Graph neural networks (GNNs) have emerged as powerful tools to accurately
predict materials and molecular properties in computational discovery
pipelines. In this article, we exploit the invertible nature of these neural
networks to directly generate molecular structures with desired electronic
properties. Starting from a random graph or an existing molecule, we perform a
gradient ascent while holding the GNN weights fixed in order to optimize its
input, the molecular graph, towards the target property. Valence rules are
enforced strictly through a judicious graph construction. The method relies
entirely on the property predictor; no additional training is required on
molecular structures. We demonstrate the application of this method by
generating molecules with specific DFT-verified energy gaps and octanol-water
partition coefficients (logP). Our approach hits target properties with rates
comparable to or better than state-of-the-art generative models while
consistently generating more diverse molecules.

利用可逆神经网络直接生成具有期望电子性质的分子结构，该方法不需要对分子结构进行额外训练，通过严格的图构建规则确保化合价规则，将该方法应用于生成具有特定能隙和分配系数（logP）的分子，并在生成更多样化分子的同时，以与最先进的生成模型相媲美甚至更好的速率实现目标性质。

使用 GNN 特性预测器作为分子生成器

Using GNN property predictors as molecule generators

This article presents the "PubChemQC B3LYP/6-31G*//PM6" dataset, containing
electronic properties of 85,938,443 molecules. It includes orbitals, orbital
energies, total energies, dipole moments, and other relevant properties. The
dataset encompasses a wide range of molecules, from essential compounds to
biomolecules up to 1000 molecular weight, covering 94.0% of the original
PubChem Compound catalog (as of August 29, 2016). The electronic properties
were calculated using the B3LYP/6-31G* and PM6 methods. The dataset is
available in three formats: (i) GAMESS quantum chemistry program files, (ii)
selected JSON output files, and (iii) a PostgreSQL database, enabling
researchers to query molecular properties. Five sub-datasets offer more
specific data. The first two subsets include molecules with C, H, O, and N,
under 300 and 500 molecular weight respectively. The third and fourth subsets
contain C, H, N, O, P, S, F, and Cl, under 300 and 500 molecular weight
respectively. The fifth subset includes C, H, N, O, P, S, F, Cl, Na, K, Mg, and
Ca, under 500 molecular weight. Coefficients of determination ranged from 0.892
(CHON500) to 0.803 (whole) for the HOMO-LUMO energy gap. These findings
represent extensive investigations and can be utilized for drug discovery,
material science, and other applications. The datasets are available under the
Creative Commons Attribution 4.0 International license at
this https URL

本文介绍了 PubChemQC B3LYP/6-31G*//PM6 数据集，包含 85938443 种分子的电子性质等，并说明了 5 个子数据集及用途，可实现药物发现和材料科学等应用。

PubChemQC B3LYP/6-31G*//PM6 数据集：使用 B3LYP/6-31G * 计算得出 8600 万分子的电子结构

PubChemQC B3LYP/6-31G*//PM6 dataset: the Electronic Structures of 86  Million Molecules using B3LYP/6-31G* calculations

The rational design of molecules with desired properties is a long-standing
challenge in chemistry. Generative neural networks have emerged as a powerful
approach to sample novel molecules from a learned distribution. Here, we
propose a conditional generative neural network for 3d molecular structures
with specified chemical and structural properties. This approach is agnostic to
chemical bonding and enables targeted sampling of novel molecules from
conditional distributions, even in domains where reference calculations are
sparse. We demonstrate the utility of our method for inverse design by
generating molecules with specified motifs or composition, discovering
particularly stable molecules, and jointly targeting multiple electronic
properties beyond the training regime.

本篇论文提出了一种基于条件生成神经网络的三维分子结构的设计方法，该方法不依赖于化学键，并能针对特定分布生成新颖分子，同时能够逆向设计并发现稳定的分子，在多种电子性质方面提高了生成效能。