TL;DR
The proposed MPFDock is a tightly coupled equivariant flow matching method for molecular docking guided by multimodal physical constraints in Cartesian space that consistently outperforms existing methods in terms of docking accuracy and physical realism on the evaluated benchmarks.
GeoNet is a physicochemical-principle-guided framework for modeling dual-range atomic interactions that achieves the smallest model size and the shortest training time, demonstrating both superior predictive performance and computational efficiency.
An enhanced local optimization strategy based on curved line search (CLS) is introduced and integrated into AutoDock Vina, resulting in Vina_CLS, demonstrating that improved local optimization can substantially enhance docking performance.
PandaDock’s empirical scoring function ranks 8th of 25 methods evaluated, ahead of every AutoDock Vina and Vinardo configuration tested, while the GNN scores below Vina, consistent with the within-target ceiling identified on SAIR.
Biomolecular binder design for peptides and antibodies requires generating diverse candidates that satisfy stringent three-dimensional geometric constraints while enabling affinity-oriented exploration under strong structural priors. In current generative models, the effective search space for structurally feasible bin...
Accurate identification of near-native ligand binding poses is a central challenge in structure-based drug design. From a physical point of view, the successful construction of a protein-ligand complex structure is dependent on whether protein and ligand can form enough atomically pairwise interactions that result in a...
3D-GEGCL is a framework that integrates 3D geometrically equivariant graph neural networks with self-supervised contrastive learning for robust and accurate PLBA prediction and achieves superior performance over existing leading methods across a range of assessment criteria.
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