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.

中文翻译：

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dMXP：具有图注意力网络的 De Novo 小分子 3D 结构预测器

生成小分子的三维 (3D) 结构对于基于结构和配体的药物设计至关重要。基于结构的药物设计需要化合物的生物活性构象来进行先导化合物的识别和优化。基于配体的药物设计技术，如3D形状相似性搜索、3D药效团模型、3D-QSAR等，都需要高质量的小分子配体构象才能获得可靠的结果。尽管预测小分子生物活性构象异构体需要来自受体的信息，但分子的晶体结构是其在特定受体中的生物活性构象异构体的适当近似，因为小分子在其受体结合袋中的结合姿势应该在能量上接近于其在特定受体中的生物活性构象异构体。晶体结构。这项研究提出了一种从头开始的小分子结构预测器（dMXP），其图注意力网络基于剑桥结构数据库（CSD）中的晶体数据，并结合密度泛函理论（DFT）计算的分子静电信息。采用两种特征策略（拓扑特征和原子部分变化特征）来探索这些特征与小分子 3D 晶体结构之间的关系。然后将这些特征组合起来构建分子的整体 3D 晶体结构。使用图注意机制对分子图进行编码，以处理局部子结构对整个分子结构的不一致问题。大约 80% dMXP 预测结构和受体内天然结合位姿的均方根偏差 (RMSD) 小于 2.0 Å。