MCST-AFN: A Multichannel Spatiotemporal Feature Adaptive Fusion Network Framework Based on a Low-Fidelity Molecular Dynamics Model

The capability of predicting molecular properties plays a crucial role in drug development, and the learning of molecular representations stands as the primary step in tasks aimed at predicting molecular properties. Static three-dimensional (3D) structural information has been shown to significantly aid in molecular representation; however, molecules are in constant motion and change, implying that their properties should be closely linked with dynamic molecular conformations. Traditional four-dimensional (4D) Quantitative Structure-Property Relationship (QSPR) methods, while incorporating time as a dimension, have high computational costs and fail to fully integrate the temporal dimension, leading to ineffective integration of molecular conformation ensembles. Inspired by deep learning-based molecular dynamics (DLMD) techniques and multifidelity learning (MFL) strategies, in this work, a multichannel spatiotemporal feature adaptive fusion network framework (MCST-AFN) based on a low-fidelity molecular dynamics model is proposed. This framework integrates deep learning technology with molecular dynamics (MD) simulations, effectively enhancing molecular representation while significantly reducing computational costs. Initially, a low-fidelity molecular dynamics simulation model is trained using real molecular dynamics simulation data. Compared to existing tools such as Amber, this low-fidelity model can update atomic coordinates at a lower computational cost and output multichannel atom-level embeddings that encapsulate information across different time scales. Subsequently, an attention-based network is constructed to achieve adaptive fusion of multichannel spatiotemporal features, and a self-supervised learning task for atom masking prediction is designed to further enhance molecular representation. The MCST-AFN was tested on 13 benchmark data sets for molecular property prediction, achieving an average performance improvement of 2.10% across 12 data sets. The most significant enhancement was seen in the ESOL data set, with a performance boost of 19.70%.