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

ABSTRACT

Introduction: 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.

KEYWORDS:

• Multidimensional-QSAR
• HQSAR
• binary QSAR
• MIA-QSAR
• multi-target QSAR

Article highlights

• Multidimensional quantitative structure-activity relationship modeling (multi-dimensional QSAR) is believed to be one of the most important computational methods employed in medicinal chemistry, material science, predictive toxicology and so on.

• Multi-dimensional QSAR could be classified based on various categories, however the model development process is usually consisted of three main parts: descriptor preparation, model processing and model application.

• Multi-dimensional QSAR could be developed and shown as different forms according to practical problems, for example, 3D QSAR might be the most widely used method, and multi-target QSAR is developed to predict properties on various targets simultaneously.

• With the growing development of multi-dimensional QSAR, novel methods have been proposed such as HQSAR, G-QSAR and MIA-QSAR and applied to more emerging field such as material science.

• Many factors might lead to the failure of a constructed model; however, the model performance could be further improved combined with other additional necessary information, such as the biological information or the structure-based information.This box summarizes key points contained in the article.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Additional information

Funding

The authors are supported by the National Natural Science Foundation of China (grant no. 21376215), the National High-Tech Research and Development Program of China (2012AA022302), the National Science and Technology Major Project of New Drug, China (2012ZX09103101-075), and the Science and Technology Project of Zhejiang Province (2014C33174).