DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach

Table 3 Comparison of the effectiveness of property targeting task

Method	−2.5 ≤ logP ≤ − 2		5 ≤ logP ≤ 5.5		150 ≤ MW ≤ 200		500 ≤ MW ≤ 550
Method	Success	Diversity	Success	Diversity	Success	Diversity	Success	Diversity
ZINC	0.3%	0.919	1.3%	0.909	1.7%	0.938	0	–
ORGAN	0	–	0.2%	0.909	15.1%	0.759	0.1%	0.907
JT-VAE	11.3%	0.846	7.6%	0.907	0.7%	0.824	16.0%	0.898
GCPN	85.5%	0.392	54.7%	0.855	76.1%	0.921	74.1%	0.920

MW here stands for the Molecular Weight. Success is defined as the percentage of generated molecules in the target range and Diversity is defined as the average pairwise Tanimoto distance between the Morgan fingerprints of the molecules. Citations to ORGAN and JT-VAE are given in the legend to Table 2
Italics values refer to the best results among the methods compared

ISSN: 1758-2946