Multi-Parameter Optimization (MPO) is a major challenge in New Chemical Entity (NCE) drug discovery projects, and the inability to identify molecules meeting all the criteria of lead optimization (LO) is an important cause of NCE project failure. Several ligand- and structure-based de novo design methods have been published over the past decades, some of which have proved useful multiobjective optimization. However, there is still need for improvement to better address the chemical feasibility of generated compounds as well as increasing the explored chemical space while tackling the MPO challenge. Recently, promising results have been reported for deep learning generative models applied to de novo molecular design, but until now, to our knowledge, no report has been made of the value of this new technology for addressing MPO in an actual drug discovery project. Our objective in this study was to evaluate the potential of a ligand-based de novo design technology using deep learning generative models to accelerate the discovery of an optimized lead compound meeting all in vitro late stage LO criteria.

中文翻译：

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基于配体的从头设计的深度生成模型在多参数优化中的应用

多参数优化（MPO）是新化学实体（NCE）药物发现项目中的主要挑战，而无法识别出符合铅优化（LO）所有标准的分子是NCE项目失败的重要原因。在过去的几十年中，已经发布了几种基于配体和结构的从头设计方法，其中一些方法已证明是有用的多目标优化。但是，仍然需要进行改进，以更好地解决生成的化合物的化学可行性，并在应对MPO挑战的同时增加探索的化学空间。最近，已经报道了应用于从头分子设计的深度学习生成模型的可喜结果，但直到现在，据我们所知，尚未有任何报道称这项新技术在实际的药物发现项目中解决MPO的价值。我们在这项研究中的目标是使用深度学习生成模型来评估基于配体的从头设计技术的潜力，以加速发现符合所有体外晚期LO标准的优化的前导化合物的潜力。