dMXP: A De Novo Small-Molecule 3D Structure Predictor with Graph Attention Networks

• Published in: Journal of chemical information and modeling 2024-05, Vol.64 (9), p.3744-3755
• Main Authors: Ai, Haopeng, Wu, Deyin, Zhou, Huihao, Xu, Jun, Gu, Qiong
• Format: Article
• Language: English
• Subjects: Binding; Biological activity; Crystal structure; Density functional theory; Design optimization; Drug Design; Ligands; Machine Learning and Deep Learning; Models, Molecular; Molecular Conformation; Molecular structure; Quantitative Structure-Activity Relationship; Receptors; Small Molecule Libraries - chemistry; Static Electricity
• ISSN: 1549-9596; 1549-960X
• DOI: 10.1021/acs.jcim.4c00391

Generating the three-dimensional (3D) structure of small molecules is crucial in both structure- and ligand-based drug design. Structure-based drug design needs bioactive conformations of compounds for lead identification and optimization. Ligand-based drug design techniques, such as 3D shape similarity search, 3D pharmacophore model, 3D-QSAR, etc., all require high-quality small-molecule ligand conformations to obtain reliable results. Although predicting a small molecular bioactive conformer requires information from the receptor, a crystal structure of the molecule is a proper approximation to its bioactive conformer in a specific receptor because the binding pose of a small molecule in its receptor’s binding pockets should be energetically close to the crystal structures. This study presents a de novo small molecular structure predictor (dMXP) with graph attention networks based on crystal data derived from the Cambridge Structural Database (CSD) combined with molecular electrostatic information calculated by density-functional theory (DFT). Two featuring strategies (topological and atomic partial change features) were employed to explore the relation between these features and the 3D crystal structure of a small molecule. These features were then assembled to construct the holistic 3D crystal structure of a molecule. Molecular graphs were encoded using a graph attention mechanism to deal with the issues of the inconsistencies of local substructures contributing to the entire molecular structure. The root-mean-square deviation (RMSDs) of approximately 80% dMXP predicted structures and the native binding poses within receptors are less than 2.0 Å.