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

被引:117
作者
Cruz-Monteagudo, Maykel [1 ,2 ,3 ,4 ]
Medina-Francos, Jose L. [5 ]
Perez-Castillo, Yunierkis [4 ]
Nicolotti, Orazio [6 ]
Cordeiro, M. Natalia D. S. [2 ]
Borges, Fernanda [1 ]
机构
[1] Univ Porto, Dept Chem & Biochem, CIQ, Fac Sci, P-4169007 Oporto, Portugal
[2] Univ Porto, Dept Chem & Biochem, REQUIMTE, Fac Sci, P-4169007 Oporto, Portugal
[3] Cent Univ Las Villas, Fac Chem & Pharm, Ctr Estudios Quim Aplicada, Santa Clara 54830, Cuba
[4] Cent Univ Las Villas, Mol Simulat & Drug Design Grp, Ctr Bioact Quim, Santa Clara 54830, Cuba
[5] Mayo Clin, Scottsdale, AZ 85259 USA
[6] Univ Bari Aldo Moro, Dipartimento Farm Sci Farm, I-70125 Bari, Italy
关键词
ACTIVITY LANDSCAPES; SYSTEMATIC IDENTIFICATION; RELATIONSHIP INFORMATION; COMPUTATIONAL ANALYSIS; ACTIVITY RIDGES; PPAR LIGANDS; QSAR; REPRESENTATIONS; CLASSIFICATION; PROGRESSION;
D O I
10.1016/j.drudis.2014.02.003
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
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.
引用
收藏
页码:1069 / 1080
页数:12
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