posted on 2021-01-25, 14:57authored bySowmya
Ramaswamy Krishnan, Navneet Bung, Gopalakrishnan Bulusu, Arijit Roy
In the world plagued by the emergence
of new diseases, it is essential
that we accelerate the drug design process to develop new therapeutics
against them. In recent years, deep learning-based methods have shown
some success in ligand-based drug design. Yet, these methods face
the problem of data scarcity while designing drugs against a novel
target. In this work, the potential of deep learning and molecular
modeling approaches was leveraged to develop a drug design pipeline,
which can be useful for cases where there is limited or no availability
of target-specific ligand datasets. Inhibitors of the homologues of
the target protein were screened at the active site of the target
protein to create an initial target-specific dataset. Transfer learning
was used to learn the features of the target-specific dataset. A deep
predictive model was utilized to predict the docking scores of newly
designed molecules. Both these models were combined using reinforcement
learning to design new chemical entities with an optimized docking
score. The pipeline was validated by designing inhibitors against
the human JAK2 protein, where none of the existing JAK2 inhibitors
were used for training. The ability of the method to reproduce existing
molecules from the validation dataset and design molecules with better
binding energy demonstrates the potential of the proposed approach.