In recent years, computer-aided drug design (CADD) has become an integral part of the development of novel therapeutics because it is able to mitigate the reliance on expensive and time-consuming experimental procedures. The mechanism of many effective therapeutics relies on the targeted inhibition of individual proteins, and CADD has played an important role in discovering and designing these inhibitors. In this work, we propose a three-stage pipeline for the development of novel protein-specific inhibitors. In the first stage, a database as large as one million compounds is fed into the pipeline and high-throughput virtual screening (HTVS) is performed. Select candidates identified in screening are treated in the next stage as a scaffold, on which functional groups are attached in order to improve binding affinity to a desired protein. Through evolutionary functionalization of the small molecule scaffold, we seek to optimize protein-ligand binding affinity while maintaining synthesizability and mitigating potential toxicity. The most promising novel structures generated during optimization pass on to a third and final stage where rigorous binding affinity determination using the MM/GBSA method narrows down the pool of lead compounds to a select few. As a test case, we have utilized our pipeline to design small molecule inhibitors for several nonstructural proteins encoded by HEV ORF1 (hepatitis E virus open reading frame 1). Starting with a database of one million compounds, many novel molecular structures were generated and several top candidates were validated through molecular dynamics simulation.

Fully automated drug design pipeline for the development of small molecule protein inhibitors

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