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Table 3 Comparison of the effectiveness of property targeting task

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

Method −2.5 ≤ logP ≤ − 2 5 ≤ logP ≤ 5.5 150 ≤ MW ≤ 200 500 ≤ MW ≤ 550
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
  1. 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
  2. Italics values refer to the best results among the methods compared