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

Understand

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.