DeLA-Drug: A Deep Learning Algorithm for Automated Design of Druglike Analogues

In
this paper, we present a deep learning algorithm for automated
design of druglike analogues (DeLA-Drug), a recurrent neural network
(RNN) model composed of two long short-term memory (LSTM) layers and
conceived for data-driven generation of similar-to-bioactive compounds.
DeLA-Drug captures the syntax of SMILES strings of more than 1 million
compounds belonging to the ChEMBL28 database and, by employing a new
strategy called sampling with substitutions (SWS), generates molecules
starting from a single user-defined query compound. Remarkably, the
algorithm preserves druglikeness and synthetic accessibility of the
known bioactive compounds present in the ChEMBL28 repository. The
absence of any time-demanding fine-tuning procedure enables DeLA-Drug
to perform a fast generation of focused libraries for further high-throughput
screening and makes it a suitable tool for performing de novo design even in low-data regimes. To provide a concrete idea of its
applicability, DeLA-Drug was applied to the cannabinoid receptor subtype
2 (CB2R), a known target involved in different pathological conditions
such as cancer and neurodegeneration. DeLA-Drug, available as a free
web platform (http://www.ba.ic.cnr.it/softwareic/deladrugportal/), can help medicinal chemists interested in generating analogues
of compounds already available in their laboratories and, for this
reason, good candidates for an easy and low-cost synthesis.