Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties. To improve the compound design process, we introduce Mol-CycleGAN—a CycleGAN-based model that generates optimized compounds with high structural similarity to the original ones. Namely, given a molecule our model generates a structurally similar one with an optimized value of the considered property. We evaluate the performance of the model on selected optimization objectives related to structural properties (presence of halogen groups, number of aromatic rings) and to a physicochemical property (penalized logP). In the task of optimization of penalized logP of drug-like molecules our model significantly outperforms previous results.

中文翻译：

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Mol-CycleGAN：用于分子优化的生成模型

设计具有所需特性的分子是药物开发中的最大挑战之一，因为它需要针对许多复杂特性优化化合物结构。为了改善化合物的设计过程，我们引入了Mol-CycleGAN（基于CycleGAN的模型），该模型可生成与原始化合物具有高度结构相似性的优化化合物。即，给定一个分子，我们的模型会生成结构相似的分子，并具有最优化的考虑属性值。我们在与结构特性（卤素基团的存在，芳香环的数量）和理化特性（惩罚的logP）有关的所选优化目标上评估模型的性能。在优化类药物分子的惩罚logP的任务中，我们的模型明显优于先前的结果。