Activity cliffs in drug discovery: Dr Jekyll or Mr Hyde?

• Type: Article in International Scientific Journal
• Date: 2014

Authors

Maykel Cruz Monteagudo (Author) Other; Jose L Medina Francos (Author) Other; Yunierkis Perez Castillo (Author) Other; Orazio Nicolotti (Author) Other; Natalia N D S Cordeiro (Author) FCUP; Fernanda Borges (Author) FCUP

Journal

Title: Drug Discovery Today

Vol. 19

Pages: 1069-1080

ISSN: 1359-6446

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 116 Citations

ISI Web of Science

Scopus - 125 Citations

Scientific classification

FOS: Medical and Health sciences > Basic medicine

CORDIS: Physical sciences > Chemistry

Other information

Authenticus ID: P-009-S5M

DOI: 10.1016/j.drudis.2014.02.003

Abstract (EN): The impact activity cliffs have on drug discovery is double-edged. For instance, whereas medicinal chemists can take advantage of regions in chemical space rich in activity cliffs, QSAR practitioners need to escape from such regions. The influence of activity cliffs in medicinal chemistry applications is extensively documented. However, the 'dark side' of activity cliffs (i.e. their detrimental effect on the development of predictive machine learning algorithms) has been understudied. Similarly, limited amounts of work have been devoted to propose potential solutions to the drawbacks of activity cliffs in similarity-based approaches. In this review, the duality of activity cliffs in medicinal chemistry and computational approaches is addressed, with emphasis on the rationale and potential solutions for handling the 'ugly face' of activity cliffs.

Language: English

Type (Professor's evaluation): Scientific