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Comment on "Predicting reaction performance in C–N cross-coupling using machine learning"
2018
Science
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(Reports, 13 April 2018) applied machine learning models to predict C–N cross-coupling reaction yields. The models use atomic, electronic, and vibrational descriptors as input features. ...
thus failing classical controls in machine learning. ...
A recent report by Ahneman et al. (1) describes a machine learning approach for modeling chemical reactions with data collected through ultrahigh-throughput experimentation. ...
doi:10.1126/science.aat8603
fatcat:ps5ybxdxhfhk7pdovwbmh2csra
Artificial Intelligence in Drug Design
2018
Molecules
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Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. ...
Numerous applications in property or activity predictions like physicochemical and ADMET properties have recently appeared and underpin the strength of this technology in quantitative structure-property ...
Author Contributions: Both authors contributed to the writing of the manuscript.
Funding: The authors are full time Sanofi employees. This work received no external funding. ...
doi:10.3390/molecules23102520
pmid:30279331
pmcid:PMC6222615
fatcat:gx6klyz3afhcbmxgynv2jn6zeq
Predicting outcomes of catalytic reactions using machine learning
[article]
2019
arXiv
pre-print
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Our machine learning approach complements chemical intuition in predicting the outcome of several types of chemical reactions. ...
In some cases, machine learning makes correct predictions where chemical intuition fails. We achieve up to 93% prediction accuracy for a small data set of less than two hundred reactions. ...
Machine learning (ML) provides an approach to efficient reaction prediction that does not require a strong a priori understanding of the pathways and kinetics involved in the reaction. ...
arXiv:1908.10953v1
fatcat:h2esnehd4raqjeg6gahvmuxnrm
Computational Chemical Synthesis Analysis and Pathway Design
2018
Frontiers in Chemistry
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A., Azencott, C. A., Chen, J. H., and Baldi, P. (2011). Learning to predict chemical reactions. J. Chem. Inf. ...
Carrera et al. used machine learning to predict chemical reactivity of organic molecules (Carrera et al., 2009). ...
doi:10.3389/fchem.2018.00199
pmid:29915783
pmcid:PMC5994992
fatcat:ea5aqsgtc5dhheu2njje36y77i
Controlling an organic synthesis robot with machine learning to search for new reactivity
2018
Nature
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Inspired by strategies based on chemists' intuition7, we propose that a reaction system controlled by a machine learning algorithm may be able to explore the space of chemical reactions quickly, especially ...
An attractive alternative is to predict reactivity, although relevant approaches, such as computer-aided reaction design, are still in their infancy2. ...
We thank A. Henson for help with the Tanimoto analysis. ...
doi:10.1038/s41586-018-0307-8
pmid:30022133
pmcid:PMC6223543
fatcat:j2k2jarqyjcu5oahu4ezp76jsi
Universal Chemical Synthesis and Discovery with 'The Chemputer'
2019
Trends in Chemistry
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The alternative to this problem, as shown in this opinion article, is the development of an approach to universal chemistry using a programming language with automation in combination with machine learning ...
Applying this to chemistry requires a holistic approach to chemical synthesis design and execution. ...
Machine Learning towards Chemical Space Exploration Machine learning approaches are fundamental to scientific investigation in many disciplines. ...
doi:10.1016/j.trechm.2019.07.004
fatcat:c7ak3odppbcyliv6mp3qa4wi6q
Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats
2021
Engineering
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Nevertheless, machine learning will definitely become a trustworthy element in the modeling toolbox of chemical engineers. ...
In the last five years, the increasing availability of data and computational resources has led to a resurgence in machine learning-based research. ...
Van Geem declare that they have no conflict of interest or financial conflicts to disclose. ...
doi:10.1016/j.eng.2021.03.019
fatcat:y6z546dm55dw7djsehpkr3sjmu
Design of self-assembly dipeptide hydrogels and machine learning via their chemical features
2019
Proceedings of the National Academy of Sciences of the United States of America
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We used a quantitative structure–property relationship to calculate their chemical features reflecting the topological and physicochemical properties, and applied machine learning to predict the self-assembly ...
The combinatorial hydrogel library and the machine learning approach we developed linked the chemical structures with their self-assembly behavior and can accelerate the design of novel peptide structures ...
We generated the chemical features of the whole chemical library and developed the machine learning method to recognize these chemical features and predict whether a chemical structure can form a hydrogel ...
doi:10.1073/pnas.1903376116
pmid:31110004
pmcid:PMC6561259
fatcat:dv3tlgodp5c7fijbghreyoark4
QNA-Based Prediction of Sites of Metabolism
2017
Molecules
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Integration of various methods and techniques using different computational and experimental approaches is one of the keys to a successful metabolism prediction. ...
In this paper, we describe the application of Quantitative Neighborhoods of Atoms (QNA) descriptors for prediction of the SOMs using potential function method, as well as several different machine learning ...
Alexey Lagunin, Anastassia Rudik, and Alexander Dmitriev prepared the datasets of chemical structures for the compounds that underwent metabolizing reactions. ...
doi:10.3390/molecules22122123
pmid:29194399
fatcat:en2chpz6jzcffcca7r2k3dwtjy
A graph-convolutional neural network model for the prediction of chemical reactivity
2019
Chemical Science
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We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s). ...
Acknowledgements We thank members of the MIT Department of Chemistry and Department of Chemical Engineering who participated in the human benchmarking study. ...
Major developments in machine learning and data availability have enabled new approaches to this problem. 12 For specic reaction families with sufficiently detailed reaction condition data, machine ...
doi:10.1039/c8sc04228d
pmid:30746086
pmcid:PMC6335848
fatcat:lld75gdluvclrmv673ucveh5eu
Review of Machine Learning Methods for the Prediction and Reconstruction of Metabolic Pathways
2021
Frontiers in Molecular Biosciences
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To support researchers in this field, we briefly review the research progress of metabolic pathway reconstruction and prediction based on machine learning. ...
Machine learning techniques are showing state-of-the-art performance to handle the rapidly increasing volume of data in synthetic biology. ...
To alleviate above problem, a lot of machine learning based approaches have been proposed to predict metabolites via learning the spectra patterns of the known compounds. ...
doi:10.3389/fmolb.2021.634141
fatcat:2l54gs5xvfeqvpihx6niqn2vgu
Journal of Materials Chemistry A and Materials Advances Editor's choice web collection: "Machine learning for materials innovation"
2021
Materials Advances
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Zhen Zhou introduces a Journal of Materials Chemistry/Materials Advances Editor's choice web collection on machine learning for materials innovation (https://rsc.li/MachineLearning). ...
Bokka et al. used machine learning techniques to accurately predict the stimuli of a sensor (DOI: 10.1039/d0ma00573h). ...
Liu et al. developed an approach to assess the possible reactions and thermodynamic stability of Li|Li 7 La 3 Zr 2 O 12 interfaces under various chemical conditions and proposed that some dopants in Li ...
doi:10.1039/d0ma90054k
fatcat:5344vzoidbhhjmx7aycfzupb24
Machine learning made easy for optimizing chemical reactions
2021
Nature
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Machine learning has emerged as a useful tool for various aspects of chemical synthesis, because it is ideally suited to extrapolating predictive models that are used to solve synthetic problems by recognizing ...
To train their model, Shields and colleagues selected a method that uses a machine-learning approach called Bayesian optimization. ...
Machine learning has emerged as a useful tool for various aspects of chemical synthesis, because it is ideally suited to extrapolating predictive models that are used to solve synthetic problems by recognizing ...
doi:10.1038/d41586-021-00209-6
pmid:33536642
fatcat:5opnhiuchnaebi6q3264hyd5ye
ACS Central Science Virtual Issue on Machine Learning
2018
ACS Central Science
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learning in chemical science essentially engage this goal by learning to extract models, rules, and predictions from data, but one approach stands out for its remarkable power and flexibility in a diversity ...
chemical features. 9 In the context of reaction prediction and engineering, Aspuru-Guzik and co-workers 10 and Green and Jensen and co-workers 11 use deep learning to predict the products of organic ...
doi:10.1021/acscentsci.8b00528
pmid:30159387
pmcid:PMC6107860
fatcat:qtism3iabbbsvg5osjofishkj4
Advances of Machine Learning in Molecular Modeling and Simulation
[article]
2019
arXiv
pre-print
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We conclude by outlining challenges and future research directions that need to be addressed in order to make machine learning a mainstream chemical engineering tool. ...
After giving a brief overview of the foundations, components, and workflow of a typical supervised learning approach for chemical problems, we showcase areas and state-of-the-art examples of their deployment ...
Its early successes indicate that ML is bound to become a mainstream tool in chemical research. Yet, there is still much to (machine) learn on how to develop the full potential of ML in chemistry. ...
arXiv:1902.00140v2
fatcat:yzco7xyw6zdybknasl53gu7nqi
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