Berenger Francois, Tsuda Koji
Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba, 277-8561, Japan.
J Cheminform. 2021 Nov 14;13(1):88. doi: 10.1186/s13321-021-00566-4.
In recent years, in silico molecular design is regaining interest. To generate on a computer molecules with optimized properties, scoring functions can be coupled with a molecular generator to design novel molecules with a desired property profile.
In this article, a simple method is described to generate only valid molecules at high frequency ([Formula: see text] molecule/s using a single CPU core), given a molecular training set. The proposed method generates diverse SMILES (or DeepSMILES) encoded molecules while also showing some propensity at training set distribution matching. When working with DeepSMILES, the method reaches peak performance ([Formula: see text] molecule/s) because it relies almost exclusively on string operations. The "Fast Assembly of SMILES Fragments" software is released as open-source at https://github.com/UnixJunkie/FASMIFRA . Experiments regarding speed, training set distribution matching, molecular diversity and benchmark against several other methods are also shown.
近年来，计算机辅助分子设计正重新受到关注。为了在计算机上生成具有优化性质的分子，评分函数可与分子生成器相结合，以设计具有所需性质概况的新型分子。
在本文中，描述了一种简单的方法，在给定分子训练集的情况下，能够以高频（使用单个CPU核心时为[公式：见正文]分子/秒）仅生成有效的分子。所提出的方法生成多样化的SMILES（或DeepSMILES）编码分子，同时在训练集分布匹配方面也表现出一定倾向。当使用DeepSMILES时，该方法达到峰值性能（[公式：见正文]分子/秒），因为它几乎完全依赖于字符串操作。“SMILES片段快速组装”软件作为开源软件在https://github.com/UnixJunkie/FASMIFRA上发布。还展示了关于速度、训练集分布匹配、分子多样性以及与其他几种方法对比的基准测试的实验。
