平衡探索与利用：用于从头药物设计的解耦 $β$-CVAE
Balancing Exploration and Exploitation: Disentangled $β$-CVAE in De Novo Drug Design
AI总结：
本文提出分子图 $β$-CVAE，通过调节解耦程度平衡分子生成的探索与利用，实现单变量及多变量类药性质优化。
摘要：
深度生成模型最近已成为一种很有前景的从头药物设计方法。在这方面，深度生成条件变分自编码器（CVAE）模型是生成具有理想类药特性的新型分子的强大方法。然而，基于分子图、具备解耦和多变量显式潜变量条件化的模型尚未得到充分阐明。为解决这一问题，我们提出了一种用于从头药物设计的分子图 $β$-CVAE 模型。在此，我们通过经验调节解耦程度，并评估其生成具有优化的单变量或多变量特性分子的能力。具体而言，我们优化了辛醇-水分配系数（ClogP）、摩尔折射率（CMR）、类药性定量估计（QED）和合成可及性评分（SAS）。结果表明，较低的 $β$ 值会提高生成分子的独特性（探索）。单变量优化结果显示，依据 Ghose 过滤器，我们的模型生成分子的特性平均值为 ClogP = 41.07% ± 0.01%，CMR 为 66.76% ± 0.01%。多变量特性优化结果显示，我们的模型平均生成 30.07% ± 0.01% 同时满足两种期望特性的分子。此外，我们的模型改善了所生成分子的 QED 和 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，是一种很有前景的从头药物设计模型，从而为未来研究提供基础。
英文摘要：
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 $β$-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 $β$ 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 $β$-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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
