Molecular Machine Learning: The Future of Synthetic Chemistry?

Chemists: AI is here, unite to get the benefits

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A Structure-Based Platform for Predicting Chemical Reactivity

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A mobile robotic chemist

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Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning

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Rapid Prediction of Electron–Ionization Mass Spectrometry Using Neural Networks

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Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra

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Machine Learning for Computational Heterogeneous Catalysis

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Holistic prediction of enantioselectivity in asymmetric catalysis

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Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis

(2019) Xiwen Jia et al.   NATURE

Rapid prediction of NMR spectral properties with quantified uncertainty

(2019) Eric Jonas et al.   Journal of Cheminformatics

Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction

(2019) Philippe Schwaller et al.   ACS Central Science

Synergy Between Expert and Machine-Learning Approaches Allows for Improved Retrosynthetic Planning

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Machine Learning in Computer-Aided Synthesis Planning

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Machine learning in chemoinformatics and drug discovery

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Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

(2018) Sabrina Jaeger et al.   Journal of Chemical Information and Modeling

Predicting reaction performance in C–N cross-coupling using machine learning

(2018) Derek T. Ahneman et al.   SCIENCE

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

(2018) Rafael Gómez-Bombarelli et al.   ACS Central Science

Controlling an organic synthesis robot with machine learning to search for new reactivity

(2018) Jarosław M. Granda et al.   NATURE

Planning chemical syntheses with deep neural networks and symbolic AI

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Adversarial Controls for Scientific Machine Learning

(2018) Kangway V. Chuang et al.   ACS Chemical Biology

Prediction of Major Regio-, Site-, and Diastereoisomers in Diels-Alder Reactions by Using Machine-Learning: The Importance of Physically Meaningful Descriptors

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Comment on “Predicting reaction performance in C–N cross-coupling using machine learning”

(2018) Kangway V. Chuang et al.   SCIENCE

Using Machine Learning To Predict Suitable Conditions for Organic Reactions

(2018) Hanyu Gao et al.   ACS Central Science

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

(2017) Felix A. Faber et al.   Journal of Chemical Theory and Computation

Prediction of Organic Reaction Outcomes Using Machine Learning

(2017) Connor W. Coley et al.   ACS Central Science

Computer-Assisted Synthetic Planning: The End of the Beginning

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Molecular graph convolutions: moving beyond fingerprints

(2016) Steven Kearnes et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

Quantum chemistry structures and properties of 134 kilo molecules

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A Survey on Transfer Learning

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