A Concise Review on the Significance of QSAR in Drug Design

Abstract

Drug designing is a crucial step in the exploration of novel drugs which requires potent methodologies. One of such methodologies is Quantitative Structure Activity Relationship (QSAR) which is a widely used statistical tool that correlates the structure of a molecule to a biological activity as a function of molecular descriptors, thereby, playing an essential role in the drug designing. QSAR utilizes Density Functional Theory (DFT) based chemical descriptors for this purpose. The selection of such significant molecular descriptors from various available descriptors is the foremost challenge in a QSAR as structural descriptors are representative of the molecular characteristics accountable for the relevant activity. Recently, new QSAR approaches have been introduced which further enhance the study of the activities. Further, the constructed QSAR models also need to be tested and validated for their efficiency and practical usage. As the QSAR models are structure specific, they may not be universally applicable. However, because of their high precision and efficacy, they have a promising future in the world of drug design. This review briefly summarizes the role of descriptor based QSAR in drug design in conjunction with existing QSAR approaches and also the utility as well as constraints of this approach in drug design.

In silico prediction of boiling point, octanol–water partition coefficient, and retention time index of polycyclic aromatic hydrocarbons through machine learning

“…Three hundred molecular descriptors, including thermodynamics, connectivity, geometry, topology, electronics, and quantum mechanics (Khan, 2016; Golbraikh et al, 2014; Tandon et al, 2019; Myint & Xie, 2010), were used to establish the QSPR model. These descriptors were randomly divided into 30 groups, and each group contains 10 descriptors.…”

“…An optimization strategy of combining molecular descriptor and molecular fingerprint was employed in this work, which can improve the prediction performance (Ramdani et al, 2013). Three hundred molecular descriptors, including thermodynamics, connectivity, geometry, topology, electronics, and quantum mechanics (Khan, 2016;Golbraikh et al, 2014;Tandon et al, 2019;Myint & Xie, 2010), were used to establish the QSPR model. These descriptors were randomly divided into 30 groups, and each group contains 10 descriptors.…”

Quantitative structure–activity relationship-based computational approaches

“…By the by, there is no particular medication that has been accounted for in light of the fact that the creation of an effectual and solid medication requires quite a while of examination and clinical preliminaries. Subsequently, drug repositioning has been a methodology embraced by a majority of specialists worldwide to look for viable treatment in a brief timeframe ( Tandon et al, 2019 ) ( Fig. 10.7 ).…”

In silico exploration of phenolics as modulators of penicillin binding protein (PBP) 2× of Streptococcus pneumoniae

“…The limitations of docking techniques relating to lack of energy refinement and assessment of stability were addressed using molecular dynamic (MD) simulation and mechanics/GB surface area (MMGBSA) approaches. Due to the effectiveness of these techniques in the identification of leads from a library of compounds, coupled with the precision of density functional theory in studying the geometries and molecular properties of drug molecules 21 , these computational techniques have become an important tool in drug discovery and development. Thus, in this study, they have been employed in the identification of potentially active antibacterial candidates that can treat or act in synergy with conventional antibiotics in the treatment of infections caused by S. pneumoniae .…”