While generative models have recently become ubiquitous in many scientific
areas, less attention has been paid to their evaluation. For molecular
generative models, the state-of-the-art examines their output in isolation or
in relation to its input. However, their biological and functional properties,
such as ligand-target interaction is not being addressed. In this study, a
novel biologically-inspired benchmark for the evaluation of molecular
generative models is proposed. Specifically, three diverse reference datasets
are designed and a set of metrics are introduced which are directly relevant to
the drug discovery process. In particular we propose a recreation metric, apply
drug-target affinity prediction and molecular docking as complementary
techniques for the evaluation of generative outputs. While all three metrics
show consistent results across the tested generative models, a more detailed
comparison of drug-target affinity binding and molecular docking scores
revealed that unimodal predictiors can lead to erroneous conclusions about
target binding on a molecular level and a multi-modal approach is thus
preferrable. The key advantage of this framework is that it incorporates prior
physico-chemical domain knowledge into the benchmarking process by focusing
explicitly on ligand-target interactions and thus creating a highly efficient
tool not only for evaluating molecular generative outputs in particular, but
also for enriching the drug discovery process in general.

本研究为分子生成模型提出了一个新的具有生物学启发的评估基准，重点关注配体 - 靶标相互作用，提出了一系列有关蛋白质配体亲和预测、分子对接等与药物发现过程直接相关的指标，并采用多模态方法结合各个指标进行评估，结果表明该单元模式可能导致关于靶标与分子相互作用的错误结论，从而创建了一个高度有效的工具来评估分子生成输出，为药物发现过程的丰富提供了可能性。