Integrating computational and mixture-based screening of combinatorial libraries

• Published in: Journal of molecular modeling 2011-06, Vol.17 (6), p.1473-1482
• Main Authors: Yongye, Austin B., Pinilla, Clemencia, Medina-Franco, Jose L., Giulianotti, Marc A., Dooley, Colette T., Appel, Jon R., Nefzi, Adel, Scior, Thomas, Houghten, Richard A., Martínez-Mayorga, Karina
• Format: Article
• Language: English
• Subjects: Bridged Bicyclo Compounds - chemistry; Characterization and Evaluation of Materials; Chemistry; Chemistry and Materials Science; Computer Appl. in Life Sciences; Computer Applications in Chemistry; Computer Simulation; Databases, Factual; Guanidines - chemistry; High-Throughput Screening Assays - methods; Models, Molecular; Molecular Conformation; Molecular Medicine; Original Paper; Quantitative Structure-Activity Relationship; Receptors, Opioid, kappa - chemistry; Small Molecule Libraries - chemistry; Theoretical and Computational Chemistry
• ISSN: 1610-2940; 0948-5023
• DOI: 10.1007/s00894-010-0850-1

Mixture-based synthetic combinatorial library (MB-SCL) screening is a well-established experimental approach for rapidly retrieving structure–activity relationships (SAR) and identifying hits. Virtual screening is also a powerful approach that is increasingly being used in drug discovery programs and has a growing number of successful applications. However, limited efforts have been made to integrate both techniques. To this end, we combined experimental data from a MB-SCL of bicyclic guanidines screened against the κ-opioid receptor and molecular similarity methods. The activity data and similarity analyses were integrated in a biometric analysis–similarity map. Such a map allows the molecules to be categorized as actives, activity cliffs, low similarity to the reference compounds, or missed hits. A compound with IC 50  = 309 nM was found in the “missed hits” region, showing that active compounds can be retrieved from a MS-SCL via computational approaches. The strategy presented in this work is general and is envisioned as a general-purpose approach that can be applied to other MB-SCLs. Mixture-based screening activity data and molecular similarity comparisons to known active compounds are integrated via a biometrical analysis-similarity map , to determine the extent to which molecular similarity methods can rescue missed hits from a mixture-based screening synthetic combinatorial library.