A geometric rotation-equivariant spherical convolutional and gaussian radial basis network for predicting protein-ligand binding affinity

Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials

A novel anisotropic machine learning CG potential is introduced that extends the point particle representation of atomic nuclei to massive ellipsoidal beads with orientation-dependent features, enabling the learning of energies, forces, and torques directly from atomistic data.

UniPocket: Physics-Aware Geometric Graph Learning with Manifold Completeness for Ligand-Specific Binding Site Prediction

Molecular Property Prediction via Sparse Binary Matrix Representation and Convolutional Neural Networks

The SBMR-CNN model demonstrates highly competitive accuracy, outperforming the CM, Uni-Mol+, and MPNN-2D benchmarks, while closely approaching the performance of the more computationally intensive MPNN-3D and SOAP descriptors, as well as the RF-MF model.

An algebraic graph neural network model for protein-ligand binding affinity prediction

An Algebraic Graph Neural Network model designed to encode molecular structures into a low-dimensional graph representation while preserving critical biochemical interactions is introduced, demonstrating superior performance in binding affinity prediction compared to state-of-the-art scoring functions.

Rem3Di: Learning smooth, chiral 3D molecular descriptors from atomistic foundation models

Rem3Di is introduced, a representation-learning framework that repurposes latent features from atomistic foundation models as transferable molecular descriptors for property prediction and virtual screening and provides a route from simulation-trained atomistic representations to transferable, chirality-aware molecular representations for chemical machine learning.

GDGraph: Geometry-Enhanced Dual-View Graph for Molecular Representation Learning

Learning effective molecular representations is crucial for accurate property prediction in AI-aided drug discovery. However, most existing molecular pre-training methods are still primarily based on 2D topological graphs, limiting their ability to exploit 3D geometric information. Moreover, methods that do incorporate 3D geometry often do not distinguish between the roles of atom-centered and bond-centered representations. To address these limitations, we propose GDGraph, a geometryenhanced dual-view framework for molecular representation learning. GDGraph models molecular geometry from two complementary structural perspectives: an atom view for capturing global spatial dependencies and a bond view for modeling local geometric patterns. To support this dual-view design, we introduce a multi-scale geometric feature encoding scheme and a view-specific geometry-aware learning strategy, enabling each view to focus on the geometric dependencies it is best suited to capture. Extensive experiments demonstrate that GDGraph achieves strong and stable performance on molecular property prediction benchmarks, and effectively predicts geometrysensitive quantum chemical properties on the QM9 dataset.