Exploring Low-Toxicity Chemical Space with Deep Learning for Molecular Generation,Journal of Chemical Information and Modeling

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Exploring Low-Toxicity Chemical Space with Deep Learning for Molecular Generation
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2022-06-17 , DOI: 10.1021/acs.jcim.2c00671
Yuwei Yang ₁ , Zhenxing Wu ₂ , Xiaojun Yao ₃ , Yu Kang ₂ , Tingjun Hou ₂ , Chang-Yu Hsieh ₄ , Huanxiang Liu _{1,

5}

Affiliation

Creating a wide range of new compounds that not only have ideal pharmacological properties but also easily pass long-term toxicity evaluation is still a challenging task in current drug discovery. In this study, we developed a conditional generative model by combining a semisupervised variational autoencoder (SSVAE) with an MGA toxicity predictor. Our aim is to generate molecules with low toxicity, good drug-like properties, and structural diversity. For multiobjective optimization, we have developed a method with hierarchical constraints on the toxicity space of small molecules to generate drug-like small molecules, which can also minimize the effect on the diversity of generated results. The evaluation results of the metrics indicate that the developed model has good effectiveness, novelty, and diversity. The generated molecules by this model are mainly distributed in low-toxicity regions, which suggests that our model can efficiently constrain the generation of toxic structures. In contrast to simply filtering toxic ones after generation, the low-toxicity molecular generative model can generate molecules with structural diversity. Our strategy can be used in target-based drug discovery to improve the quality of generated molecules with low-toxicity, drug-like, and highly active properties.

中文翻译：

通过深度学习探索低毒性化学空间以进行分子生成

创造范围广泛的新化合物，不仅具有理想的药理特性，而且易于通过长期毒性评价，仍然是当前药物发现的一项艰巨任务。在这项研究中，我们通过将半监督变分自动编码器 (SSVAE) 与 MGA 毒性预测器相结合，开发了一个条件生成模型。我们的目标是产生具有低毒性、良好的药物样特性和结构多样性的分子。对于多目标优化，我们开发了一种对小分子毒性空间进行分级约束的方法来生成类药物小分子，该方法还可以最大限度地减少对生成结果多样性的影响。指标评价结果表明，所开发的模型具有良好的有效性、新颖性和多样性。该模型生成的分子主要分布在低毒性区域，这表明我们的模型可以有效地限制有毒结构的生成。与生成后简单过滤有毒分子相比，低毒分子生成模型可以生成具有结构多样性的分子。我们的策略可用于基于靶点的药物发现，以提高生成的低毒、类药物和高活性分子的质量。

更新日期：2022-06-17

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