A Mixed Quantum Chemistry/Machine Learning Approach
for the Fast and Accurate Prediction of Biochemical Redox Potentials
and Its Large-Scale Application to 315 000 Redox Reactions
Version 3 2019-07-24, 07:16
Version 2 2019-06-07, 20:31
Version 1 2019-06-07, 20:03
Posted on 2019-07-24 - 07:16
A quantitative
understanding of the thermodynamics of biochemical
reactions is essential for accurately modeling metabolism. The group
contribution method (GCM) is one of the most widely used approaches
to estimate standard Gibbs energies and redox potentials of reactions
for which no experimental measurements exist. Previous work has shown
that quantum chemical predictions of biochemical thermodynamics are
a promising approach to overcome the limitations of GCM. However,
the quantum chemistry approach is significantly more expensive. Here,
we use a combination of quantum chemistry and machine learning to
obtain a fast and accurate method for predicting the thermodynamics
of biochemical redox reactions. We focus on predicting the redox potentials
of carbonyl functional group reductions to alcohols and amines, two
of the most ubiquitous carbon redox transformations in biology. Our
method relies on semiempirical quantum chemistry calculations calibrated
with Gaussian process (GP) regression against available experimental
data and results in higher predictive power than the GCM at low computational
cost. Direct calibration of GCM and fingerprint-based predictions
(without quantum chemistry) with GP regression also results in significant
improvements in prediction accuracy, demonstrating the versatility
of the approach. We design and implement a network expansion algorithm
that iteratively reduces and oxidizes a set of natural seed metabolites
and demonstrate the high-throughput applicability of our method by
predicting the standard potentials of more than 315 000 redox
reactions involving approximately 70 000 compounds. Additionally,
we developed a novel fingerprint-based framework for detecting molecular
environment motifs that are enriched or depleted across different
regions of the redox potential landscape. We provide open access to
all source code and data generated.
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Jinich, Adrian; Sanchez-Lengeling, Benjamin; Ren, Haniu; Harman, Rebecca; Aspuru-Guzik, Alán (2019). A Mixed Quantum Chemistry/Machine Learning Approach
for the Fast and Accurate Prediction of Biochemical Redox Potentials
and Its Large-Scale Application to 315 000 Redox Reactions. ACS Publications. Collection. https://doi.org/10.1021/acscentsci.9b00297
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AUTHORS (5)
AJ
Adrian Jinich
BS
Benjamin Sanchez-Lengeling
HR
Haniu Ren
RH
Rebecca Harman
AA
Alán Aspuru-Guzik
KEYWORDS
quantum chemistry calculationsGP70 000 compoundsBiochemical Redox Potentialsnovel fingerprint-based frameworkquantum chemistrycarbon redox transformationsGCMquantum chemistry approach315 000 Redox Reactions315 000group contribution method315 000 redox reactionsthermodynamicnetwork expansion algorithmredox potentialsquantum chemical predictions