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Cheminformatics tools for analyzing and designing optimized small molecule libraries

View ORCID ProfileNienke Moret, View ORCID ProfileNicholas A. Clark, View ORCID ProfileMarc Hafner, Yuan Wang, Eugen Lounkine, Mario Medvedovic, Jinhua Wang, Nathanael Gray, View ORCID ProfileJeremy Jenkins, View ORCID ProfilePeter K. Sorger
doi: https://doi.org/10.1101/358978
Nienke Moret
1HMS LINCS and Druggable Genome Centers, Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, Massachusetts 02115, USA
2Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
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Nicholas A. Clark
3Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45221
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Marc Hafner
1HMS LINCS and Druggable Genome Centers, Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, Massachusetts 02115, USA
2Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
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Yuan Wang
4Novartis Institutes for BioMedical Research Inc., 181 Massachusetts Avenue, Cambridge, MA 02139, USA
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Eugen Lounkine
4Novartis Institutes for BioMedical Research Inc., 181 Massachusetts Avenue, Cambridge, MA 02139, USA
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Mario Medvedovic
3Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45221
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Jinhua Wang
5Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA.
6Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 360 Longwood Avenue, Longwood Center 2209, Boston, MA 02115, USA.
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Nathanael Gray
5Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA.
6Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 360 Longwood Avenue, Longwood Center 2209, Boston, MA 02115, USA.
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Jeremy Jenkins
4Novartis Institutes for BioMedical Research Inc., 181 Massachusetts Avenue, Cambridge, MA 02139, USA
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Peter K. Sorger
1HMS LINCS and Druggable Genome Centers, Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, Massachusetts 02115, USA
2Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
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Abstract

Libraries of highly annotated small molecules have many uses in chemical genetics, drug discovery and drug repurposing. Many such libraries have become available, but few data-driven approaches exist to compare these libraries and design new ones. In this paper, we describe such an approach that makes use of data on binding selectivity, target coverage and induced cellular phenotypes as well as chemical structure and stage of clinical development. We implement the approach as R software and a Web-accessible tool (http://www.smallmoleculesuite.org) that uses incomplete and often confounded public data in combination with user preferences to score and create libraries. Analysis of six kinase inhibitor libraries using our approach reveals dramatic differences among them, leading us to design a new LSP-OptimalKinase library that outperforms all previous collections in terms of target coverage and compact size. We also assemble a mechanism of action library that optimally covers 1852 targets of the liganded genome. Using our tools, individual research groups and companies can quickly analyze private compound collections and public libraries can be progressively improved using the latest data.

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Posted June 29, 2018.
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Cheminformatics tools for analyzing and designing optimized small molecule libraries
Nienke Moret, Nicholas A. Clark, Marc Hafner, Yuan Wang, Eugen Lounkine, Mario Medvedovic, Jinhua Wang, Nathanael Gray, Jeremy Jenkins, Peter K. Sorger
bioRxiv 358978; doi: https://doi.org/10.1101/358978
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Cheminformatics tools for analyzing and designing optimized small molecule libraries
Nienke Moret, Nicholas A. Clark, Marc Hafner, Yuan Wang, Eugen Lounkine, Mario Medvedovic, Jinhua Wang, Nathanael Gray, Jeremy Jenkins, Peter K. Sorger
bioRxiv 358978; doi: https://doi.org/10.1101/358978

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