Jun 2025· Advancement of science· Vol 12· 6 citations· 29 references
Medicine
TL;DR
The Q‐GEM comprises a GNN embedded with the molecular electronic and complete 3D geometrical structural information as well as several well‐designed multiscale SSL tasks, achieving superior absolute molecular conformation prediction and conformational discrimination.
Abstract
Recently, various self‐supervised learning (SSL) methods based on 3D graph neural networks (GNNs) have been developed to comprehensively represent the structural information of molecules in 3D space; this is essential for discovering new drugs. However, existing methods fail to comprehensively characterize the 3D structures of molecules and neglect the electronic structural information that significantly influences key properties such as molecular reactivity, strong electrostatic interactions, and chemical adsorption. Therefore, here, a novel molecular representation learning method is constructed, Q‐GEM, incorporating quantum and geometric structural information enhancement, based on the quantum chemical property database QuanDB and SSL methods. Q‐GEM comprises a GNN embedded with the molecular electronic and complete 3D geometrical structural information as well as several well‐designed multiscale SSL tasks, achieving superior absolute molecular conformation prediction and conformational discrimination. The Q‐GEM achieved state‐of‐the‐art performance in 12 out of 13 prediction tasks on the MoleculeNet dataset, with an average performance improvement of 3.3% and 2.0% for classification and regression prediction tasks, respectively. Moreover, an average performance improvement of 5.2% is achieved in three localized quantum chemical properties, fully demonstrating the excellent performance of Q‐GEM in distinguishing molecular electronic structures. The Q‐GEM represents a novel, powerful breakthrough for accurate molecular property prediction.
Machine-learning-based modeling of molecular crystals is limited by representations that encode geometric structure while overlooking the electronic features that often govern solid-state behavior. We introduce a multimodal learning framework that integrates crystal graphs with solid-state Quantum Theory of Atoms in Molecules (QTAIM)-informed molecular graphs for enhanced predictions of solid-state properties. Our architecture employs modality-specific graph neural network encoders and a bidirectional gated fusion mechanism to capture complementary information between the two representations. When applied to band gap prediction in organic molecular crystals, the fused model outperforms geometry-only and molecular-only baselines. Analysis of the learned representations reveals that QTAIM descriptors contribute interpretable signals associated with intermolecular interactions and density redistribution within the crystal, highlighting the value of quantum-informed learning for advancing data-driven solid-state modeling.
Ferdawss Ihiri, M. García-Garibay, Anastassia N. Alexandrova· Journal of Chemical Informat...· 0 citations
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
This review provides a systematic overview of recent advances in SSL-based molecular property prediction and analyzes how multimodal molecular representation learning by integrating sequence, graph, three-dimensional structure, and textual information can improve the quality and expressiveness of molecular representations.
Shuning Yang, Lei Deng· Journal of Chemical Informat...· 0 citations
Experiments show that PWAV generally improves over classical fingerprint descriptors within learned models and achieves competitive performance relative to established external baselines on several endpoints, positioning PWAV as a competitive and chemically transparent component for hybrid molecular property prediction, rather than as a replacement for domain-specific benchmark systems.
M. Afzal, S. Siddiqi· Physica Scripta· 0 citations
Active learning provides an efficient strategy for molecular property prediction by iteratively prioritizing compounds for experimental evaluation. However, the effectiveness of active learning pipelines depends strongly on the choice of molecular representation, and systematic understanding of how representation families affect the active learning process in terms of uncertainty and predictive performance remains limited. In this work, we introduce ActiveFusion, a framework for integrating heterogeneous molecular representations within active learning workflows for molecular property prediction. The framework enables systematic evaluation of physicochemical descriptors, molecular fingerprints, learned graph neural network (GNN) representations, and pretrained Transformer-based representations, as well as feature-level fusion strategies that combine complementary chemical information sources. ActiveFusion evaluates models across four molecular property prediction regression tasks. Across datasets, we demonstrate that feature fusion between learned graph representations and physicochemical descriptors consistently improves prediction performance and discovery (average final iteration R2 of 0.71, 0.67, and 0.54 for our overall best representations Chemprop+RDKit, Chemprop, and RDKit, respectively). We show that exploration-driven acquisition strategies enhance scaffold coverage and promote sampling of structurally novel regions of chemical space, and that model-agnostic acquisition of new compounds based on diversity has strong performance. Notably, both pretrained and finetuned Transformer-based embeddings do not consistently outperform physicochemical features or GNN-learned representations in our setting, highlighting the continued relevance of chemically interpretable features and learned features from supervised, task-specific models for active learning applications in molecular property prediction. Overall, ActiveFusion provides a systematic framework for studying representation-acquisition interactions in molecular discovery with representation fusion capabilities. Our study offers practical guidance for designing active learning pipelines that balance prediction accuracy with chemical space exploration.
Nelson Evbarunegbe, Shiyun Wa, Luke Taylor et al.· Journal of Chemical Informat...· 0 citations
This work releases OpenGEM26 (Open Generated Ensemble of Molecules, 2026), a large-scale dataset comprising 200,000 unique molecules and 4.4 million conformations composed of H, C, N, O, S and Cl with up to ten heavy atoms, providing a high-quality resource and robust ML potential for efficient simulations of sulfur- and chlorine-containing organic molecules.
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduSep 9, 2026
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
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