| Full text | |
| Author(s): |
Maltarollo, Vinicius Goncalves
[1]
;
Kronenberger, Thales
[2]
;
Espinoza, Gabriel Zarzana
[3]
;
Oliveira, Patricia Rufino
[3]
;
Honorio, Kathia Maria
[3, 4]
Total Authors: 5
|
| Affiliation: | [1] Univ Fed Minas Gerais, Fac Farm, Dept Prod Farmaceut, Belo Horizonte, MG - Brazil
[2] Univ Hosp Tubingen, Dept Internal Med 8, Tubingen - Germany
[3] Univ Sao Paulo, Escola Artes Ciencias & Humanidades, BR-03828000 Sao Paulo, SP - Brazil
[4] Univ Fed ABC, Ctr Ciencias Nat & Humanas, Santo Andre - Brazil
Total Affiliations: 4
|
| Document type: | Review article |
| Source: | EXPERT OPINION ON DRUG DISCOVERY; v. 14, n. 1, p. 23-33, JAN 2 2019. |
| Web of Science Citations: | 1 |
| 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. (AU) | |
| FAPESP's process: | 16/18840-3 - Applying transfer learning techniques and ontologies to QSAR regression models |
| Grantee: | Patrícia Rufino Oliveira |
| Support Opportunities: | Research Grants - Research Partnership for Technological Innovation - PITE |