posted on 2021-11-18, 17:04authored bySowmya
Ramaswamy Krishnan, Navneet Bung, Sarveswara Rao Vangala, Rajgopal Srinivasan, Gopalakrishnan Bulusu, Arijit Roy
In recent years, deep learning-based
methods have emerged as promising
tools for de novo drug design. Most of these methods
are ligand-based, where an initial target-specific ligand data set
is necessary to design potent molecules with optimized properties.
Although there have been attempts to develop alternative ways to design
target-specific ligand data sets, availability of such data sets remains
a challenge while designing molecules against novel target proteins.
In this work, we propose a deep learning-based method, where the knowledge
of the active site structure of the target protein is sufficient to
design new molecules. First, a graph attention model was used to learn
the structure and features of the amino acids in the active site of
proteins that are experimentally known to form protein–ligand
complexes. Next, the learned active site features were used along
with a pretrained generative model for conditional generation of new
molecules. A bioactivity prediction model was then used in a reinforcement
learning framework to optimize the conditional generative model. We
validated our method against two well-studied proteins, Janus kinase
2 (JAK2) and dopamine receptor D2 (DRD2), where we produce molecules
similar to the known inhibitors. The graph attention model could identify
the probable key active site residues, which influenced the conditional
molecule generator to design new molecules with pharmacophoric features
similar to the known inhibitors.