Structure Based Drug Designing Approaches in SARS-CoV-2 Spike Inhibitor Design.

The COVID-19 outbreak and the pandemic situation have hastened the research community to design a novel drug and vaccine against its causative organism, the SARS-CoV-2. The spike glycoprotein present on the surface of this pathogenic organism plays an immense role in viral entry and antigenicity. Hence, it is considered an important drug target in COVID-19 drug design. Several three-dimensional crystal structures of this SARS-CoV-2 spike protein have been identified and deposited in the Protein DataBank during the pandemic period. This accelerated the research in computer-aided drug designing, especially in the field of structure-based drug designing. This review summarizes various structure-based drug design approaches applied to this SARS-CoV-2 spike protein and its findings. Specifically, it is focused on different structure-based approaches such as molecular docking, high-throughput virtual screening, molecular dynamics simulation, drug repurposing, and target-based pharmacophore modelling and screening. These structural approaches have been applied to different ligands and datasets such as FDA-approved drugs, small molecular chemical compounds, chemical libraries, chemical databases, structural analogs, and natural compounds, which resulted in the prediction of spike inhibitors, spike-ACE-2 interface inhibitors, and allosteric inhibitors.

Methods and applications of machine learning in structure-based drug discovery

Structure-based drug discovery (SBDD) utilizes the three-dimensional (3D) structure of a target protein to identify the lead compounds. This medium is then considered a viable solution based on its availability and correlation with a particular disease. In the case of pandemics like COVID 19, shortening drug development time can save millions of people worldwide;for such a task, classical drug discovery methods will take a long time. Hence, researchers worldwide actively incorporated machine learning (ML) into the drug discovery process, particularly in SBDD, to minimize the lead optimization time. ML uses statistical methods to make a computer perform tasks, take a critical decision, and automate this entire process without being explicitly programmed. With this, the computer can discover new insights about data and unknown patterns crucial to decide the therapeutic use of lead compounds as drugs. The use of ML in the drug discovery field is not new, and it spans an ample research space. By integrating artificial intelligence with ML techniques, viable targets can be found using data clustering, regression, and classification from vast omics databases and sources. In this chapter, we will discuss the methods and applications of ML in SBDD. © 2022 Elsevier Inc. All rights reserved.