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Chapter 16
Cheminformatics: At the Crossroad of Eras
Denis Fourches
© Springer Science+Business Media Dordrecht 2014
L. Gorb et al. (eds.), Application of Computational Techniques in Pharmacy and Medicine,
Challenges and Advances in Computational Chemistry and Physics 17,
DOI 10.1007/978-94-017-9257-8_16
D. Fourches ()
Laboratory for Molecular Modeling, University of North Carolina,
Chapel Hill, NC 27599, USA
e-mail: fourches@email.unc.edu
Abstract In this chapter, we discuss how the profusion of experimental chemogenomics data available in public repositories is transforming the field of cheminformatics. In particular, we describe (i) both theoretical and technical challenges
related to the management, analysis, and visualization of large and diverse chemical
datasets, (ii) the unique opportunities offered by Big Chemical Data for designing
molecules with the desired properties and expanding the use of cheminformatics in
novel areas of research, and (iii) some innovative approaches that are likely to shape
the future of cheminformatics.
The growing compendium of chemogenomics datasets available in publicly-accessible repositories is moving the field of Cheminformatics from the era of data
scarcity into the era of “Big Data” [1, 2]. Only ten years ago, a set of chemicals
with their associated activities was considered “large” when including ca. 100
compounds. Due to the lack of available experimental data, Quantitative StructureActivity Relationships (QSAR) models [3] were built using 20–50 molecules [4],
sometimes even less [5]. Meanwhile, combinatorial and high–throughput screening
(HTS) technologies have been skyrocketing in both academia and industry [6]. Although pharmaceutical companies still run the biggest HTS platforms, incorporating libraries of several millions of compounds, there are more and more academic
centers that not only conduct HTS but integrate their platform within academic drug
discovery centers [7]. The following US institutions are notable for this: UCLA’s
Molecular Screening Shared Resources (MSSR), Stanford’s HTBC, Northwestern
University’s High Throughput Analysis Laboratory, Molecular Libraries Screening
Center Network (MLSCN), as well as UNC’s NIMH Psychoactive Drug Screening
Program (PDSP) and UNC’s Center for Integrative Chemical Biology and Drug
Discovery (CICBDD). Certain of these centers have the potential to screen ca.
Chapter 16
Cheminformatics: At the Crossroad of Eras
Denis Fourches
© Springer Science+Business Media Dordrecht 2014
L. Gorb et al. (eds.), Application of Computational Techniques in Pharmacy and Medicine,
Challenges and Advances in Computational Chemistry and Physics 17,
DOI 10.1007/978-94-017-9257-8_16
D. Fourches ()
Laboratory for Molecular Modeling, University of North Carolina,
Chapel Hill, NC 27599, USA
e-mail: fourches@email.unc.edu
Abstract In this chapter, we discuss how the profusion of experimental chemogenomics data available in public repositories is transforming the field of cheminformatics. In particular, we describe (i) both theoretical and technical challenges
related to the management, analysis, and visualization of large and diverse chemical
datasets, (ii) the unique opportunities offered by Big Chemical Data for designing
molecules with the desired properties and expanding the use of cheminformatics in
novel areas of research, and (iii) some innovative approaches that are likely to shape
the future of cheminformatics.
The growing compendium of chemogenomics datasets available in publicly-accessible repositories is moving the field of Cheminformatics from the era of data
scarcity into the era of “Big Data” [1, 2]. Only ten years ago, a set of chemicals
with their associated activities was considered “large” when including ca. 100
compounds. Due to the lack of available experimental data, Quantitative StructureActivity Relationships (QSAR) models [3] were built using 20–50 molecules [4],
sometimes even less [5]. Meanwhile, combinatorial and high–throughput screening
(HTS) technologies have been skyrocketing in both academia and industry [6]. Although pharmaceutical companies still run the biggest HTS platforms, incorporating libraries of several millions of compounds, there are more and more academic
centers that not only conduct HTS but integrate their platform within academic drug
discovery centers [7]. The following US institutions are notable for this: UCLA’s
Molecular Screening Shared Resources (MSSR), Stanford’s HTBC, Northwestern
University’s High Throughput Analysis Laboratory, Molecular Libraries Screening
Center Network (MLSCN), as well as UNC’s NIMH Psychoactive Drug Screening
Program (PDSP) and UNC’s Center for Integrative Chemical Biology and Drug
Discovery (CICBDD). Certain of these centers have the potential to screen ca.