Deep Latent-Variable Models for Controllable Molecule Generation

Yuanqi Du, Yinkai Wang, Fardina Fathmiul Alam, Yuanjie Lu, Xiaojie Guo, Liang Zhao, Amarda Shehu

Abstract: Representation learning via deep generative models is opening a new avenue for small molecule generation in silico. Linking chemical and biological space remains a key challenge. In this paper, we debut a graph-based variational autoencoder framework to address this challenge under the umbrella of disentangled representation learning. The framework permits several inductive biases that connect the learned latent factors to molecular properties. Evaluation on diverse benchmark datasets shows that the resulting models are powerful and open up an exciting line of research on controllable molecule generation in support of cheminformatics, drug discovery, and other application settings.