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Machine-Learning Models for Combinatorial Catalyst Discovery

Published online by Cambridge University Press:  01 February 2011

Gregory A. Landrum ,
Julie Penzotti  and
Santosh Putta
Gregory A. Landrum
Affiliation:
Rational Discovery LLC, 555 Bryant St. #467, Palo Alto, CA 94301, USA
Julie Penzotti
Affiliation:
Rational Discovery LLC, 555 Bryant St. #467, Palo Alto, CA 94301, USA
Santosh Putta
Affiliation:
Rational Discovery LLC, 555 Bryant St. #467, Palo Alto, CA 94301, USA
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Abstract

Standard machine-learning algorithms were used to build models capable of predicting the molecular weights of polymers generated by a homogeneous catalyst. Using descriptors calculated from only the two-dimensional structures of the ligands, the average accuracy of the models on an external validation data set was approximately 70%. Because the models show no bias and perform significantly better than equivalent models built using randomized data, we conclude that they learned useful rules and did not overfit the data.

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