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Cheminformatics Tools for Analyzing and Designing Optimized Small Molecule Collections and Libraries

45 Pages Posted: 30 May 2019 Publication Status: Published

See all articles by Nienke Moret

Nienke Moret

Harvard University - Department of Systems Biology

Nicholas A. Clark

University of Cincinnati - Division of Biostatistics and Bioinformatics

Marc Hafner

Harvard University - Department of Systems Biology

Yuan Wang

Novartis Institutes for Biomedical Research, Cambridge

Eugen Lounkine

Novartis Institutes for Biomedical Research, Cambridge

Mario Medvedovic

University of Cincinnati - Division of Biostatistics and Bioinformatics

Jinhua Wang

Dana-Farber Cancer Institute - Department of Cancer Biology

Nathanael Gray

Dana-Farber/Harvard Cancer Center - Department of Cancer Biology; Harvard University - Department of Biological Chemistry and Molecular Pharmacology

Jeremy Jenkins

Novartis Institutes for Biomedical Research, Cambridge

Peter Sorger

Harvard University - Department of Systems Biology

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Abstract

Libraries of well-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 an approach to scoring and creating libraries based on available data, which is often incomplete, on binding selectivity, target coverage and induced cellular phenotypes as well as chemical structure, stage of clinical development and user preference. The approach, implemented as R software and a Web-accessible tool also assembles small sets of compounds (typically 2-8) that target a protein of interest while having the lowest possible off target overlap. 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, public libraries can be updated based on the latest data and the confounding effects of off-target activities minimized in chemical genetic studies.

Suggested Citation

Moret, Nienke and Clark, Nicholas A. and Hafner, Marc and Wang, Yuan and Lounkine, Eugen and Medvedovic, Mario and Wang, Jinhua and Gray, Nathanael and Jenkins, Jeremy and Sorger, Peter, Cheminformatics Tools for Analyzing and Designing Optimized Small Molecule Collections and Libraries (2018). Available at SSRN: https://ssrn.com/abstract=3219258 or http://dx.doi.org/10.2139/ssrn.3219258
This version of the paper has not been formally peer reviewed.

Nienke Moret

Harvard University - Department of Systems Biology

Boston,, MA 02115
United States

Nicholas A. Clark

University of Cincinnati - Division of Biostatistics and Bioinformatics

United States

Marc Hafner

Harvard University - Department of Systems Biology

Boston,, MA 02115
United States

Yuan Wang

Novartis Institutes for Biomedical Research, Cambridge

6A-209
250 Massachusetts Avenue
Cambridge, MA 02139
United States

Eugen Lounkine

Novartis Institutes for Biomedical Research, Cambridge

6A-209
250 Massachusetts Avenue
Cambridge, MA 02139
United States

Mario Medvedovic

University of Cincinnati - Division of Biostatistics and Bioinformatics

United States

Jinhua Wang

Dana-Farber Cancer Institute - Department of Cancer Biology

450 Brookline Avenue
Boston, MA 02215
United States

Nathanael Gray

Dana-Farber/Harvard Cancer Center - Department of Cancer Biology

450 Brookline Avenue
Boston, MA 02115
United States

Harvard University - Department of Biological Chemistry and Molecular Pharmacology

250 Longwood Avenue
Boston, MA 02115
United States

Jeremy Jenkins

Novartis Institutes for Biomedical Research, Cambridge

6A-209
250 Massachusetts Avenue
Cambridge, MA 02139
United States

Peter Sorger (Contact Author)

Harvard University - Department of Systems Biology ( email )

Boston,, MA 02115
United States

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