Abstract
Pharmaceutical research has successfully incorporated a wealth of molecular modeling methods, within a variety of drug discovery programs, to study complex biological and chemical systems. The integration of computational and experimental strategies has been of great value in the identification and development of novel promising compounds. Broadly used in modern drug design, molecular docking methods explore the ligand conformations adopted within the binding sites of macromolecular targets. This approach also estimates the ligand-receptor binding free energy by evaluating critical phenomena involved in the intermolecular recognition process. Today, as a variety of docking algorithms are available, an understanding of the advantages and limitations of each method is of fundamental importance in the development of effective strategies and the generation of relevant results. The purpose of this review is to examine current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advances in the field and the role played by the integration of structure- and ligand-based methods.
Highlights
The research-based pharmaceutical industry has increasingly employed modern medicinal chemistry methods, including molecular modeling, as powerful tools for the study of structure-activity relationships (SAR) [1]
Prediction of the binding energy is performed by evaluating the most important physical-chemical phenomena involved in ligand-receptor binding, including intermolecular interactions, desolvation and entropic effects [55]
The computational cost increases in proportion to the number of variables included in the function, a shortcoming that reduces the productivity of the docking algorithm
Summary
The research-based pharmaceutical industry has increasingly employed modern medicinal chemistry methods, including molecular modeling, as powerful tools for the study of structure-activity relationships (SAR) [1]. The field has progressed hand-in-hand with advances in biomolecular spectroscopic methods such as X-ray crystallography and nuclear magnetic resonance (NMR), which have enabled striking progress in molecular and structural biology. These techniques have allowed the resolution of more than 100,000 three-dimensional protein structures, providing vital structural information about key macromolecular drug targets [3]. Efforts in storing, organizing and exploring such information have generated a growing demand for robust and sophisticated computational tools Based on this perspective, the accurate integration of in silico and experimental methods has provided the up-to-date understanding of the intricate aspects of intermolecular recognition [4]
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