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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.
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Small molecule generation via disentangled representation learning
Abstract Motivation Expanding our knowledge of small molecules beyond what is known in nature or designed in wet laboratories promises to significantly advance cheminformatics, drug discovery, biotechnology and material science. In silico molecular design remains challenging, primarily due to the complexity of the chemical space and the non-trivial relationship between chemical structures and biological properties. Deep generative models that learn directly from data are intriguing, but they have yet to demonstrate interpretability in the learned representation, so we can learn more about the relationship between the chemical and biological space. In this article, we advance research on disentangled representation learning for small molecule generation. We build on recent work by us and others on deep graph generative frameworks, which capture atomic interactions via a graph-based representation of a small molecule. The methodological novelty is how we leverage the concept of disentanglement in the graph variational autoencoder framework both to generate biologically relevant small molecules and to enhance model interpretability. Results Extensive qualitative and quantitative experimental evaluation in comparison with state-of-the-art models demonstrate the superiority of our disentanglement framework. We believe this work is an important step to address key challenges in small molecule generation with deep generative frameworks. 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 Supplementary data are available at Bioinformatics online.
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- PAR ID:
- 10342764
- Editor(s):
- Xu, Jinbo
- Date Published:
- Journal Name:
- Bioinformatics
- Volume:
- 38
- Issue:
- 12
- ISSN:
- 1367-4803
- Page Range / eLocation ID:
- 3200 to 3208
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1093/bioinformatics/btac296
