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.
Background: The novel coronavirus SARS-CoV-2 has severely affected the health and economy of several countries. Multiple studies are in progress to design novel therapeutics against the potential target proteins in SARS-CoV-2, including 3CL protease, an essential protein for virus replication. Materials & methods: In this study we employed deep neural network-based generative and predictive models for de novo design of small molecules capable of inhibiting the 3CL protease. The generative model was optimized using transfer learning and reinforcement learning to focus around the chemical space corresponding to the protease inhibitors. Multiple physicochemical property filters and virtual screening score were used for the final screening. Conclusion: We have identified 33 potential compounds as ideal candidates for further synthesis and testing against SARS-CoV-2.
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.
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