LIT AI Lab & Institute of Bioinformatics , Johannes Kepler University , 4040 Linz , Austria.
J Chem Inf Model. 2018 Sep 24;58(9):1736-1741. doi: 10.1021/acs.jcim.8b00234. Epub 2018 Aug 28.
The new wave of successful generative models in machine learning has increased the interest in deep learning driven de novo drug design. However, method comparison is difficult because of various flaws of the currently employed evaluation metrics. We propose an evaluation metric for generative models called Fréchet ChemNet distance (FCD). The advantage of the FCD over previous metrics is that it can detect whether generated molecules are diverse and have similar chemical and biological properties as real molecules.
机器学习中新一波成功的生成模型增加了人们对深度学习驱动的从头药物设计的兴趣。然而,由于目前使用的评估指标存在各种缺陷,方法比较变得困难。我们提出了一种称为 Fréchet ChemNet 距离(FCD)的生成模型评估指标。FCD 相对于以前的指标的优势在于,它可以检测生成的分子是否具有多样性,并且具有与真实分子相似的化学和生物学性质。