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Machine learning of chemical reactivity from databases of organic reactions

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Abstract

Databases of chemical reactions contain knowledge about the reactivity of specific reagents. Although information is in general only explicitly available for compounds reported to react, it is possible to derive information about substructures that do not react in the reported reactions. Both types of information (positive and negative) can be used to train machine learning techniques to predict if a compound reacts or not with a specific reagent. The whole process was implemented with two databases of reactions, one involving BuNH2 as the reagent, and the other NaCNBH3. Negative information was derived using MOLMAP molecular descriptors, and classification models were developed with Random Forests also based on MOLMAP descriptors. MOLMAP descriptors were based exclusively on calculated physicochemical features of molecules. Correct predictions were achieved for ∼90% of independent test sets. While NaCNBH3 is a selective reducing reagent widely used in organic synthesis, BuNH2 is a nucleophile that mimics the reactivity of the lysine side chain (involved in an initiating step of the mechanism leading to skin sensitization).

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Abbreviations

MOLMAP:

MOLecular maps of atom-level properties

BuNH2 :

Butylamine

RF:

Random forest

VOC:

Volatile organic compounds

QSAR:

Quantitative structure activity relationship

OOB:

Out of bag

SVM:

Support vector machines

ROC:

Receiver operating characteristic

SOM:

Self organizing maps

HTS:

High-throughput screening

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Acknowledgments

G.C. and S.G. acknowledge Fundação para a Ciência e Tecnologia (Lisbon, Portugal) for financial support under grants SFRH/BD/18354/2004 and SFRH/BPD/14475/2003. Molecular Networks GmbH (Erlangen, Germany) and Infochem (Munich, Germany) are acknowledged for access to the PETRA program and to subsets of chemical reactions from the SPRESI database, respectively.

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Authors and Affiliations

  1. REQUIMTE, CQFB, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516, Caparica, Portugal

    Gonçalo V. S. M. Carrera, Sunil Gupta & João Aires-de-Sousa

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  1. Gonçalo V. S. M. Carrera
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  2. Sunil Gupta
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  3. João Aires-de-Sousa
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Correspondence to João Aires-de-Sousa.

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Carrera, G.V.S.M., Gupta, S. & Aires-de-Sousa, J. Machine learning of chemical reactivity from databases of organic reactions. J Comput Aided Mol Des 23, 419–429 (2009). https://doi.org/10.1007/s10822-009-9275-2

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