A scalable derivative-free optimizer trained through learning to optimize approach to interpret drug response mechanism network

With the emergence of various drug-based treatment strategies, many drug response prediction models have been developed to understand their effects. However, in order to gain comprehensive understanding of a drug response, a prediction model should reflect the underlying biological mechanisms, but the current models suffer from interpretability and scalability problems. Machine learning-based prediction models base their predictions on inferred features, which usually are not well correlated with biological mechanisms, posing a challenge on interpretability of its response predictions. In this regard, using Boolean modeling schemes may allow interpretations on mechanisms that contribute to a particular response, but optimizing Boolean models is difficult because of their high dimensional search space and discontinuous loss function. Here, we developed a scalable derivative-free optimizer for weighted sum Boolean network through meta-reinforcement learning. By using graph network and coordinate-wise policy, our learned optimizer can optimize high dimensional Boolean networks containing over 100 parameters of arbitrary structure, showing higher sample efficiency compared with other meta-heuristic algorithms. The optimized Boolean networks successfully predict the drug responses congruent with public databases and in-house experimental data. Moreover, mechanistic analysis of optimized networks shows reliable interpretability of the predictions by meaningful suggestions of known basket trial drug response prediction markers. RNA-seq of SW48, SW620 colorectal cancer cell lines in the presence or absence of Trametinib