Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

Abstract

: Today, the scientific community across the globe generates huge data; for example more than 74 million substances are registered in Chemical Abstract Services. Another estimate tells that there is about 1060 molecules classified as new drug like molecules. This huge space is now refereed as ‘dark chemical space’ or ‘dark chemistry’. Today one can see a surge in the number live databases (protein, cell, tissues, structure, drugs etc.) and every day these are updated with new information. So, the synchronization of the three different sciences ‘genomics’, proteomics’ and ‘in-silico simulation’ is essential and it will revolutionize the process of drug discovery. Now, the screening of the large number of drug like molecules is a challenge and it should be done in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process, however the drugs are need to be verified experimentally at every stage. Amongst, the various VS methods, quantitative structure–activity relationship (QSAR) analysis is the proven and accepted machine learning technique. QSAR is well-known for its high and fast throughput screening with good hit rate. The QSAR model building involves, (i) chemogenomics data collection from a database or literature, (ii) Calculation of right descriptors from molecular representation, (iii) establishing a relationship (model) between biological activity & the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. Therefore, it is desirable to test experimentally the hits obtained by VS. In this mini-review: different web-based machine learning tools, techniques of QSAR for VS, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of this powerful technique were highlighted.

A Comparison between Enrichment Optimization Algorithm (EOA)-Based and Docking-Based Virtual Screening

“…Several such studies were reported in the literature, and in most cases, the descriptors were calculated for the ligands in their unbound states [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. Some of these efforts were summarized in several review articles [ 29 , 30 ]. In other cases, ligand-based descriptors were combined with descriptors derived from the ligands’ 3D conformations as obtained from molecular docking [ 31 , 32 ] or with the docking scores themselves [ 33 ].…”

Photodegradation and In Silico Molecular Docking Study of a Diuretic Drug: Clopamide

“…As structural properties of molecules are linked with activity of drug, altered pharmacological properties , can be expected. This has stimulated researchers to unfold the pharmacological properties of drugs undergoing photodegradation as a consequence of the action of light on drugs, which may be important for correlating the results with the in vivo biological environment and to unveil the structural substratum to design new therapeutic agents . As a virtual screening study can be cost-effective and time-saving, in present study CPD ( 1 ) and its photoproducts 2 and 3 were analyzed for antioxidant potentials by screening their post-docking interactions, Gibbs free energy values, and H-bond interactions with tyrosinase.…”

Computational Methods in Cooperation with Experimental Approaches to Design Protein Tyrosine Phosphatase 1B Inhibitors in Type 2 Diabetes Drug Design: A Review of the Achievements of This Century

“…On the other hand, 3D-QSAR relies not only on chemical structures but also on 3D coordinates of atoms to correlate with biological activity and is divided into alignment-based and alignment-independent techniques [ 78 ]. The reader is referred to [ 79 , 80 , 81 ] for a comprehensive review.…”