CIQ, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal; REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal; Centro de Estudios de Química Aplicada (CEQA), Faculty of Chemistry and Pharmacy, Central University of Las Villas, Santa Clara 54830, Cuba; Molecular Simulation and Drug Design Group, Centro de Bioactivos Químicos (CBQ), Central University of Las Villas, Santa Clara 54830, Cuba.
Mayo Clinic, 13400 East Shea Boulevard, Scottsdale, AZ 85259, USA.
Drug Discov Today. 2014 Aug;19(8):1069-80. doi: 10.1016/j.drudis.2014.02.003. Epub 2014 Feb 20.
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.
活性悬崖对药物发现的影响是一把双刃剑。例如，虽然药物化学家可以利用活性悬崖丰富的化学空间区域，但定量构效关系（QSAR）从业者则需要避开这些区域。活性悬崖在药物化学应用中的影响已得到广泛证明。然而，活性悬崖的“阴暗面”（即它们对开发预测性机器学习算法的不利影响）尚未得到充分研究。同样，在基于相似性的方法中，针对活性悬崖的缺点提出潜在解决方案的工作也很少。在这篇综述中，我们探讨了药物化学和计算方法中活性悬崖的双重性，重点讨论了处理活性悬崖“丑陋一面”的基本原理和潜在解决方案。
