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PaccMannRL: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning


ABSTRACT: Summary With the advent of deep generative models in computational chemistry, in-silico drug design is undergoing an unprecedented transformation. Although deep learning approaches have shown potential in generating compounds with desired chemical properties, they disregard the cellular environment of target diseases. Bridging systems biology and drug design, we present a reinforcement learning method for de novo molecular design from gene expression profiles. We construct a hybrid Variational Autoencoder that tailors molecules to target-specific transcriptomic profiles, using an anticancer drug sensitivity prediction model (PaccMann) as reward function. Without incorporating information about anticancer drugs, the molecule generation is biased toward compounds with high predicted efficacy against cell lines or cancer types. The generation can be further refined by subsidiary constraints such as toxicity. Our cancer-type-specific candidate drugs are similar to cancer drugs in drug-likeness, synthesizability, and solubility and frequently exhibit the highest structural similarity to compounds with known efficacy against these cancer types. Graphical abstract Highlights • A conditional generative model for de novo design of anticancer hit molecules is devised• Drug sensitivity and toxicity models steer the molecule design via reinforcement learning• Molecules are designed to target individual transcriptomic profiles of cell lines• Targeted, hit-like molecules are generated more frequently, even for unseen cell lines• In silico, the molecules exhibit similar physicochemical properties to real cancer drugs Complex System Biology; Systems Biology; Transcriptomics; Computer Science

SUBMITTER: Born J 

PROVIDER: S-EPMC8022157 | biostudies-literature |

REPOSITORIES: biostudies-literature

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