De novo molecule generation can suffer from data inefficiency; requiring large amounts of training data or many sampled data points to conduct objective optimization. The latter is a particular disadvantage when combining deep generative models with computationally expensive molecule scoring functions (a.k.a. oracles) commonly used in computer-aided drug design. Recent works have therefore focused on methods to improve sample efficiency in the context of de novo molecule drug design, or to benchmark it. In this work, we discuss and adapt a recent sample efficiency benchmark to better reflect realistic goals also with respect to the quality of chemistry generated, which must always be considered in the context of small-molecule drug design; we then re-evaluate all benchmarked generative models. We find that accounting for molecular weight and LogP with respect to the training data, and the diversity of chemistry proposed, re-orders the ranking of generative models. In addition, we benchmark a recently proposed method to improve sample efficiency (Augmented Hill-Climb) and found it ranked top when considering both the sample efficiency and chemistry of molecules generated. Continual improvements in sample efficiency and chemical desirability enable more routine integration of computationally expensive scoring functions on a more realistic timescale.
翻译:因此,最近的工作侧重于在新分子药物设计或基准方面提高样本效率的方法;要求大量培训数据或许多抽样数据点来进行客观优化;后者在将深基因化模型与计算机辅助药物设计中常用的计算昂贵分子评分功能(a.k.a.a.a. orcles)相结合时特别不利;因此,最近的工作侧重于在脱新分子药物设计方面提高样本效率的方法或对其进行基准衡量;在这项工作中,我们讨论并调整最近一个抽样效率基准,以更好地反映所产生化学质量方面的实际目标,而化学质量也必须始终在小分子药物设计的范围内加以考虑;然后重新评价所有基准基因化模型。我们发现,在培训数据以及拟议的化学多样性方面,计算分子重量和LogP,重新排列基因化模型的排序;此外,我们为最近提出的提高样本效率的方法(Augmented Hill-Climb)制定基准,在考虑所产生分子的样本效率和化学质量时,其排名最高;在不断提高样本效率和化学性水平时,能够更经常地计算更昂贵的定级。