The application of deep learning to generative molecule design has shown
early promise for accelerating lead series development. However, questions
remain concerning how factors like training, dataset, and seed bias impact the
technology's utility to medicine and computational chemists. In this work, we
analyze the impact of seed and training bias on the output of an
activity-conditioned graph-based variational autoencoder (VAE). Leveraging a
massive, labeled dataset corresponding to the dopamine D2 receptor, our
graph-based generative model is shown to excel in producing desired conditioned
activities and favorable unconditioned physical properties in generated
molecules. We implement an activity swapping method that allows for the
activation, deactivation, or retention of activity of molecular seeds, and we
apply independent deep learning classifiers to verify the generative results.
Overall, we uncover relationships between noise, molecular seeds, and training
set selection across a range of latent-space sampling procedures, providing
important insights for practical AI-driven molecule generation.
Seung-gu Kang, Joseph A. Morrone, Jeffrey K. Weber, Wendy D. Cornell