Exploring Chemical Space with Machine Learning

Arús-Pous, Josep; Awale, Mahendra; Probst, Daniel; Reymond, Jean-Louis (2019). Exploring Chemical Space with Machine Learning. CHIMIA, 73(12), pp. 1018-1023. Schweizerische Chemische Gesellschaft 10.2533/chimia.2019.1018

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Chemical space is a concept to organize molecular diversity by postulating that different molecules occupy different regions of a mathematical space where the position of each molecule is defined by its properties. Our aim is to develop methods to explicitly explore chemical space in the area of drug discovery. Here we review our implementations of machine learning in this project, including our use of deep neural networks to enumerate the GDB13 database from a small sample set, to generate analogs of drugs and natural products after training with fragment-size molecules, and to predict the polypharmacology of molecules after training with known bioactive compounds from ChEMBL. We also discuss visualization methods for big data as means to keep track and learn from machine learning results. Computational tools discussed in this review are freely available at http://gdb.unibe.ch and https://github.com/reymond-group.

Item Type:

Journal Article (Original Article)

Division/Institute:

08 Faculty of Science > Departement of Chemistry and Biochemistry

UniBE Contributor:

Arús Pous, Josep; Awale, Mahendra; Probst, Daniel and Reymond, Jean-Louis

Subjects:

500 Science > 570 Life sciences; biology
500 Science > 540 Chemistry

ISSN:

0009-4293

Publisher:

Schweizerische Chemische Gesellschaft

Language:

English

Submitter:

Sandra Tanja Zbinden Di Biase

Date Deposited:

06 Apr 2020 15:43

Last Modified:

06 Apr 2020 15:51

Publisher DOI:

10.2533/chimia.2019.1018

PubMed ID:

31883554

BORIS DOI:

10.7892/boris.141738

URI:

https://boris.unibe.ch/id/eprint/141738

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