In silico generation of novel, drug-like chemical matter using the LSTM neural network
This work addresses the need for efficient molecule generation in cheminformatics to support drug discovery, though it is incremental as it applies an existing LSTM method to this domain.
The authors tackled the problem of generating novel, drug-like molecules for drug discovery by using an LSTM neural network, resulting in the generation of one million molecules in 2 hours that are novel, diverse, and have good physicochemical properties and synthetic accessibility, with virtual screening showing their bioactivity potential is comparable to the ChEMBL dataset.
The exploration of novel chemical spaces is one of the most important tasks of cheminformatics when supporting the drug discovery process. Properly designed and trained deep neural networks can provide a viable alternative to brute-force de novo approaches or various other machine-learning techniques for generating novel drug-like molecules. In this article we present a method to generate molecules using a long short-term memory (LSTM) neural network and provide an analysis of the results, including a virtual screening test. Using the network one million drug-like molecules were generated in 2 hours. The molecules are novel, diverse (contain numerous novel chemotypes), have good physicochemical properties and have good synthetic accessibility, even though these qualities were not specific constraints. Although novel, their structural features and functional groups remain closely within the drug-like space defined by the bioactive molecules from ChEMBL. Virtual screening using the profile QSAR approach confirms that the potential of these novel molecules to show bioactivity is comparable to the ChEMBL set from which they were derived. The molecule generator written in Python used in this study is available on request.