New therapeutic entities are identified through the process of drug discovery and development with a goal to improve life’s quality and to reduce suffering to a minimum. Despite advances in biotechnology, it is very complex, time-consuming, and resource-intensive. Over the past decades, technology in the healthcare industry has rapidly changed. Researchers are applying computational abilities for simplifying drug discovery and designing process. The theory behind the drug design involves the designing of molecules based on the knowledge of the biological targets. It explains the relationship between biological activity and structure. Drug design can modify the drug molecule according to the need and can be categorized into two groups-
- Structure-based drug design
- Ligand-based drug design
Drug design is an inventive process to explore novel drugs that require potent methodologies such as QSAR (Quantitative Structure-Activity Relationship). QSAR plays a vital role in drug designing as it identifies new inhibitors de novo and optimizes various properties of identified molecules from various sources. It predicts and classifies the biological activities of untested compounds. The role of computational technology in drug discovery has simplified the utilization of in silico methods in the designing process. QSAR comes under ligand-based drug design used for finding relationships between chemical structures and biological activity. It not only gives high and fast throughput but also has a good hit rate. QSAR developmental process includes-
- Preparing models for experiment
- Data analysis
- Internal and external validation
QSAR is the cost-effective alternative for medium-throughput in vitro and low throughput in vivo assays to obtain a reliable statistical model for the prediction of the activities of new chemical entities. It establishes a mathematical relationship between biological activity and measurable physicochemical parameters. QSAR works on the principle that variations in structural properties cause different biological activities. Machine learning approaches including linear regression models, Bayesian neural networks, and random forest have been applied to QSAR prediction.
Some of the outstanding applications of QSAR-based virtual screening were used in the cases of malaria, schistosomiasis, tuberculosis, viral infections, mood, and anxiety disorders for the discovery of new hits and hit-to-lead optimization. QSAR methodologies can be classified based on the dimensionality and the methods used such as multiple linear regression, partial least-squares, and artificial neural network. The main goal of QSAR is to reduce trial and error synthesis of drugs leading to the development of the best drug model. It promotes the synthesis of the more potent drugs for diseases and also enhances the importance and role of greener chemistry.
QSAR leads to the synthesis of novel analogs as it provides a deeper understanding of the effect of changes in structural properties on biological activity. But there are some limitations associated with it such as the arising of false correlation and lack of consideration of the 3D structure of the molecules. Therefore, 3D-QSAR is an alternative for classical QSAR which has emerged as a natural extension for considering the ligand properties calculated in its bioactive conformation. Recent reports have suggested that these advanced methodologies have been applied to study the interaction of nanomaterials with their biological targets.
SNI Publications invites you to share your knowledge and recent advancement on QSAR by publishing their work with the Journal of Molecular Biology and Drug Design also a small piece of information that can be shared here itself in the comment section.