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In silico generation of novel, drug-like chemical matter using the LSTM neural network
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.Comment: in this version fixed some reference number
Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks
In de novo drug design, computational strategies are used to generate novel
molecules with good affinity to the desired biological target. In this work, we
show that recurrent neural networks can be trained as generative models for
molecular structures, similar to statistical language models in natural
language processing. We demonstrate that the properties of the generated
molecules correlate very well with the properties of the molecules used to
train the model. In order to enrich libraries with molecules active towards a
given biological target, we propose to fine-tune the model with small sets of
molecules, which are known to be active against that target.
Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test
molecules that medicinal chemists designed, whereas against Plasmodium
falciparum (Malaria) it reproduced 28% of 1240 test molecules. When coupled
with a scoring function, our model can perform the complete de novo drug design
cycle to generate large sets of novel molecules for drug discovery.Comment: 17 pages, 17 figure
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