Abstract
In the last decades drug design and discovery changed dramatically, based on the one hand on previous knowledge of succesful drugs of medicinal chemistry, and on the other hand supported by the development of sophisticated and powerful new techniques such as molecular modelling, combinatorial chemistry, automated high-throughput screening, computer-aided designing, deep learning, etc. For a long time drug design and discovery revolved around a screening approach and trial-and-error methods.This methodology was time consuming, laborious and expensive. Pharmaceutical companies and researchers aimed to minimize the time and cost by introducing computer-aided simulation methods and other imaginative techniques. Computer-aided methods, also called in silico methodologies (like in vivo, in vitro), have created rapid advances and revolutionarized the way scientists search through millions of compounds in databases, choose suitable designs of drugs according to targeted protein molecules and promote promissing novel drugs. Target-based drug discovery has enabled a great expansion of chemotypes and pharmacophores available for research and manufacture. New drugs are designed by investigating biologically active compounds with pharmacokinetic, pharmacodynamic, toxicological, therapeutic and clinical parameters, biovailability, metabolic half-life and lack of side effects for prolonged clinical trials. This review contains information and scientific investigations on various in silico methodologies for the design and discovery of new drugs in the last decade: In silico methodologies: Virtual screening, Computational biology models of cellular behaviour, Homology modeling in 3D protein structure, Molecular docking approach, Virtual high-throughput screening, Quantitative structure-activity relationship methods (QSAR), Hologram Quantitative Strucutre Activity Relationship (HQSAR), Comparative molecular field analysis (CoMFA), Comparative similarity indices analyis (CoMSIA), 3D parmacophore mapping modeling (ligand-based and structure-based), Microarray analysis in drug design, Three-dimensional printing (3DP) of drugs and Deep learning in pharmaceutical research. These “in silico” methodologies, advanced the fields of Chemoinformatics and Bioinformatics with vast improvements in the last decades.
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