PDF(Pubmed)
Abstract：
De novo drug design aims to rationally discover novel and potent compounds while reducing experimental costs during the drug development stage. Despite the numerous generative models that have been developed, few successful cases of drug design utilizing generative models have been reported. One of the most common challenges is designing compounds that are not synthesizable or realistic. Therefore, methods capable of accurately assessing the chemical structures proposed by generative models for drug design are needed. In this study, we present AnoChem, a computational framework based on deep learning designed to assess the likelihood of a generated molecule being real. AnoChem achieves an area under the receiver operating characteristic curve score of 0.900 for distinguishing between real and generated molecules. We utilized AnoChem to evaluate and compare the performances of several generative models, using other metrics, namely SAscore and Fréschet ChemNet distance (FCD). AnoChem demonstrates a strong correlation with these metrics, validating its effectiveness as a reliable tool for assessing generative models. The source code for AnoChem is available at https://github.com/CSB-L/AnoChem.
摘要：
从头药物设计旨在合理地发现新的和有效的化合物，同时降低药物开发阶段的实验成本。尽管已经开发了许多生成模型，已经报道了利用生成模型进行药物设计的成功案例。最常见的挑战之一是设计不可合成或不现实的化合物。因此,需要能够准确评估药物设计生成模型提出的化学结构的方法。在这项研究中,我们介绍AnoChem,基于深度学习的计算框架，旨在评估生成的分子是真实的可能性。AnoChem实现了0.900的接收器工作特性曲线下的面积，以区分真实分子和生成分子。我们利用AnoChem来评估和比较几个生成模型的性能，使用其他指标，即SAscore和FréschetChemNet距离(FCD)。AnoChem与这些指标有很强的相关性，验证其作为评估生成模型的可靠工具的有效性。AnoChem的源代码可在https://github.com/CSB-L/AnoChem获得。
