A combinatorial computational approach for drug discovery against AIDS: Machine learning and proteochemometrics

Sofia D'souza, Seetharaman Balaji

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Computational methods have been widely used in drug discovery including identification of novel targets, studying drug target interactions, and in virtual screening of compounds against known targets. Machine learning techniques have been used in predictions of novel targets and drugs with greater accuracy compared to other methods. Machine learning algorithms have also been widely used in predicting the progression of disease, resistance of a drug to a virus, treatment efficacy prediction, and also in predicting the effectiveness of combinational therapy with respect to HIV-1. In this article, we have focused on some of the machine learning techniques in the context of viral disease. In brief, machine learning methods have great potential in drug discovery, drug repurposing, and in precision medicine.

Original languageEnglish
Title of host publicationGlobal Virology III
Subtitle of host publicationVirology in the 21st Century
PublisherSpringer International Publishing AG
Pages345-359
Number of pages15
ISBN (Electronic)9783030290221
ISBN (Print)9783030290214
DOIs
Publication statusPublished - 01-01-2019

All Science Journal Classification (ASJC) codes

  • Medicine(all)
  • Immunology and Microbiology(all)
  • Neuroscience(all)

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    D'souza, S., & Balaji, S. (2019). A combinatorial computational approach for drug discovery against AIDS: Machine learning and proteochemometrics. In Global Virology III: Virology in the 21st Century (pp. 345-359). Springer International Publishing AG. https://doi.org/10.1007/978-3-030-29022-1_11