  Combination therapy has shown to improve therapeutic efficacy while reducing
side effects. Importantly, it has become an indispensable strategy to overcome
resistance in antibiotics, anti-microbials, and anti-cancer drugs. Facing
enormous chemical space and unclear design principles for small-molecule
combinations, the computational drug-combination design has not seen generative
models to meet its potential to accelerate resistance-overcoming drug
combination discovery. 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
(HVGAE) 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 a 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 drug combinations. Numerical results indicate that,
compared to graph embedding methods, HVGAE learns more informative and
generalizable disease representations. 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.
