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The exploration of novel chemical spaces is one of the most important tasksof cheminformatics when supporting the drug discovery process. Properlydesigned and trained deep neural networks can provide a viable alternative tobrute-force de novo approaches or various other machine-learning techniques forgenerating novel drug-like molecules. In this article we present a method togenerate molecules using a long short-term memory (LSTM) neural network andprovide an analysis of the results, including a virtual screening test. Usingthe network one million drug-like molecules were generated in 2 hours. Themolecules are novel, diverse (contain numerous novel chemotypes), have goodphysicochemical properties and have good synthetic accessibility, even thoughthese qualities were not specific constraints. Although novel, their structuralfeatures and functional groups remain closely within the drug-like spacedefined by the bioactive molecules from ChEMBL. Virtual screening using theprofile QSAR approach confirms that the potential of these novel molecules toshow bioactivity is comparable to the ChEMBL set from which they were derived.The molecule generator written in Python used in this study is available onrequest.

In silico generation of novel, drug-like chemical matter using the LSTM neural network