Advances with support vector machines for novel drug discovery

ABSTRACT

Introduction: Novel drug discovery remains an enormous challenge, with various computer-aided drug design (CADD) approaches having been widely employed for this purpose. CADD, specifically the commonly used support vector machines (SVMs), can employ machine learning techniques. SVMs and their variations offer numerous drug discovery applications, which range from the classification of substances (as active or inactive) to the construction of regression models and the ranking/virtual screening of databased compounds.

Areas covered: Herein, the authors consider some of the applications of SVMs in medicinal chemistry, illustrating their main advantages and disadvantages, as well as trends in their utilization, via the available published literature. The aim of this review is to provide an up-to-date review of the recent applications of SVMs in drug discovery as described by the literature, thereby highlighting their strengths, weaknesses, and future challenges.

Expert opinion: Techniques based on SVMs are considered as powerful approaches in early drug discovery. The ability of SVMs to classify active or inactive compounds has enabled the prioritization of substances for virtual screening. Indeed, one of the main advantages of SVMs is related to their potential in the analysis of nonlinear problems. However, despite successes in employing SVMs, the challenges of improving accuracy remain.

KEYWORDS:

• Drug discovery
• medicinal chemistry
• machine learning
• support vector machines
• QSAR
• classification
• virtual screening

Article Highlights

• Despite progression in terms of the techniques used, including that of CADD, novel drug discovery remains a challenge.

• SVMs are one of the most used machine learning techniques in drug design and discovery studies.

• SVMs can be employed to classify different varieties of compounds, to predict the biological activity of new molecules (from regression models) and to rank substances in virtual screening assays.

• There are several crucial challenges involved in SVM approaches, such as the choice of kernel functions and optimum parameters for specific problems.

• The integration of SVMs with other approaches has proven to be an excellent strategy for medicinal chemistry studies.

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 were supported by the Universidade Federal de Minas Gerais, the Fundação de Amparo à Pesquisa do Estado de São Paulo (grant number 2016/18840-3), the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior and the Conselho Nacional de Desenvolvimento Científico e Tecnológico of the Ministério da Ciência, Tecnologia e Inovação, Brazil.