Recent advances in quantitative structure–activity relationship models of antimalarial drugs

ABSTRACT

Introduction

Due to emerging resistance to the first-line artemisinin-based antimalarials and lack of efficient vaccines and limited chemotherapeutic alternatives, there is an urgent need to develop new antimalarial compounds. In this regard, quantitative structure–activity relationship (QSAR) modeling can provide essential information about required physicochemical properties and structural parameters of antimalarial drug candidates.

Areas covered

The authors provide an overview of recent advances of QSAR models covering different classes of antimalarial compounds as well as molecular docking studies of compounds acting on different antimalarial targets reported in the last 5 years (2015–2019) to explore the mode of interactions between the molecules and the receptors. We have tried to cover most of the QSAR models of antimalarials (along with results from some other related computational methods) reported during 2015–2019.

Expert opinion

Many QSAR reports for antimalarial compounds are based on small number of data points. This review infers that most of the present work deals with analog-based QSAR approach with a limited applicability domain (a very few cases with wide domain) whereas novel target-based computational approach is reported in very few cases, which leads to huge voids of computational work based on novel antimalarial targets.

KEYWORDS:

• Malaria
• qsar
• resistance
• validation
• double Cross-Validation
• virtual screening

Article Highlights

• Malaria is one of the life-threatening tropical diseases causing approximately 435 000 deaths globally.

• Emerging resistance to the first-line artemisinin-based antimalarials and lack of efficient vaccines and limited chemotherapeutic alternatives are major public health problems to the control of malaria.

• Computational approaches like quantitative structure–activity relationships (QSARs) studies may give insights to the medicinal chemists for understanding the relationship between molecular properties and antimalarial activity for designing and development of new potent analogs.

• Most of the recently reported QSARs are mainly analog-based QSARs developed from limited number of data points applicable to a particular class of compounds (a very few cases with wide domain).

• Target based QSAR models have been reported in very few cases.

• Combi-approaches, i.e., both ligand-based and target-based approaches may provide more accurate and reliable information to the medicinal chemists for antimalarial drug discovery.

• The ‘Exhaustive Double Cross-Validation’ tool is helpful for QSAR modeling small number of data points.

• Intelligent consensus predictor (ICP) tool can be used to improve the quality of predictions for the external test through ‘intelligent’ selection of multiple models.

This box summarizes key points contained in the article.

Declaration of interest

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.

Reviewer Disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

The authors have received financial assistance from the UGC, New Delhi in the form of a fellowship (Letter number and date: F./PDFSS-2015-17-WES-11996; dated: 06/04/2016) to PK Ojha. V Kumar thanks the Indian Council of Medical Research (ICMR), New Delhi for a Senior Research Fellowship. Financial assistance from the Department of Atomic Energy-Board of Research in Nuclear Sciences (DAE-BRNS) in form of a fellowship to J Roy is also thankfully acknowledged.