Roles of Virtual Screening and Molecular Dynamics Simulations in Discovering and Understanding Antimalarial Drugs

Abstract

Malaria continues to be a global health threat, with approximately 247 million cases worldwide. Despite therapeutic interventions being available, patient compliance is a problem due to the length of treatment. Moreover, drug-resistant strains have emerged over the years, necessitating urgent identification of novel and more potent treatments. Given that traditional drug discovery often requires a great deal of time and resources, most drug discovery efforts now use computational methods. In silico techniques such as quantitative structure-activity relationship (QSAR), docking, and molecular dynamics (MD) can be used to study protein-ligand interactions and determine the potency and safety profile of a set of candidate compounds to help prioritize those tested using assays and animal models. This paper provides an overview of antimalarial drug discovery and the application of computational methods in identifying candidate inhibitors and elucidating their potential mechanisms of action. We conclude with the continued challenges and future perspectives in the field of antimalarial drug discovery.

Active and machine learning-enhanced discovery of new FGFR3 inhibitor, Rhapontin, through virtual screening of receptor structures and anti-cancer activity assessment

“…The present study presents a systematic exploration aimed at augmenting the efficacy of Ceritinib, a prominent FGFR3 inhibitor, via the integration of natural compound-derived alternatives. Our investigation embraces a multidimensional approach, employing active learning derived virtual screen ( Ma et al, 2009 ), deep learning derived QSAR modeling ( Matsuzaka and Uesawa, 2023 ), molecular dynamics simulations ( Duay et al, 2023 ), and biological assays to dissect the mechanisms underlying the potential synergy between Ceritinib and the identified natural compounds.…”

Structure-based virtual screening and molecular dynamics studies to explore potential natural inhibitors against 3C protease of foot-and-mouth disease virus

“…In recent years, the integration of structure-based virtual screening (SBVS), MD simulations, and density functional theory (DFT) analysis enhances the drug discovery process by providing a multifaceted understanding of the molecular interactions between drugs and their targets. This integrated approach enables more informed decision-making in the identification, optimization, and design of novel drug candidates (17)(18)(19)(20)(21). Hence, in the current study, we explored potential natural compounds against FMDV 3Cpro through structure-based virtual screening.…”

MD–Ligand–Receptor: A High-Performance Computing Tool for Characterizing Ligand–Receptor Binding Interactions in Molecular Dynamics Trajectories

“…In this context, methods such as molecular docking and molecular dynamics, together with innovative machine learning techniques, are revolutionizing the approaches to the issue [2,3]. Molecular docking is a widely used tool for predicting and studying protein-ligand interactions [4][5][6]. This approach is also used as a starting point for the molecular dynamics simulation of the protein-ligand complex and the calculation of binding energy, providing valuable information about the structure of the complex and the strength of the interaction [7].…”