BetaCitation Atlas

RESEARCH PAPER · Chem · 2020

A Structure-Based Platform for Predicting Chemical Reactivity

Frederik Sandfort, Felix Strieth‐Kalthoff, Marius Kühnemund, Christian Beecks, Frank Glorius

Read original paper ↗

See where this research leads.

283 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
42 direct citing works

A Structure-Based Platform for Predicting Chemical ReactivityOriginalMachine learning the ropes: principles, applications and directions in synthetic chemistry279PATENTOMETRIA: UMA FERRAMENTA INDISPENSÁVEL NO ESTUDO DE DESENVOLVIMENTO DE TECNOLOGIAS PARA A INDÚSTRIA QUÍMICA9What can reaction databases teach us about Buchwald–Hartwig cross-couplings?64Fingerprints of molecular reactivity6Molecular Representation: Going Long on Fingerprints114Development of a Computer-Guided Workflow for Catalyst Optimization. Descriptor Validation, Subset Selection, and Training Set Analysis92Electronic Properties and Photocatalytic Hydrogen Evolution Rates for Alternating Conjugated Copolymers: Predictions and Insights by Data-Driven Models0Molecular Machine Learning: The Future of Synthetic Chemistry?72Molekulares maschinelles Lernen: Die Zukunft der Synthesechemie?3Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies204Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset124Electro‐Descriptors for the Performance Prediction of Electro‐Organic Synthesis56Electro‐Descriptors for the Performance Prediction of Electro‐Organic Synthesis14Computational Methods for Training Set Selection and Error Assessment Applied to Catalyst Design: Guidelines for Deciding Which Reactions to Run First and Which to Run Next0Data augmentation strategies to improve reaction yield predictions and estimate uncertainty26Data augmentation strategies to improve reaction yield predictions and estimate uncertainty17Intelligent Microfluidics: The Convergence of Machine Learning and Microfluidics in Materials Science and Biomedicine189Improvement of Prediction Performance With Conjoint Molecular Fingerprint in Deep Learning83Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors169Chemical data intelligence for sustainable chemistry68Computational methods for training set selection and error assessment applied to catalyst design: guidelines for deciding which reactions to run first and which to run next25DeepReac+: deep active learning for quantitative modeling of organic chemical reactions52Machine learning prediction of hydrogen atom transfer reactivity in photoredox-mediated C–H functionalization33Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts105Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries83Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases62Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning88Mapping the space of chemical reactions using attention-based neural networks356Permutationally Invariant Deep Learning Approach to Molecular Fingerprinting with Application to Compound Mixtures5Cross-validation strategies in QSPR modelling of chemical reactions21Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields175Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning112Prediction of chemical reaction yields using deep learning291Organic reactivity from mechanism to machine learning175Automation and computer-assisted planning for chemical synthesis189Predicting Reaction Yields via Supervised Learning187Kernel Methods for Predicting Yields of Chemical Reactions2Dreams, False Starts, Dead Ends, and Redemption: A Chronicle of the Evolution of a Chemoinformatic Workflow for the Optimization of Enantioselective Catalysts56A Conformer‐Dependent, Quantitative Quadrant Model14Quantum Machine Learning Algorithms for Drug Discovery Applications175Reaction Classification and Yield Prediction using the Differential Reaction Fingerprint DRFP9Hydrogen Evolution Prediction for Alternating Conjugated Copolymers Enabled by Machine Learning with Multidimension Fragmentation Descriptors27Standardisierung und Kontrolle von Grignard‐Reaktionen mittels Online‐NMR in einer universellen chemischen Syntheseplattform1Standardization and Control of Grignard Reactions in a Universal Chemical Synthesis Machine using online NMR29Prediction of Multicomponent Reaction Yields Using Machine Learning33Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning78Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning6Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space69Teaching a neural network to attach and detach electrons from molecules126A reactivity map for oxidative addition enables quantitative predictions for multiple catalytic reaction classes0Highly accurate prediction of reaction performance and enantioselectivity with a uniform machine learning protocol0Prediction of Reaction Yield for Buchwald‐Hartwig Cross‐coupling Reactions Using Deep Learning23The Evolution of Data-Driven Modeling in Organic Chemistry185Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives21Kernel Methods for Predicting Yields of Chemical Reactions47Machine learning modelling of chemical reaction characteristics: yesterday, today, tomorrow13Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases37Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction142Featurization Strategies for Polymer Sequence or Composition Design by Machine Learning2Machine-Learning-Enabled Virtual Screening for Inhibitors of Lysine-Specific Histone Demethylase 111Chemical Reactivity Prediction: Current Methods and Different Application Areas17A machine learning approach for predicting the empirical polarity of organic solvents19A reactivity model for oxidative addition to palladium enables quantitative predictions for catalytic cross-coupling reactions63A transfer learning protocol for chemical catalysis using a recurrent neural network adapted from natural language processing37Featurization strategies for polymer sequence or composition design by machine learning96Low-cost prediction of molecular and transition state partition functions via machine learning8Molecular field analysis for data-driven molecular design in asymmetric catalysis14Predicting reaction conditions from limited data through active transfer learning78Reaction classification and yield prediction using the differential reaction fingerprint DRFP178The effect of chemical representation on active machine learning towards closed-loop optimization55Using Data Science To Guide Aryl Bromide Substrate Scope Analysis in a Ni/Photoredox-Catalyzed Cross-Coupling with Acetals as Alcohol-Derived Radical Sources191Predicting Solute Descriptors for Organic Chemicals by a Deep Neural Network (DNN) Using Basic Chemical Structures and a Surrogate Metric34Uncertainty-aware prediction of chemical reaction yields with graph neural networks76Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity38Regression Transformer enables concurrent sequence regression and generation for molecular language modelling6Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability, and explainability93ARTIFICIAL INTELLIGENCE IN PHARMACY DRUG DESIGN9Machine intelligence for chemical reaction space124Unified Deep Learning Model for Multitask Reaction Predictions with Explanation129Assessment of Predicting Frontier Orbital Energies for Small Organic Molecules Using Knowledge-Based and Structural Information16An Ensemble Structure and Physicochemical (SPOC) Descriptor for Machine‐Learning Prediction of Chemical Reaction and Molecular Properties35Machine Learning for Chemical Reactivity: The Importance of Failed Experiments217Maschinelles Lernen zur Vorhersage chemischer Reaktivität: Die Bedeutung “gescheiterter” Experimente8Machine learning predicts electrospray particle size58Recent advances in electroanalytical methods for electroorganic synthesis10Evaluation guidelines for machine learning tools in the chemical sciences180GraphRXN: A Novel Representation for Reaction Prediction0Advancing Base Metal Catalysis through Data Science: Insight and Predictive Models for Ni-Catalyzed Borylation through Supervised Machine Learning24Machine Learning Prediction of Structure‐Performance Relationship in Organic Synthesis16Computational Catalyst Design with Data–Driven Tools – General Approaches and Applications1Mechanistic Inference from Statistical Models at Different Data-Size Regimes37Deep learning for development of organic optoelectronic devices: efficient prescreening of hosts and emitters in deep-blue fluorescent OLEDs56Extended Connectivity Fingerprints as a Chemical Reaction Representation for Enantioselective Organophosphorus-Catalyzed Asymmetric Reaction Prediction25Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors1Machine Learning Yield Prediction from NiCOlit, a Small-Size Literature Data Set of Nickel Catalyzed C–O Couplings87In silico approach: Prediction of ADMET, molecular docking, and QSPR of secondary metabolites in Mangroves9Reaxtica: A knowledge-guided machine learning platform for fast and accurate reaction selectivity and yield prediction0Reaxtica: A knowledge-guided machine learning platform for fast and accurate reaction selectivity and yield prediction1Physics-based representations for machine learning properties of chemical reactions33Auto Machine Learning Assisted Preparation of Carboxylic Acid by TEMPO‐Catalyzed Primary Alcohol Oxidation19Roadmap to Pharmaceutically Relevant Reactivity Models Leveraging High-Throughput Experimentation17Selective functionalization of hindered meta-C–H bond of o-alkylaryl ketones promoted by automation and deep learning31A Combined DFT, Energy Decomposition, and Data Analysis Approach to Investigate the Relationship Between Noncovalent Interactions and Selectivity in a Flexible DABCOnium/Chiral Anion Catalyst System23Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis51Chemical transformer compression for accelerating both training and inference of molecular modeling1Advancing molecular graphs with descriptors for the prediction of chemical reaction yields20Expediting Chemical Enhanced Oil Recovery Processes with Prediction of Chemical Reaction Yield Using Machine Learning0High-Throughput Experimentation for Resource-Efficient Discovery of Methane Functionalization Catalysts1Prediction of Initial Reaction Characteristics of Materials from Molecular Conformational Changes Based on Artificial Intelligence Technology2Bayesian optimisation for additive screening and yieldimprovements in chemical reactions – beyond one-hot encodings2Accessing Diverse Azole Carboxylic Acid Building Blocks via Mild C–H Carboxylation: Parallel, One-Pot Amide Couplings and Machine-Learning-Guided Substrate Scope Design31ACCELERATING DRUG DISCOVERY BY HIGH-THROUGHPUT EXPERIMENTATION1Base-mediated C–B bond activation of benzylic boronate for the rapid construction of β-silyl/boryl functionalized 1,1-diarylalkanes from aromatic alkenes11Emerging Machine Learning Techniques in Predicting Adverse Drug Reactions2Inverse design of viral infectivity-enhancing peptide fibrils from continuous protein-vector embeddings16Machine learning methods in photochemistry and photophysics10nanoNET: machine learning platform for predicting nanoparticles distribution in a polymer matrix21On the use of real-world datasets for reaction yield prediction141Synthesize in a Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis1Δ-Machine learning for quantum chemistry prediction of solution-phase molecular properties at the ground and excited states26Machine Learning C–N Couplings: Obstacles for a General-Purpose Reaction Yield Prediction48Machine‐Learning Classification for the Prediction of Catalytic Activity of Organic Photosensitizers in the Nickel(II)‐Salt‐Induced Synthesis of Phenols39Machine‐Learning Classification for the Prediction of Catalytic Activity of Organic Photosensitizers in the Nickel(II)‐Salt‐Induced Synthesis of Phenols1A Review on Artificial Intelligence Enabled Design, Synthesis, and Process Optimization of Chemical Products for Industry 4.076Predict Ionization Energy of Molecules Using Conventional and Graph-Based Machine Learning Models15Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors**64Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors**2Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights24Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges88Molecular Understanding and Practical In Silico Catalyst Design in Computational Organocatalysis and Phase Transfer Catalysis—Challenges and Opportunities7Global reactivity models are impactful in industrial synthesis applications15Genetic Algorithms for the Discovery of Homogeneous Catalysts14A Brief Introduction to Chemical Reaction Optimization437Machine Learning in Unmanned Systems for Chemical Synthesis8Algorithm Design in Programming Language Education1An Overview of Academic Studies on Land Congression in Turkey0An Updated Overview of Non-Dairy Probiotic Beverages0Carbon nitride-based photocatalysts for hydrogen evolution: A review0Generative Artificial Intelligence Applications Specific to the Air Transport Industry2Investigation of the Effects of Different Cooling/Lubrication Conditions and Cutting Parameters on Energy Consumption During Milling of Hybrid Aluminum Composites0Machine-learning screening of luminogens with aggregation-induced emission characteristics for fluorescence imaging34The Design of Mechanisms via Artificial Intelligence13The Importance of the Thermal Properties of Biomass Components0A Machine Learning Approach to Model Interaction Effects: Development and Application to Alcohol Deoxyfluorination23Regression Transformer enables concurrent sequence regression and generation for molecular language modelling141Enhancing the nucleophilicity of benzylic boronates enables the divergent formation of C−C and C−X bonds0Machine learning approach to map the thermal conductivity of over 2,000 neoteric solvents for green energy storage applications68Bayesian optimisation for additive screening and yieldimprovements in chemical reactions – beyond one-hot encoding0Incorporating Domain Knowledge and Structure-Based Descriptors for Machine Learning: A Case Study of Pd-Catalyzed Sonogashira Reactions2Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit39

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close43 nodes / 53 citation links
Browse Matching Papers

Papers that cite this work

283 direct citing papers saved. Showing the first 50 here; use Browse Matching Papers in the map for all loaded papers.

  1. Machine learning the ropes: principles, applications and directions in synthetic chemistry

    Chemical Society Reviews · 2020-01-01

    Felix Strieth‐Kalthoff, Frederik Sandfort, Marwin Segler, Frank Glorius

    Read original paper ↗ ·

  2. PATENTOMETRIA: UMA FERRAMENTA INDISPENSÁVEL NO ESTUDO DE DESENVOLVIMENTO DE TECNOLOGIAS PARA A INDÚSTRIA QUÍMICA

    Química Nova · 2020-01-01

    Marcelo Gomes Speziali, Raphael Nascimento

    Read original paper ↗ ·

  3. What can reaction databases teach us about Buchwald–Hartwig cross-couplings?

    Chemical Science · 2020-01-01

    Martin Fitzner, Georg Wuitschik, Raffael Koller, Jean‐Michel Adam, Torsten Schindler, Jean‐Louis Reymond

    Read original paper ↗ ·

  4. Fingerprints of molecular reactivity

    Nature Reviews Chemistry · 2020-04-06

    Gabriella Graziano

    Read original paper ↗ ·

  5. Molecular Representation: Going Long on Fingerprints

    Chem · 2020-05-18

    Lagnajit Pattanaik, Connor W. Coley

    Read original paper ↗ ·

  6. Development of a Computer-Guided Workflow for Catalyst Optimization. Descriptor Validation, Subset Selection, and Training Set Analysis

    Journal of the American Chemical Society · 2020-06-22

    Jeremy J. Henle, Andrew F. Zahrt, Brennan T. Rose, William T. Darrow, Yang Wang, Scott E. Denmark

    Read original paper ↗ ·

  7. Electronic Properties and Photocatalytic Hydrogen Evolution Rates for Alternating Conjugated Copolymers: Predictions and Insights by Data-Driven Models

    ChemRxiv · 2020-09-04

    Yuzhi Xu, Cheng‐Wei Ju, Bo Li, Qiu-Shi Ma, Lianjie Zhang, Junwu Chen

    Read original paper ↗ ·

  8. Molecular Machine Learning: The Future of Synthetic Chemistry?

    Angewandte Chemie International Edition · 2020-09-15

    Philipp Miro Pflüger, Frank Glorius

    Read original paper ↗ ·

  9. Molekulares maschinelles Lernen: Die Zukunft der Synthesechemie?

    Angewandte Chemie · 2020-09-15

    Philipp Miro Pflüger, Frank Glorius

    Read original paper ↗ ·

  10. Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

    Chemical Science · 2020-11-05

    Kjell Jorner, Tore Brinck, Per‐Ola Norrby, David Buttar

    Read original paper ↗ ·

  11. Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset

    The Journal of Physical Chemistry A · 2020-11-11

    Gabriel A. Pinheiro, Johnatan Mucelini, Marinalva Dias Soares, Ronaldo Cristiano Prati, Juarez L. F. Da Silva, Marcos G. Quiles

    Read original paper ↗ ·

  12. Electro‐Descriptors for the Performance Prediction of Electro‐Organic Synthesis

    Angewandte Chemie International Edition · 2020-11-12

    Yuxuan Chen, Bailin Tian, Zheng Cheng, Xiaoshan Li, Min Joon Huang, Yuxia Sun, Shuai Liu, Xu Cheng, Shuhua Li, Mengning Ding

    Read original paper ↗ ·

  13. Electro‐Descriptors for the Performance Prediction of Electro‐Organic Synthesis

    Angewandte Chemie · 2020-11-12

    Yuxuan Chen, Bailin Tian, Zheng Cheng, Xiaoshan Li, Min Joon Huang, Yuxia Sun, Shuai Liu, Xu Cheng, Shuhua Li, Mengning Ding

    Read original paper ↗ ·

  14. Computational Methods for Training Set Selection and Error Assessment Applied to Catalyst Design: Guidelines for Deciding Which Reactions to Run First and Which to Run Next

    ChemRxiv · 2020-11-16

    Scott E. Denmark, Andrew F. Zahrt, William T. Darrow, Brennan T. Rose, Jeremy J. Henle

    Read original paper ↗ ·

  15. Data augmentation strategies to improve reaction yield predictions and estimate uncertainty

    ChemRxiv · 2020-11-26

    Philippe Schwaller, Alain C. Vaucher, Teodoro Laino, Jean‐Louis Reymond

    Read original paper ↗ ·

  16. Data augmentation strategies to improve reaction yield predictions and estimate uncertainty

    ChemRxiv · 2020-11-26

    Philippe Schwaller, Alain C. Vaucher, Teodoro Laino, Jean‐Louis Reymond

    Read original paper ↗ ·

  17. Intelligent Microfluidics: The Convergence of Machine Learning and Microfluidics in Materials Science and Biomedicine

    Matter · 2020-12-01

    Edgar A. Galan, Haoran Zhao, Xukang Wang, Qionghai Dai, Wilhelm T. S. Huck, Shaohua Ma

    Read original paper ↗ ·

  18. Improvement of Prediction Performance With Conjoint Molecular Fingerprint in Deep Learning

    Frontiers in Pharmacology · 2020-12-18

    Liangxu Xie, Lei Xu, Ren Kong, Shan Chang, Xiaojun Xu

    Read original paper ↗ ·

  19. Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors

    Chemical Science · 2020-12-22

    Yanfei Guan, Connor W. Coley, Haoyang Wu, Duminda S. Ranasinghe, Esther Heid, Thomas J. Struble, Lagnajit Pattanaik, William H. Green, Klavs F. Jensen

    Read original paper ↗ ·

  20. Chemical data intelligence for sustainable chemistry

    Chemical Society Reviews · 2021-01-01

    Jana M. Weber, Zhen Guo, Chonghuan Zhang, Artur M. Schweidtmann, Alexei A. Lapkin

    Read original paper ↗ ·

  21. Computational methods for training set selection and error assessment applied to catalyst design: guidelines for deciding which reactions to run first and which to run next

    Reaction Chemistry & Engineering · 2021-01-01

    Andrew F. Zahrt, Brennan T. Rose, William T. Darrow, Jeremy J. Henle, Scott E. Denmark

    Read original paper ↗ ·

  22. DeepReac+: deep active learning for quantitative modeling of organic chemical reactions

    Chemical Science · 2021-01-01

    Yukang Gong, Dongyu Xue, Guohui Chuai, Jing Yu, Qi Liu

    Read original paper ↗ ·

  23. Machine learning prediction of hydrogen atom transfer reactivity in photoredox-mediated C–H functionalization

    Organic Chemistry Frontiers · 2021-01-01

    Li‐Cheng Yang, Xin Li, Shuo‐Qing Zhang, Xin Hong

    Read original paper ↗ ·

  24. Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts

    Chemical Science · 2021-01-01

    Simone Gallarati, Raimón Fabregat, Rubén Laplaza, Sinjini Bhattacharjee, Matthew D. Wodrich, Clémence Corminbœuf

    Read original paper ↗ ·

  25. Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries

    Conference on Empirical Methods in Natural Language Processing (EMNLP) · 2021-01-01

    Carl Edwards, ChengXiang Zhai, Heng Ji

    Read original paper ↗ ·

  26. Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases

    Wiley Interdisciplinary Reviews Computational Molecular Science · 2021-01-05

    José Peña Guerrero, Paul Nguewa, Alfonso T. García‐Sosa

    Read original paper ↗ ·

  27. Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning

    Trends in Chemistry · 2021-01-14

    Gabe Gomes, Robert Pollice, Alán Aspuru‐Guzik

    Read original paper ↗ ·

  28. Mapping the space of chemical reactions using attention-based neural networks

    Nature Machine Intelligence · 2021-01-28

    Philippe Schwaller, Daniel Probst, Alain C. Vaucher, Vishnu H Nair, David Kreutter, Teodoro Laino, Jean‐Louis Reymond

    Read original paper ↗ ·

  29. Permutationally Invariant Deep Learning Approach to Molecular Fingerprinting with Application to Compound Mixtures

    Journal of Chemical Information and Modeling · 2021-02-04

    Andrei Buin, Hung Yi Chiang, S. Andrew Gadsden, Faraz Alderson

    Read original paper ↗ ·

  30. Cross-validation strategies in QSPR modelling of chemical reactions

    SAR and QSAR in environmental research · 2021-02-19

    Assima Rakhimbekova, Tagir N. Akhmetshin, Guzel Minibaeva, Нугманов Рамиль Ирекович, Timur Rustemovich Gimadiev, Timur Madzhidov, Igor I. Baskin, Alexandre A. Varnek

    Read original paper ↗ ·

  31. Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields

    Journal of Chemical Information and Modeling · 2021-02-23

    Cheng‐Wei Ju, Hanzhi Bai, Bo Li, Rizhang Liu

    Read original paper ↗ ·

  32. Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning

    Journal of Chemical Information and Modeling · 2021-02-25

    Ziteng Liu, Liqiang Lin, Qingqing Jia, Zheng Cheng, Yanyan Jiang, Yanwen Guo, Jing Ma

    Read original paper ↗ ·

  33. Prediction of chemical reaction yields using deep learning

    Machine Learning Science and Technology · 2021-03-01

    Philippe Schwaller, Alain C. Vaucher, Teodoro Laino, Jean‐Louis Reymond

    Read original paper ↗ ·

  34. Organic reactivity from mechanism to machine learning

    Nature Reviews Chemistry · 2021-03-16

    Kjell Jorner, Anna Tomberg, Christoph Alexander Bauer, Christian Sköld, Per‐Ola Norrby

    Read original paper ↗ ·

  35. Automation and computer-assisted planning for chemical synthesis

    Nature Reviews Methods Primers · 2021-03-18

    Yuning Shen, Julia E. Borowski, Melissa A. Hardy, Richmond Sarpong, Abigail G. Doyle, Tim Cernak

    Read original paper ↗ ·

  36. Predicting Reaction Yields via Supervised Learning

    Accounts of Chemical Research · 2021-03-31

    Andrzej M. Żurański, Jesus I. Martinez Alvarado, Benjamin J. Shields, Abigail G. Doyle

    Read original paper ↗ ·

  37. Kernel Methods for Predicting Yields of Chemical Reactions

    ChemRxiv · 2021-04-01

    Alexe L. Haywood, Joseph Redshaw, Magnus W. D. Hanson‐Heine, Adam Taylor, Alex Brown, Andy Mason, Thomas Gaertner, Jonathan D. Hirst

    Read original paper ↗ ·

  38. Dreams, False Starts, Dead Ends, and Redemption: A Chronicle of the Evolution of a Chemoinformatic Workflow for the Optimization of Enantioselective Catalysts

    Accounts of Chemical Research · 2021-04-15

    N. Ian Rinehart, Andrew F. Zahrt, Jeremy J. Henle, Scott E. Denmark

    Read original paper ↗ ·

  39. A Conformer‐Dependent, Quantitative Quadrant Model

    European Journal of Organic Chemistry · 2021-04-21

    Andrew F. Zahrt, N. Ian Rinehart, Scott E. Denmark

    Read original paper ↗ ·

  40. Quantum Machine Learning Algorithms for Drug Discovery Applications

    Journal of Chemical Information and Modeling · 2021-05-25

    Kushal Batra, Kimberley M. Zorn, Daniel H. Foil, Eni Minerali, Victor Oliveira Gawriljuk, Thomas R. Lane, Sean Ekins

    Read original paper ↗ ·

  41. Reaction Classification and Yield Prediction using the Differential Reaction Fingerprint DRFP

    Unknown journal · 2021-07-06

    Daniel Probst, Philippe Schwaller, Jean‐Louis Reymond

    Read original paper ↗ ·

  42. Hydrogen Evolution Prediction for Alternating Conjugated Copolymers Enabled by Machine Learning with Multidimension Fragmentation Descriptors

    ACS Applied Materials & Interfaces · 2021-07-16

    Yuzhi Xu, Cheng‐Wei Ju, Bo Li, Qiu-Shi Ma, Zhenyu Chen, Lianjie Zhang, Junwu Chen

    Read original paper ↗ ·

  43. Standardisierung und Kontrolle von Grignard‐Reaktionen mittels Online‐NMR in einer universellen chemischen Syntheseplattform

    Angewandte Chemie · 2021-07-19

    Martin Bornemann‐Pfeiffer, Jakob B. Wolf, Klas Meyer, Simon Kern, Davide Angelone, Artem I. Leonov, Leroy Cronin, Franziska Emmerling

    Read original paper ↗ ·

  44. Standardization and Control of Grignard Reactions in a Universal Chemical Synthesis Machine using online NMR

    Angewandte Chemie International Edition · 2021-07-19

    Martin Bornemann‐Pfeiffer, Jakob B. Wolf, Klas Meyer, Simon Kern, Davide Angelone, Artem I. Leonov, Leroy Cronin, Franziska Emmerling

    Read original paper ↗ ·

  45. Prediction of Multicomponent Reaction Yields Using Machine Learning

    Chinese Journal of Chemistry · 2021-08-04

    Xingyong Zhu, Chuan‐Kun Ran, Ming Fen Wen, Gui‐Ling Guo, Yuan Liu, Li‐Li Liao, Yi‐Zhou Li, Meng‐Long Li, Da‐Gang Yu

    Read original paper ↗ ·

  46. Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning

    Angewandte Chemie International Edition · 2021-08-09

    Li‐Cheng Xu, Shuo‐Qing Zhang, Xin Li, Miao‐Jiong Tang, Pei‐Pei Xie, Xin Hong

    Read original paper ↗ ·

  47. Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning

    Angewandte Chemie · 2021-08-09

    Li‐Cheng Xu, Shuo‐Qing Zhang, Xin Li, Miao‐Jiong Tang, Pei‐Pei Xie, Xin Hong

    Read original paper ↗ ·

  48. Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space

    The Journal of Chemical Physics · 2021-08-10

    Stefan Heinen, Guido Falk von Rudorff, O. Anatole von Lilienfeld

    Read original paper ↗ ·

  49. Teaching a neural network to attach and detach electrons from molecules

    Nature Communications · 2021-08-11

    R.I. Zubatyuk, Justin S. Smith, Benjamin T. Nebgen, Sergei Tretiak, Olexandr Isayev

    Read original paper ↗ ·

  50. A reactivity map for oxidative addition enables quantitative predictions for multiple catalytic reaction classes

    Unknown journal · 2021-08-26

    Jingru Lu, Sofia Donnecke, Irina Paci, David C. Leitch

    Read original paper ↗ ·
More citing papers →
Citation Atlas · A map of research, not a ranking.