A biologically-inspired multi-modal evaluation of molecular generative machine learning
This provides a more efficient tool for evaluating molecular generative outputs and enriching the drug discovery process, though it is incremental as it builds on existing evaluation methods by adding biological focus.
The authors tackled the lack of biological and functional evaluation for molecular generative models by proposing a novel benchmark with three reference datasets and metrics like drug-target affinity prediction and molecular docking, revealing that unimodal predictors can lead to erroneous conclusions and a multi-modal approach is preferable.
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.