Abstract: Nigam et al. reported a genetic algorithm (GA) utilizing the SELFIES
representation and also propose an adaptive, neural network-based, penalty that
is supposed to improve the diversity of the generated molecules. The main
claims of the paper are that this GA outperforms other generative techniques
(as measured by the penalized logP) and that a neural network-based adaptive
penalty increases the diversity of the generated molecules. In this work, we
investigated the reproducibility of their claims.
Overall, we were able to reproduce comparable results using the SELFIES-based
GA, but mostly by exploiting deficiencies of the (easily optimizable) fitness
function (i.e., generating long, sulfur containing, chains). In addition, we
also reproduce that the discriminator can be used to bias the generation of
molecules to ones that are similar to the reference set.
In addition, we also attempted to quantify the evolution of the diversity,
understand the influence of some hyperparameters, and propose improvements to
the adaptive penalty.