A new paradigm combining quantum-mechanical calculations with machine learning (ML) to rationally design compounds with specific properties from extremely large chemical space is emerging and developing at a rapid pace. In this context, appropriately describing molecules and efficiently extracting patterns from electronic-structure calculations are the core challenges for the success of the quantum-mechanics-based ML approaches. Here, MolNet-3D is introduced, a strong deep learning architecture capable of mapping from a flexible and universal 3D topography descriptor to quantum-mechanical observables of molecules of arbitrary shape. The model can learn an invariant representation without the need for the transformation of atom coordinates into interatomic distances, thus preserving the intrinsic 3D topography information of molecules. The capabilities of MolNet-3D are shown by accurately predicting the various density functional theory calculated properties for molecules, including energetic, electronic, and thermodynamic properties. Compared with the previously proposed ML methods in the MoleculeNet benchmarks, our model generally offers the best performance in those quantum-mechanical tasks, elucidating the importance of intrinsic topography information in molecular representation learning. This work may provide new insight into the construction of molecular ML models from 3D topography recognition perspectives.

中文翻译：

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MolNet-3D：从 3D 拓扑中深度学习分子表示和属性

一种将量子力学计算与机器学习 (ML) 相结合的新范式，以从极大的化学空间中合理设计具有特定性质的化合物，正在迅速出现和发展。在这种情况下，适当地描述分子并有效地从电子结构计算中提取模式是基于量子力学的机器学习方法成功的核心挑战。在这里，介绍了 MolNet-3D，这是一种强大的深度学习架构，能够从灵活且通用的 3D 形貌描述符映射到任意形状分子的量子力学可观测值。该模型可以学习不变的表示，而无需将原子坐标转换为原子间距离，从而保留分子的内在 3D 形貌信息。MolNet-3D 的功能通过准确预测各种密度泛函理论计算的分子特性（包括能量、电子和热力学特性）来展示。与之前在 MoleculeNet 基准中提出的 ML 方法相比，我们的模型通常在那些量子力学任务中提供最佳性能，阐明了内在形貌信息在分子表示学习中的重要性。这项工作可能会为从 3D 形貌识别的角度构建分子 ML 模型提供新的见解。我们的模型通常在那些量子力学任务中提供最佳性能，阐明了内在形貌信息在分子表示学习中的重要性。这项工作可能会为从 3D 形貌识别的角度构建分子 ML 模型提供新的见解。我们的模型通常在那些量子力学任务中提供最佳性能，阐明了内在形貌信息在分子表示学习中的重要性。这项工作可能会为从 3D 形貌识别的角度构建分子 ML 模型提供新的见解。