Deep generative models have recently emerged as a promising de novo drug design method. In this respect, deep generative conditional variational autoencoder (CVAE) models are a powerful approach for generating novel molecules with desired drug-like properties. However, molecular graph-based models with disentanglement and multivariate explicit latent conditioning have not been fully elucidated. To address this, we proposed a molecular-graph $\beta$-CVAE model for de novo drug design. Here, we empirically tuned the value of disentanglement and assessed its ability to generate molecules with optimised univariate- or-multivariate properties. In particular, we optimised the octanol-water partition coefficient (ClogP), molar refractivity (CMR), quantitative estimate of drug-likeness (QED), and synthetic accessibility score (SAS). Results suggest that a lower $\beta$ value increases the uniqueness of generated molecules (exploration). Univariate optimisation results showed our model generated molecular property averages of ClogP = 41.07% $\pm$ 0.01% and CMR 66.76% $\pm$ 0.01% by the Ghose filter. Multivariate property optimisation results showed that our model generated an average of 30.07% $\pm$ 0.01% molecules for both desired properties. Furthermore, our model improved the QED and SAS (exploitation) of molecules generated. Together, these results suggest that the $\beta$-CVAE could balance exploration and exploitation through disentanglement and is a promising model for de novo drug design, thus providing a basis for future studies.

翻译：深度生成模型近年已成为一种具有前景的全新药物设计方法。在这方面，深度生成条件变分自编码器（CVAE）模型是生成具有所需药物性质的新型分子的有力工具。然而，基于分子图且具有解耦与多变量显式潜在条件约束的模型尚未得到充分阐释。为解决这一问题，我们提出了一种用于全新药物设计的分子图$β$-CVAE模型。在此，我们通过实验调整了解耦值，并评估其生成具有优化单变量或多变量性质分子的能力。具体而言，我们优化了辛醇-水分配系数（ClogP）、摩尔折射度（CMR）、药物相似性定量估计（QED）及合成可及性评分（SAS）。结果表明，较低的$β$值可提高生成分子的独特性（探索）。单变量优化结果显示，我们的模型生成的分子性质平均值在Ghose过滤条件下分别为ClogP = 41.07% ± 0.01%和CMR = 66.76% ± 0.01%。多变量性质优化结果显示，我们的模型生成的平均30.07% ± 0.01%的分子同时具备两种目标性质。此外，模型还改善了生成分子的QED与SAS（利用）。综上，这些结果表明$β$-CVAE可通过解耦平衡探索与利用，是一种具有前景的全新药物设计模型，为未来研究奠定了基础。