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Organic reactivity from mechanism to machine learning

Nature Reviews Chemistry volume 5, pages 240–255 (2021)Cite this article

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A Publisher Correction to this article was published on 22 March 2021

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Abstract

As more data are introduced in the building of models of chemical reactivity, the mechanistic component can be reduced until ‘big data’ applications are reached. These methods no longer depend on underlying mechanistic hypotheses, potentially learning them implicitly through extensive data training. Reactivity models often focus on reaction barriers, but can also be trained to directly predict lab-relevant properties, such as yields or conditions. Calculations with a quantum-mechanical component are still preferred for quantitative predictions of reactivity. Although big data applications tend to be more qualitative, they have the advantage to be broadly applied to different kinds of reactions. There is a continuum of methods in between these extremes, such as methods that use quantum-derived data or descriptors in machine learning models. Here, we present an overview of the recent machine learning applications in the field of chemical reactivity from a mechanistic perspective. Starting with a summary of how reactivity questions are addressed by quantum-mechanical methods, we discuss methods that augment or replace quantum-based modelling with faster alternatives relying on machine learning.

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Fig. 1: Approaches for modelling chemical reactivity.
Fig. 2: An example reaction profile of a simple E2 elimination.
Fig. 3: Molecular mechanics methods for generating transition states.
Fig. 4: Reactivity predictions from quantum mechanics data.
Fig. 5: Experiment versus prediction using a descriptor-based model.
Fig. 6: Different types of reaction fingerprints.
Fig. 7: General reactivity models.

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Acknowledgements

K.J. is a fellow of the AstraZeneca Postdoc Programme.

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Authors and Affiliations

  1. Early Chemical Development, Pharmaceutical Sciences, R&D, AstraZeneca, Macclesfield, UK

    Kjell Jorner

  2. Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden

    Anna Tomberg

  3. Data Science and Modelling, Pharmaceutical Sciences, R&D, AstraZeneca, Gothenburg, Sweden

    Christoph Bauer & Per-Ola Norrby

  4. Drug Design and Discovery, Department of Medicinal Chemistry, Uppsala University, Uppsala, Sweden

    Christian Sköld

Authors
  1. Kjell Jorner
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  2. Anna Tomberg
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  3. Christoph Bauer
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  4. Christian Sköld
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  5. Per-Ola Norrby
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Correspondence to Per-Ola Norrby.

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Related links

CGRtools: https://github.com/cimm-kzn/CGRtools

CIMtools: https://github.com/cimm-kzn/CIMtools

Daylight Chemical Information Systems: Fingerprints: https://www.daylight.com/dayhtml/doc/theory/theory.finger.html

Daylight Chemical Information Systems: SMILES: https://www.daylight.com/dayhtml/doc/theory/theory.smiles.html

Daylight Chemical Information Systems: SMARTS: https://www.daylight.com/dayhtml/doc/theory/theory.smarts.html

Dragon Descriptors: https://chm.kode-solutions.net/products_dragon_descriptors.php

IUPAC InChI: http://www.iupac.org/inchi/

Lowe, D. M. Patent Reaction Extractor: https://github.com/dan2097/patent-reaction-extraction

Open Reaction Database: https://ord-schema.readthedocs.io/en/latest/

RDKit: Open-Source Cheminformatics Software: https://www.rdkit.org/

Reaxys: http://www.reaxys.com

Glossary

Density functional theory

(DFT). A quantum-mechanical method based on electron density for simulating molecules and reactions.

Descriptors

Also referred to as features. The properties used to train a machine learning model.

Semiempirical QM methods

Use the same algorithms as wave function and density functional theory methods, but approximated values for matrix elements.

Domains of applicability

The regions of chemical space within which a model can reliably make predictions.

Gaussian process regression

Machine learning algorithm in which the data points are assumed to be the means of Gaussian distributions. Delivers both predicted means and variance.

Extra tree regressor model

Machine learning algorithm similar to random forest. Owing to differences in implementation, this method is usually faster than a random forest.

Random forest

Machine learning algorithm that builds an ensemble of decision trees and predicts the value of a new example by taking into consideration the prediction from each decision tree in the ensemble.

Sterimol parameters

A set of parameters that describes the steric effects of substituents.

Gradient boosting decision tree model

Machine learning algorithm that is based on decision trees (see ‘random forest’). The model is built stepwise, conjoined with the introduction of a learning rate. This approach has been shown to avoid overfitting problems.

Receiver operator characteristic

(ROC). Curve of true positive rate versus the false positive rate of a machine learning classification algorithm. The area under the ROC curve is often used as a performance metric.

Support vector machine

(SVM). A machine learning algorithm based on the idea that data points are divided by a hyperplane. The model tries to define the form of the hyperplane so as to maximize the separation between dissimilar data points.

Deep feed-forward neural network models

A feed-forward neural network, also called a multilayer perceptron, is one of the basic architectures in machine learning, in which the input nodes connect to hidden layers of nodes, which, in turn, connect to the output nodes. A neural network is feed-forward when no output information is channelled back into the model, as opposed to recurrent networks.

Molecular fingerprints

Molecular representations derived from the molecular connectivity.

Representations

Machine-readable descriptions of a molecule as, for example, a string of characters, a vector or a graph.

Atom mapping

Refers to the labelling of atoms in the reactants and the corresponding atoms in the products in a reaction SMARTS.

Deep learning

The field of machine learning that uses neural networks with many hidden layers.

Templates

Patterns describing a chemical reaction, often represented by reaction SMARTS.

SMARTS

A string representation of a molecular pattern, based on the simplified molecular input line entry system (SMILES). SMARTS are used to define a substructure of a molecule. For example, ethanol could be represented using the SMILES string CCO. To define the alcohol functional group, one uses SMARTS [#6][OX2H], in which each atomic position is enclosed in square brackets and encodes which atom types are allowed at this position.

Negative reactions

Reactions that give a low or zero yield. These are important for machine learning because the model needs to learn that not all input leads to a product.

Graph convolutional networks

Neural networks that operate on a graph and use convolution to create their own features for learning.

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Jorner, K., Tomberg, A., Bauer, C. et al. Organic reactivity from mechanism to machine learning. Nat Rev Chem 5, 240–255 (2021). https://doi.org/10.1038/s41570-021-00260-x

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