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A simple representation of three-dimensional molecular structure

View ORCID ProfileSeth D. Axen, View ORCID ProfileXi-Ping Huang, Elena L. Cáceres, Leo Gendelev, View ORCID ProfileBryan L. Roth, View ORCID ProfileMichael J. Keiser
doi: https://doi.org/10.1101/136705
Seth D. Axen
1Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, 675 Nelson Rising Ln NS 416A, San Francisco, CA 94143
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Xi-Ping Huang
2Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC 27599
4National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), University of North Carolina, Chapel Hill, North Carolina, USA
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Elena L. Cáceres
1Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, 675 Nelson Rising Ln NS 416A, San Francisco, CA 94143
3Department of Pharmaceutical Chemistry, Institute for Neurodegenerative Diseases, and Institute for Computational Health Sciences, University of California, San Francisco, 675 Nelson Rising Ln NS 416A, San Francisco, CA 94143
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Leo Gendelev
1Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, 675 Nelson Rising Ln NS 416A, San Francisco, CA 94143
3Department of Pharmaceutical Chemistry, Institute for Neurodegenerative Diseases, and Institute for Computational Health Sciences, University of California, San Francisco, 675 Nelson Rising Ln NS 416A, San Francisco, CA 94143
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Bryan L. Roth
2Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC 27599
4National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), University of North Carolina, Chapel Hill, North Carolina, USA
5Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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Michael J. Keiser
1Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, 675 Nelson Rising Ln NS 416A, San Francisco, CA 94143
3Department of Pharmaceutical Chemistry, Institute for Neurodegenerative Diseases, and Institute for Computational Health Sciences, University of California, San Francisco, 675 Nelson Rising Ln NS 416A, San Francisco, CA 94143
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  • For correspondence: keiser@keiserlab.org
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Abstract

Statistical and machine learning approaches predict drug-to-target relationships from 2D small-molecule topology patterns. One might expect 3D information to improve these calculations. Here we apply the logic of the Extended Connectivity FingerPrint (ECFP) to develop a rapid, alignment-invariant 3D representation of molecular conformers, the Extended Three-Dimensional FingerPrint (E3FP). By integrating E3FP with the Similarity Ensemble Approach (SEA), we achieve higher precision-recall performance relative to SEA with ECFP on ChEMBL20, and equivalent receiver operating characteristic performance. We identify classes of molecules for which E3FP is a better predictor of similarity in bioactivity than is ECFP. Finally, we report novel drug-to-target binding predictions inaccessible by 2D fingerprints and confirm three of them experimentally with ligand efficiencies from 0.442 - 0.637 kcal/mol/heavy atom.

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Posted May 13, 2017.
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A simple representation of three-dimensional molecular structure
Seth D. Axen, Xi-Ping Huang, Elena L. Cáceres, Leo Gendelev, Bryan L. Roth, Michael J. Keiser
bioRxiv 136705; doi: https://doi.org/10.1101/136705
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A simple representation of three-dimensional molecular structure
Seth D. Axen, Xi-Ping Huang, Elena L. Cáceres, Leo Gendelev, Bryan L. Roth, Michael J. Keiser
bioRxiv 136705; doi: https://doi.org/10.1101/136705

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