As the number of compounds and the volume of bioactivity data rapidly grow, advanced computational methods are required to study structure–activity relationships (SARs) on a large scale. Herein, the SAR matrix (SARM) methodology is described that was designed to systematically extract structural relationships between bioactive compounds from large databases, explore structure–activity relationships, and navigate multitarget activity spaces, which is one of the core tasks in chemogenomics. In addition, the SARM approach was designed to visualize structural and structure–activity relationships, which is often of critical importance for making this information available in an intuitive form for practical applications.
Bioactive compounds Structure–activity relationships (SARs) Multitarget activities Large-scale SAR analysis SAR visualization ChEMBL SAR matrix data structure
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Hu Y, Bajorath J (2014) Learning from ‘big data’: compounds and targets. Drug Discov Today 19:357–360CrossRefGoogle Scholar
Dossetter AG, Ecker G, Laverty H, Overington J (2014) ‘Big data’ in pharmaceutical science: challenges and opportunities. Future Med Chem 6:857–864CrossRefGoogle Scholar
Lusher SJ, McGuire R, van Schaik RC, Nicholson CD, de Vlieg J (2014) Data-driven medicinal chemistry in the era of big data. Drug Discov Today 19:859–868CrossRefGoogle Scholar
Richter L, Ecker GF (2015) Medicinal chemistry in the era of big data. Drug Discov Today Technol 14:37–41CrossRefGoogle Scholar
Schadt EE, Linderman MD, Sorenson J, Lee L, Nolan GP (2010) Computational solutions to large-scale data management and analysis. Nat Rev Genet 11:647–657CrossRefGoogle Scholar
Kenny PW, Sadowski J (2004) In: Oprea TI (ed) Chemoinformatics in drug discovery. Wiley-VCH, Weinheim, pp 271–285Google Scholar
Hussain J, Rea C (2010) Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets. J Chem Inf Model 50:339–348CrossRefGoogle Scholar
Wassermann AM, Bajorath J (2010) Chemical substitutions that introduce activity cliffs across different compound classes and biological targets. J Chem Inf Model 50:1248–1256CrossRefGoogle Scholar
Wawer M, Bajorath J (2011) Local structural changes, global data views: graphical substructure-activity relationship trailing. J Med Chem 54:2944–2951CrossRefGoogle Scholar
Wassermann AM, Haebel P, Weskamp N, Bajorath J (2012) SAR matrices: automated extraction of information-rich SAR tables from large compound data sets. J Chem Inf Model 52:1769–1776CrossRefGoogle Scholar
Wassermann AM, Bajorath J (2011) A data mining method to facilitate SAR transfer. J Chem Inf Model 51:1857–1866CrossRefGoogle Scholar
Gupta-Ostermann D, Hu Y, Bajorath J (2013) Systematic mining of analog series with related core structures in multi-target activity space. J Comput Aided Mol Des 27:665–674CrossRefGoogle Scholar
Shanmugasundaram V, Zhang L, Kayastha S, de la Vega de León A, Dimova D, Bajorath J (2016) Monitoring the progression of structure-activity relationship information during lead optimization. J Med Chem 59:4235–4244CrossRefGoogle Scholar