The advancement of multidimensional QSAR for novel drug discovery - where are we headed?

• Published in: Expert opinion on drug discovery Vol. 12; no. 8; p. 769
• Main Authors: Wang, Tao, Yuan, Xin-Song, Wu, Mian-Bin, Lin, Jian-Ping, Yang, Li-Rong
• Format: Journal Article
• Language: English
• Published: England 01.08.2017
• Subjects: Algorithms; Computer Simulation; Drug Design; Drug Discovery - methods; Humans; Models, Molecular; Pharmaceutical Preparations - chemistry; Quantitative Structure-Activity Relationship; Risk Assessment - methods; Multidimensional-QSAR; multi-target QSAR; binary QSAR; HQSAR; MIA-QSAR
• ISSN: 1746-045X
• DOI: 10.1080/17460441.2017.1336157

The Multidimensional quantitative structure-activity relationship (multidimensional-QSAR) method is one of the most popular computational methods employed to predict interesting biochemical properties of existing or hypothetical molecules. With continuous progress, the QSAR method has made remarkable success in various fields, such as medicinal chemistry, material science and predictive toxicology. Areas covered: In this review, the authors cover the basic elements of multidimensional -QSAR including model construction, validation and application. It includes and emphasizes the very recent developments of multidimensional -QSAR such as: HQSAR, G-QSAR, MIA-QSAR, multi-target QSAR. The advantages and disadvantages of each method are also discussed and typical examples of their application are detailed. Expert opinion: Although there are defects in multidimensional-QSAR modeling, it is still of enormous help to chemists, biologists and other researchers in various fields. In the authors' opinion, the latest more precise and feasible QSAR models should be further developed by integrating new descriptors, algorithms and other relevant computational techniques. Apart from being applied in traditional fields (e.g. lead optimization and predictive risk assessment), QSAR should be used more widely as a routine method in other emerging research fields including the modeling of nanoparticles(NPs), mixture toxicity and peptides.