Deciphering the scope of in silico screening of novel natural lead molecules against putative molecular targets of multidrug-resistant bacterial pathogens

Antibacterial resistance is one of the major healthcare concerns that contributed to substantial mortality and morbidity recently. Most of the last-line resort antibacterial agents including the families of colistin, carbapenems, and high-generation cephalosporins became resistant to several Gram-negative bacterial pathogens. This situation demands the screening of novel therapeutic agents that can be used as alternatives to the present generation of antibacterial agents. Computational medicinal chemistry integrated with several in silico tools is one of the promising approaches to screen novel lead molecules. The molecules from natural sources are one of the promising lead candidates that offer ideal drug likeliness, pharmacokinetics properties such as adsorption, distribution, metabolism, excretion, and toxicity, and demonstrated binding potential to several bacterial drug targets. This chapter comprehensively elucidates the recent aspects of screening natural lead molecules and prediction of their binding potential toward various molecular targets of bacterial pathogens by computational biology tools and resources. The chapter also highlights the latest tools and computational resources used for the selection and screening of natural lead molecules by molecular modeling, molecular docking, and dynamic simulation studies. The concepts of the chapter provide an eye-opener for the scope of computational biology and bioinformatics resources in structure-based drug discovery and computational medicinal chemistry.