Skip to main content
padlock icon - secure page this page is secure

Exploring Chemical Space with Machine Learning

Buy Article:

$25.00 + tax (Refund Policy)

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.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: CHEMICAL SPACE; DATA VISUALIZATION; DEEP LEARNING; MOLECULAR DATABASES; POLYPHARMACOLOGY

Document Type: Research Article

Affiliations: 1: Department of Chemistry and Biochemistry, National Center for Competence in Research NCCR TransCure, University of Bern, Freiestrasse 3, CH-3012 Bern 2: Department of Chemistry and Biochemistry, National Center for Competence in Research NCCR TransCure, University of Bern, Freiestrasse 3, CH-3012 Bern;, Email: [email protected]

Publication date: December 1, 2019

More about this publication?
  • International Journal for Chemistry and Official Membership Journal of the Swiss Chemical Society (SCS) and its Divisions

    CHIMIA, a scientific journal for chemistry in the broadest sense, is published 10 times a year and covers the interests of a wide and diverse readership. Contributions from all fields of chemistry and related areas are considered for publication in the form of Review Articles and Notes. A characteristic feature of CHIMIA are the thematic issues, each devoted to an area of great current significance.

  • Editorial Board
  • Information for Authors
  • Subscribe to this Title
  • Information for Advertisers
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more