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Preprint
Jul 2026
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.
V. Shankar, Emil Annevelink
· 0 citations
Conference
Open access
Sep 2026
UniPocket: Physics-Aware Geometric Graph Learning with Manifold Completeness for Ligand-Specific Binding Site Prediction
Predicting ligand binding sites on protein surfaces requires capturing complex local geometries and satisfying physical constraints. Existing voxel-based methods suffer from high computational costs and rotation sensitivity, while standard point-cloud GNNs often lack geometric completeness—failing to distinguish chiral...
Kang-Xin Chen, Jie-Yu Zhao, Jin-Li Hu et al.
· Proceedings of the Thirty-Fi... · 0 citations
Aug 2026
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.
Abdulaziz W. Alherz, C. Tezak, Mohammed S. Alhajeri
· Industrial & Engineering... · 0 citations
Open access
Jan 2026
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.
Augustine Ouru, Xi Chen, Cameron Yeagle et al.
· Computational and Mathematic... · 0 citations
Preprint
Jul 2026
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...
Steffen Wedig, F. Burton, Rokas Elijošius et al.
· 0 citations
Conference
Jul 2026
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...
Yu Liu, Jonathan D. Hirst, Jianfeng Ren et al.
· Annual International Compute... · 0 citations