In recent years, tools for the development of new drugs have been dramatically improved. These include genomic and proteomic research, numerous biophysical methods, combinatorial chemistry and screening technologies. In addition, early ADMET studies are employed in order to significantly reduce the failure rate in the development of drug candidates. As a consequence, the lead finding, lead optimization and development process has gained marked enhancement in speed and efficiency. In parallel to this development, major pharma companies are increasingly outsourcing many components of drug discovery research to biotech companies. All these measures are designed to address the need for a faster time to market. New screening methodologies have contributed significantly to the efficiency of the drug discovery process. The conventional screening of single compounds or compound libraries has been dramatically accelerated by high throughput screening methods. In addition, in silico screening methods allow the evaluation of virtual compounds. A wide range of new lead finding and lead optimization opportunities result from novel screening methods by NMR, which are the topic of this review article.

Drug Discovery Today

No. 72, Division of Chemical Information. (15) Wassermann, A. M.; Dimova, D.; Bajorath, J. Comprehensive Analysis of Singleand Multi-Target Activity Cliffs Formed by Currently Available Bioactive Compounds. Chem. Biol. Drug Des. 2011, 78, 224−228. (16) Medina-Franco, J. L.; Martínez-Mayorga, K.; Bender, A.; Marín, R. M.; Giulianotti, M. A.; Pinilla, C.; Houghten, R. A. Characterization of Activity Landscapes using 2D and 3D Similarity Methods: Consensus Activity Cliffs. J. Chem. Inf. Model. 2009, 49, 477−491. (17) Yongye, A. B.; Byler, K.; Santos, R.; Martínez-Mayorga, K.; Maggiora, G. M.; Medina-Franco, J. L. Consensus Models of Activity Landscapes with Multiple Chemical, Conformer, and Property Representations. J. Chem. Inf. Model. 2011, 51, 2427−2439. (18) Rogers, D.; Hahn, M. Extended-Connectivity Fingerprints. J. Chem. Inf. Model. 2010, 50, 742−754. (19) MACCS Structural Keys; Symyx Software: San Ramon, CA, 2005. (20) Lajiness, M. Exploring and Exploiting the Potential of Structure−Activity Cliffs. Proceedings of the 240th National Meeting of the American Chemical Society, Boston, MA, August 22−26, 2010; American Chemical Society: Washington, DC, 2010; Abstract No. 61, Division of Chemical Information. (21) Stumpfe, D.; Bajorath, J. Assessing the Confidence Level of Public Domain Compound Activity Data and the Impact of Alternative Potency Measurements on SAR Analysis. J. Chem. Inf. Model. 2011, 51, 3131−3137. Journal of Medicinal Chemistry Perspective dx.doi.org/10.1021/jm201706b | J. Med. Chem. 2012, 55, 2932−2942 2941 (22) Liu, T.; Lin, Y.; Wen, X.; Jorissen, R. N.; Gilson, M. K. BindingDB: a Web-Accessible Database of Experimentally Determined Protein−Ligand Binding Affinities. Nucleic Acids Res. 2007, 35, D198− D201. (23) Agrafiotis, D. K.; Wiener, J. J. M.; Skalkin, A.; Kolpak, J. Single R-Group Polymorphisms (SRPs) and R-Cliffs: An Intuitive Framework for Analyzing and Visualizing Activity Cliffs in a Single Analog Series. J. Chem. Inf. Model. 2011, 51, 1122−1132. (24) Peltason, L.; Hu, Y.; Bajorath, J. From Structure−Activity to Structure−Selectivity Relationships: Quantitative Assessment, Selectivity Cliffs, and Key Compounds. ChemMedChem 2009, 4, 1864− 1873. (25) Dimova, D.; Wawer, M.; Wassermann, A. M.; Bajorath, J. Design of Multi-Target Activity Landscapes That Capture Hierarchical Activity Cliff Distributions. J. Chem. Inf. Model. 2011, 51, 256−288. (26) Iyer, P.; Stumpfe, D.; Bajorath, J. Molecular Mechanism-Based Network-like Similarity Graphs Reveal Relationships between Different Types of Receptor Ligands and Structural Changes That Determine Agonistic, Inverse-Agonistic, and Antagonistic Effects. J. Chem. Inf. Model. 2011, 51, 1281−1286. (27) Sisay, M. T.; Peltason, L.; Bajorath, J. Structural Interpretation of Activity Cliffs Revealed by Systematic Analysis of Structure− Activity Relationships in Analog Series. J. Chem. Inf. Model. 2009, 49, 2179−2189. (28) Seebeck, B.; Wagener, M.; Rarey, M. From Activity Cliffs to Target-Specific Scoring Models and Pharmacophore Hypotheses. ChemMedChem 2011, 6, 1630−1639. (29) Gaulton, A.; Bellis, L. J.; Bento, A. P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; Overington, J. P. ChEMBL: A Large-Scale Bioactivity Database for Drug Discovery. Nucleic Acids Res. 2012, 40, D1100−D1107. (30) Kenny, P. W.; Sadowski, J. Structure Modification in Chemical Databases. In Chemoinformatics in Drug Discovery; Oprea, T. I., Ed.; Wiley-VCH: Weinheim, Germany, 2005; pp 271−285. (31) Wassermann, A. M.; Bajorath, J. Chemical Substitutions That Introduce Activity Cliffs across Different Compound Classes and Biological Targets. J. Chem. Inf. Model. 2010, 50, 1248−1256. (32) Bemis, G. W.; Murcko, M. A. The Properties of Known Drugs. 1. Molecular Frameworks. J. Med. Chem. 1996, 39, 2887−2893. (33) Hu, Y.; Bajorath, J. Molecular Scaffolds with High Propensity to Form Multi-Target Activity Cliffs. J. Chem. Inf. Model. 2010, 50, 500−510. (34) Vogt, M.; Huang, Y.; Bajorath, J. From Activity Cliffs to Activity Ridges: Informative Data Structures for SAR Analysis. J. Chem. Inf. Model. 2011, 51, 1848−1856. (35) Xu, Y.-J.; Johnson, M. Algorithm for Naming Molecular Equivalence Classes Represented by Labeled Pseudographs. J. Chem. Inf. Comput. Sci. 2001, 41, 181−185. (36) Wawer, M.; Bajorath, J. Local Structural Changes, Global Data Views: Graphical Substructure−Activity Relationship Trailing. J. Med. Chem. 2011, 54, 2944−2951. (37) Lounkine, E.; Wawer, M.; Wassermann, A. M.; Bajorath, J. SARANEA: A Freely Available Program To Mine Structure−Activity and Structure−Selectivity Relationship Information in Compound Data Sets. J. Chem. Inf. Model. 2010, 50, 68−78. (38) Namasivayam, V.; Iyer, P.; Bajorath, J. Exploring SAR Continuity in the Vicinity of Activity Cliffs. Chem. Biol. Drug Des. 2012, 79, 22−29. (39) Hopkins, A. L. Network Pharmacology: The Next Paradigm in Drug Discovery. Nat. Chem. Biol. 2008, 4, 682−690. (40) Jacoby, E.; Mozzarelli, A. Chemogenomic Strategies To Expand the Bioactive Chemical Space. Curr. Med. Chem. 2009, 16, 4374−4381. Journal of Medicinal Chemistry Perspective dx.doi.org/10.1021/jm201706b | J. Med. Chem. 2012, 55, 2932−2942 2942

Exploring Activity Cliffs in Medicinal Chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the const...

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Information retrieval and text mining technologies for chemistry

We present OSIRIS, an entirely in-house developed drug discovery informatics system. Its components cover all information handling aspects from compound synthesis via biological testing to preclinical development. Its design principles are platform and vendor independence, a consistent look and feel, and complete coverage of the drug discovery process by custom tailored applications. These include electronic laboratory notebook applications for biology and chemistry, tools for high-throughput and secondary screening evaluation, chemistry-aware data visualization, physicochemical property prediction, 3D-pharmacophore comparisons, interactive modeling, computing grid based ligand-protein docking, and more. Most applications are developed in Java and are built on top of a Java library layer that provides reusable cheminformatics functionality and GUI components such as chemical editors, structure canonicalization, substructure search, combinatorial enumeration, enhanced stereo perception, force field minimization, and conformation generation.

OSIRIS, an entirely in-house developed drug discovery informatics system.

Integrating Biological Databases.

We present ABCD, an integrated drug discovery informatics platform developed at Johnson & Johnson Pharmaceutical Research & Development, L.L.C. ABCD is an attempt to bridge multiple continents, dat...

Advanced biological and chemical discovery (ABCD): centralizing discovery knowledge in an inherently decentralized world.

Efficient substructure searching is a key requirement for any chemical information management system. In this paper, we describe the substructure search capabilities of ABCD, an integrated drug dis...

Efficient substructure searching of large chemical libraries: the ABCD chemical cartridge.

Drug discovery is a highly complex process requiring scientists from wide-ranging disciplines to work togetherin a well-coordinated and streamlined fashion. While the process can be compartmentalized into well-defined functionaldomains, the success of the entire enterprise rests on the ability to exchange data conveniently between these domains,and integrate it in meaningful ways to support the design, execution and interpretation of experiments aimed at optimizingthe efficacy and safety of new drugs. This, in turn, requires information management systems that can support many differenttypes of scientific technologies generating data of imposing complexity, diversity and volume. Here, we describethe key components of our Advanced Biological and Chemical Discovery (ABCD), a software platform designed at Johnson& Johnson to bring coherence in the way discovery data is collected, annotated, organized, integrated, mined andvisualized. Unlike the Gordian knot of one-off solutions built to serve a single purpose for a single set of users that onetypically encounters in the pharmaceutical industry, we sought to develop a framework that could be extended and leveragedacross different application domains, and offer a consistent user experience marked by superior performance and usability.In this work, several major components of ABCD are highlighted, ranging from operational subsystems for managingreagents, reactions, compounds, and assays, to advanced data mining and visualization tools for SAR analysis andinterpretation. All these capabilities are delivered through a common application front-end called Third Dimension Explorer(3DX), a modular, multifunctional and extensible platform designed to be the “Swiss-army knife” of the discoveryscientist.

An integrated data management framework for drug discovery--from data capturing to decision support.

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Advanced biological and chemical discovery (ABCD): centralizing discovery knowledge in an inherently decentralized world.

Efficient substructure searching of large chemical libraries: the ABCD chemical cartridge.

An integrated data management framework for drug discovery--from data capturing to decision support.