Skip to Main content Skip to Navigation

Machine learning approaches for drug virtual screening

Abstract : The rational drug discovery process has limited success despite all the advances in understanding diseases, and technological breakthroughs. Indeed, the process of drug development is currently estimated to require about 1.8 billion US dollars over about 13 years on average. Computational approaches are promising ways to facilitate the tedious task of drug discovery. We focus in this thesis on statistical approaches which virtually screen a large set of compounds against a large set of proteins, which can help to identify drug candidates for known therapeutic targets, anticipate potential side effects or to suggest new therapeutic indications of known drugs. This thesis is conceived following two lines of approaches to perform drug virtual screening : data-blinded feature-based approaches (in which molecules and proteins are numerically described based on experts' knowledge), and data-driven feature-based approaches (in which compounds and proteins numerical descriptors are learned automatically from the chemical graph and the protein sequence). We discuss these approaches, and also propose applications of virtual screening to guide the drug discovery process.
Document type :
Complete list of metadata

Cited literature [503 references]  Display  Hide  Download
Contributor : ABES STAR :  Contact
Submitted on : Wednesday, July 17, 2019 - 2:43:08 PM
Last modification on : Wednesday, November 17, 2021 - 12:31:07 PM


Version validated by the jury (STAR)


  • HAL Id : tel-02186833, version 1


Benoit Playe. Machine learning approaches for drug virtual screening. Bioinformatics [q-bio.QM]. Université Paris sciences et lettres, 2019. English. ⟨NNT : 2019PSLEM010⟩. ⟨tel-02186833⟩



Record views


Files downloads