SAR Matrix Method for Large-Scale Analysis of Compound Structure-Activity Relationships and Exploration of Multitarget Activity Spaces.

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.

[1]  Jürgen Bajorath,et al.  Monitoring the Progression of Structure-Activity Relationship Information during Lead Optimization. , 2016, Journal of medicinal chemistry.

[2]  J. Bajorath,et al.  Promiscuity progression of bioactive compounds over time , 2015, F1000Research.

[3]  J. Bajorath,et al.  Polypharmacology: challenges and opportunities in drug discovery. , 2014, Journal of medicinal chemistry.

[4]  Lars Richter,et al.  Medicinal chemistry in the era of big data. , 2015, Drug discovery today. Technologies.

[5]  J. Mestres,et al.  On the origins of drug polypharmacology , 2013 .

[6]  Jürgen Bajorath,et al.  Influence of Search Parameters and Criteria on Compound Selection, Promiscuity, and Pan Assay Interference Characteristics , 2014, J. Chem. Inf. Model..

[7]  Jin-jian Lu,et al.  Multi-Target Drugs: The Trend of Drug Research and Development , 2012, PloS one.

[8]  Anne Mai Wassermann,et al.  A Data Mining Method To Facilitate SAR Transfer , 2011, J. Chem. Inf. Model..

[9]  John P. Overington,et al.  ChEMBL: a large-scale bioactivity database for drug discovery , 2011, Nucleic Acids Res..

[10]  Ross McGuire,et al.  Data-driven medicinal chemistry in the era of big data. , 2014, Drug discovery today.

[11]  George Papadatos,et al.  The ChEMBL bioactivity database: an update , 2013, Nucleic Acids Res..

[12]  John P. Overington,et al.  'Big data' in pharmaceutical science: challenges and opportunities. , 2014, Future Medicinal Chemistry.

[13]  Jameed Hussain,et al.  Computationally Efficient Algorithm to Identify Matched Molecular Pairs (MMPs) in Large Data Sets , 2010, J. Chem. Inf. Model..

[14]  J. Bajorath,et al.  Local structural changes, global data views: graphical substructure-activity relationship trailing. , 2011, Journal of medicinal chemistry.

[15]  E. Jacoby Chemogenomics: drug discovery's panacea? , 2006, Molecular bioSystems.

[16]  J. Bajorath,et al.  Monitoring drug promiscuity over time , 2014, F1000Research.

[17]  Evan Bolton,et al.  PubChem's BioAssay Database , 2011, Nucleic Acids Res..

[18]  G. Nolan,et al.  Computational solutions to large-scale data management and analysis , 2010, Nature Reviews Genetics.

[19]  Jürgen Bajorath,et al.  Systematic mining of analog series with related core structures in multi-target activity space , 2013, Journal of Computer-Aided Molecular Design.

[20]  J. Bajorath,et al.  Compound promiscuity: what can we learn from current data? , 2013, Drug discovery today.

[21]  J. Bajorath,et al.  Chemical Substitutions That Introduce Activity Cliffs Across Different Compound Classes and Biological Targets , 2010, J. Chem. Inf. Model..

[22]  J. Bajorath,et al.  Learning from 'big data': compounds and targets. , 2014, Drug discovery today.

[23]  Anne Mai Wassermann,et al.  SAR Matrices: Automated Extraction of Information-Rich SAR Tables from Large Compound Data Sets , 2012, J. Chem. Inf. Model..