Network-principled deep generative models for designing drug combinations as graph sets

Abstract

Motivation Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable strategy to overcome resistance in antibiotics, antimicrobials and anticancer drugs. Facing enormous chemical space and unclear design principles for small-molecule combinations, computational drug-combination design has not seen generative models to meet its potential to accelerate resistance-overcoming drug combination discovery. Results We have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively training a reinforcement learning-based chemical graph-set designer. First, we have developed hierarchical variational graph auto-encoders trained end-to-end to jointly embed gene–gene, gene–disease and disease–disease networks. Novel attentional pooling is introduced here for learning disease representations from associated genes’ representations. Second, targeting diseases in learned representations, we have recast the drug-combination design problem as graph-set generation and developed a deep learning-based model with novel rewards. Specifically, besides chemical validity rewards, we have introduced novel generative adversarial award, being generalized sliced Wasserstein, for chemically diverse molecules with distributions similar to known drugs. We have also designed a network principle-based reward for disease-specific drug combinations. Numerical results indicate that, compared to state-of-the-art graph embedding methods, hierarchical variational graph auto-encoder learns more informative and generalizable disease representations. Results also show that the deep generative models generate drug combinations following the principle across diseases. Case studies on four diseases show that network-principled drug combinations tend to have low toxicity. The generated drug combinations collectively cover the disease module similar to FDA-approved drug combinations and could potentially suggest novel systems pharmacology strategies. Our method allows for examining and following network-based principle or hypothesis to efficiently generate disease-specific drug combinations in a vast chemical combinatorial space. Availability and implementation https://github.com/Shen-Lab/Drug-Combo-Generator. Supplementary information Supplementary data are available at Bioinformatics online.

Generative Deep Learning for Targeted Compound Design

“…Reporting that it can be used to target specific ranges of log P and molecular weight and also optimize log P penalized by SA and large rings, while constrained by similarity to a starting molecule. Karimi et al 92 employed a similar molecular generative process to generate new drug combinations. Specifically, the proposed method aimed to directly generate sets of novel molecules that could be useful as disease-specific drug combinations.…”

Intelligent Health Care: Applications of Deep Learning in Computational Medicine

“…The model had high speed and high accuracy, which was suitable for screening thousands of drugs in a short time in some emergencies. Karimi et al (2020) developed the deep generation model for drug combination design. They used hierarchical variational graph autoencoders to jointly embed gene-gene, gene-disease, and disease-disease networks.…”

Deep Graph Generators: A Survey

“…Figure 11 provides an overview of this framework. Karimi et al [45] propose another extension to GCPN for drug-combination design, which is a key part of combination therapy. To this end, the authors first develop Hierarchical Variational Graph Auto-Encoders (HVGAE) to embed prior knowledge such as gene-gene, gene-disease, and disease-disease networks to acquire more accurate disease representations.…”