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Title: Interpretable Molecule Generation via Disentanglement Learning
Designing molecules with specific structural and functional properties (e.g., drug-likeness and water solubility) is central to advancing drug discovery and material science, but it poses outstanding challenges both in wet and dry laboratories. The search space is vast and rugged. Recent advances in deep generative models are motivating new computational approaches building over deep learning to tackle the molecular space. Despite rapid advancements, state-of-the-art deep generative models for molecule generation have many limitations, including lack of interpretability. In this paper we address this limitation by proposing a generic framework for interpretable molecule generation based on novel disentangled deep graph generative models with property control. Specifically, we propose a disentanglement enhancement strategy for graphs. We also propose new deep neural architecture to achieve the above learning objective for inference and generation for variable-size graphs efficiently. Extensive experimental evaluation demonstrates the superiority of our approach in various critical aspects, such as accuracy, novelty, and disentanglement.  more » « less
Award ID(s):
1841520
PAR ID:
10318802
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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     Availability and implementation

    Training and generated data are made available at https://ieee-dataport.org/documents/dataset-disentangled-representation-learning-interpretable-molecule-generation. All code is made available at https://anonymous.4open.science/r/D-MolVAE-2799/.

     Supplementary information

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