Molecular property prediction is a cornerstone for accelerating drug discovery, providing a computational way to alleviate the low success rate, high cost, and long development cycle of conventional pharmaceutical research and development. However, existing computational methods have obvious limitations: most current deep learning approaches rely on a single molecular view, failing to fully capture the multi-dimensional features of molecular structures; they mainly focus on intra-molecular features while ignoring inter molecular information and cross-task correlations; and limited labeled data severely impairs their generalization to novel molecules. To address these issues, we propose MSMPP, a multi-scale, multi-view fusion framework for molecular property prediction that learns intra- and inter scale features simultaneously. For intra-scale feature learning, MSMPP integrates TxGemma-enhanced 1D sequence representations, Graph Transformer-derived 2D topological graph features, and Uni-Mol-derived 3D molecular conformational features. The Graph Transformer models long-range atomic dependencies, while the two pretrained models provide task-agnostic molecular prior knowledge from large-scale pretraining corpora, thereby improving generalization to novel molecules. For inter-scale feature learning, MSMPP constructs an inter-molecular graph (IMG) that explicitly models global pairwise interactions among chemically similar molecules and also extracts the cross-task features. Evaluations on eight MoleculeNet datasets show that MSMPP significantly outperforms state-of-the-art models, demonstrating its effectiveness in integrating multi-view intra-molecular features, inter-molecular features and cross-task information. Overall, MSMPP provides a competitive tool for molecular property prediction and supports the acceleration of drug discovery workflows.
Jiongfeng Chen, Yulian Ding, Yan Yan et al.· IEEE journal of biomedical a...· 0 citations
Targeted drug discovery is fundamentally bottlenecked by the challenge of accurately modeling complex biomolecular interactions, ranging from small-molecule ligand binding to high-order macromolecular assemblies. While traditional physics-based computational methods provide profound mechanistic insights, their clinical utility is frequently hampered by prohibitive computational costs and scalability limitations when addressing highly flexible, cross-scale systems. Conversely, the rapid emergence of pure deep learning offers unprecedented computational speed but suffers from a fundamental “black-box” nature, sometimes yielding physically improbable conformations—often referred to as “hallucinations”—that can pose challenges in real-world experimental validation. To bridge this critical translational gap, the integration of physical principles with artificial intelligence—Physics-Informed Deep Learning (PIDL)—is currently driving a fundamental transition from purely empirical approximations to rational, physically grounded design. This review constructs a strategic framework to critically evaluate these transformative advances, structured around three methodological pillars: (1) Physics-constrained optimization, which integrates thermodynamic principles and integrative experimental restraints at the output level to decode macromolecular dynamics; (2) Physics-encoded architectures, which embed appropriate SE(3) or E(3) geometric symmetries directly into neural network topologies for precise structural recognition; and (3) Physics-guided representations, which project discrete sequences into continuous physicochemical manifolds to enhance interaction prediction. By delineating how physics-based priors synergize with data-driven representation learning, this review not only synthesizes current algorithmic breakthroughs but also provides a comprehensive roadmap for generating physically plausible and thermodynamically stable therapeutics, ultimately accelerating the transition of computationally designed molecules from in silico blueprints to viable clinical candidates.
Hao-Bo Xie, Hao Wang, Xiaojun Yao et al.· The Innovation Drug Discover...· 0 citations