Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?

ABSTRACT

Introduction: Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery.

Areas covered: In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field.

Expert opinion: The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.

KEYWORDS:

• Artificial neural networks
• QSAR
• drug discovery

Article highlights

• Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms

• ANNs are useful techniques as implemented into QSAR and virtual screening to improve the drug discovery process

• ANNs have been successfully used in a broad spectrum of applications covering many diseases

• ANNs have gained increased popularity among the researchers

• ANNs have met many of their expectations set in the past.

• However, old pitfalls as overtraining and interpretability are still not completely solved

• ANNs will continue to be used in the future as tools in QSAR for drug discovery

This box summarizes key points contained in the article.

Declaration of interest

The authors were supported by Estonian Research Council grants PUT95 and PUT582 and the European Regional Development Fund through the Centre of Excellence in Chemical Biology, Estonia. The authors have no other 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 apart from those disclosed.