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Seen as a problem of guided exploration with multiple objectives, molecular design requires carefully elaborated decision-tree-based strategies for traversing vast chemical space. Among main challenges, the discovery of bioactive molecules with either selective or polypharmacological inhibitory profiles remains a central one. In this work, we present a computational framework that integrates Monte Carlo tree search with predictive machine learning models and expert-defined rules to navigate
Druchok et al. (Tue,) studied this question.