A Systematic Assessment of Deep Learning Models for Molecule Generation
This work addresses the need for standardized evaluation in drug discovery for researchers, but it is incremental as it focuses on comparing existing methods rather than introducing new ones.
The authors tackled the lack of systematic comparison among deep learning models for molecule generation by creating an extensive testbed and evaluating various VAE methods from literature, presenting results that benchmark these approaches.
In recent years the scientific community has devoted much effort in the development of deep learning models for the generation of new molecules with desirable properties (i.e. drugs). This has produced many proposals in literature. However, a systematic comparison among the different VAE methods is still missing. For this reason, we propose an extensive testbed for the evaluation of generative models for drug discovery, and we present the results obtained by many of the models proposed in literature.