learning
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EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction
EquiPocket is an E(3)-equivariant Graph Neural Network designed to predict ligand binding sites on proteins. It processes proteins as geometric graphs, extracting local surface atom geometry, modeling chemical and spatial relationships, and performing equivariant message passing to capture surface geometry. A dense attention output layer mitigates issues caused by variable protein sizes, and experiments show the method outperforms current state‑of‑the‑art approaches.
arXiv AI
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SenCos-GEM: SENet-Calibrated and Law-of-Cosines-Constrained Geometry-Enhanced Molecular Representation for Property Prediction
arXiv:2607. 20551v1 Announce Type: cross Abstract: Effective molecular representation learning is crucial for accurate molecular property prediction.
arXiv AI
Jul 24
Beyond SBDD: Geometric Deep Learning in Polypharmacology and Multi-target Drug Design
arXiv:2607. 20550v1 Announce Type: cross Abstract: The traditional "one drug, one target" paradigm of structure-based drug design (SBDD) frequently proves inadequate for treating multifactorial diseases such as cancer and neurodegenerative disorders, owing to compensatory signaling pathways and the emergence of drug resistance.
arXiv AI
Jun 19
Protein Representation Learning with Secondary-Structure and Energy-Filtered Hydrogen-Bond Graphs
arXiv:2606. 19374v1 Announce Type: cross Abstract: Graph-based representations are widely used in protein modeling, yet many existing approaches rely primarily on sequence adjacency or geometric proximity, which only partially reflect the principles governing protein folding.
arXiv AI
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Structure-Guided Adaptive Propagation for Protein-Protein Interaction Site Prediction
arXiv:2606. 01781v1 Announce Type: new Abstract: Accurate prediction of protein-protein interaction sites (PPIS) is essential for understanding cellular processes, disease mechanisms, and therapeutic target discovery.
arXiv AI
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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining
arXiv:2509. 22468v2 Announce Type: replace-cross Abstract: High-quality molecular representations are essential for property prediction and molecular design, yet large labeled datasets remain scarce.
arXiv AI
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Chemical and geometric representation fidelity improves drug--target affinity prediction
The paper introduces ReGeoDTA, a framework that preserves chemical heterogeneity and continuous geometric relationships in drug and protein representations to improve drug–target affinity prediction. Experiments on three benchmark datasets show that maintaining representation fidelity consistently enhances predictive accuracy across various DTA architectures, while degrading representations harms performance and cannot be recovered by more complex downstream models. The study highlights representation fidelity as a key upstream design principle for accurate and generalizable affinity prediction.
arXiv Machine Learning
Aug 7
Physics-Based Molecular Fingerprints from Spectral Graph Theory Provide Efficient Geometry-Aware Measures of Chemical Similarity
arXiv:2608. 05336v1 Announce Type: cross Abstract: Molecular representations are essential for the evaluation of molecular similarity and the development of structure-property relationships.