BetaCitation Atlas

RESEARCH PAPER · Scientific Reports · 2017

Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?

Grzegorz Skoraczyński, Piotr Dittwald, Błażej Miasojedow, Sara Szymkuć, E. P. Gajewska, Bartosz A. Grzybowski, Anna Gambin

Read original paper ↗

See where this research leads.

150 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

Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?OriginalAutomating drug discovery895Towards operando computational modeling in heterogeneous catalysis250Discovery and Enumeration of Organic‐Chemical and Biomimetic Reaction Cycles within the Network of Chemistry18Discovery and Enumeration of Organic‐Chemical and Biomimetic Reaction Cycles within the Network of Chemistry5Predicting reaction performance in C–N cross-coupling using machine learning1050Feasibility of Activation Energy Prediction of Gas‐Phase Reactions by Machine Learning56Machine Learning for Organic Synthesis: Are Robots Replacing Chemists?74Maschinelles Lernen für die organische Synthese: Ersetzen Roboter Chemiker?8Machine Learning in Computer-Aided Synthesis Planning703Linguistic measures of chemical diversity and the “keywords” of molecular collections53Determining influential descriptors for polymer chain conformation based on empirical force-fields and molecular dynamics simulations37Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies42Toward Predicting Efficiency of Organic Solar Cells via Machine Learning and Improved Descriptors258Predictive models for kinetic and thermodynamic parameters of reactions0Concluding Remarks and Perspectives0Prediction of Chemical Reactions Using Statistical Models of Chemical Knowledge4Prediction of Chemical Reactions Using Statistical Models of Chemical Knowledge0Prediction of Major Regio‐, Site‐, and Diastereoisomers in Diels–Alder Reactions by Using Machine‐Learning: The Importance of Physically Meaningful Descriptors146Prediction of Major Regio‐, Site‐, and Diastereoisomers in Diels–Alder Reactions by Using Machine‐Learning: The Importance of Physically Meaningful Descriptors22Improving Vibrational Mode Interpretation Using Bayesian Regression106Computational design of syntheses leading to compound libraries or isotopically labelled targets29Designing promising molecules for organic solar cells via machine learning assisted virtual screening115Reaction Conditions Data Mining : The application of Machine Learning towards predicting the future of process development0Selection of cost-effective yet chemically diverse pathways from the networks of computer-generated retrosynthetic plans75Machine Learning for Understanding Compatibility of Organic–Inorganic Hybrid Perovskites with Post-Treatment Amines112Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning678An Introduction to Closed‐Loop Process Optimization and Online Analysis3Computational Ligand Descriptors for Catalyst Design406Enhancing Acute Oral Toxicity Predictions by using Consensus Modeling and Algebraic Form-Based 0D-to-2D Molecular Encodes29Identifying Physico-Chemical Laws from the Robotically Collected Data0Identifying Physico-Chemical Laws from the Robotically Collected Data0Synthetic organic chemistry driven by artificial intelligence303Rational Design of Continuous Flow Processes for Synthesis of Functional Molecules2A new formulation for symbolic regression to identify physico-chemical laws from experimental data64Quantitative Structure–Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future211Digitising chemical synthesis in automated and robotic flow49A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation131Applying machine learning algorithms in estimating the performance of heterogeneous, multi-component materials as oxygen carriers for chemical-looping processes87Identification of the Side Products That Diminish the Yields of the Monoamidated Product in Metal-Catalyzed C–H Amidation of 2-Phenylpyridine with Arylisocyanates11Quantum Chemical Reaction Prediction Method Based on Machine Learning24A Structure-Based Platform for Predicting Chemical Reactivity288Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning130Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning24Learning Molecular Representations for Medicinal Chemistry222Machine Learning to Reduce Reaction Optimization Lead Time – Proof of Concept with Suzuki, Negishi and Buchwald-Hartwig Cross-Coupling Reactions4Machine Learning to Reduce Reaction Optimization Lead Time – Proof of Concept with Suzuki, Negishi and Buchwald-Hartwig Cross-Coupling Reactions1Models of necessity13Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques5Peer Review #1 of "Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques (v0.1)"0Peer Review #2 of "Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques (v0.1)"0Peer Review #3 of "Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques (v0.1)"0Peer Review #3 of "Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques (v0.2)"0Peer Review #4 of "Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques (v0.1)"0Peer Review #4 of "Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques (v0.2)"0Computational planning of the synthesis of complex natural products271Chemical data intelligence for sustainable chemistry68Machine learning approaches to understand and predict rate constants for organic processes in mixtures containing ionic liquids27Machine learning prediction of hydrogen atom transfer reactivity in photoredox-mediated C–H functionalization33Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts105Multilabel Classification Models for the Prediction of Cross-Coupling Reaction Conditions91Transformative Applications of Machine Learning for Chemical Reactions4Chemist Ex Machina: Advanced Synthesis Planning by Computers47Prediction of chemical reaction yields using deep learning291Organic reactivity from mechanism to machine learning175Scaffold‐Directed Face Selectivity Machine‐Learned from Vectors of Non‐covalent Interactions42Scaffold‐Directed Face Selectivity Machine‐Learned from Vectors of Non‐covalent Interactions15Virtual Screening for Chemists0Graph Neural Networks for Predicting Chemical Reaction Performance1Machine Learning for Chemical Reactions437Graph Neural Networks for Predicting Chemical Reaction Performance4Computational Analysis of Synthetic Planning: Past and Future19Reaction Classification and Yield Prediction using the Differential Reaction Fingerprint DRFP9An optimized deep convolutional neural network for yield prediction of Buchwald-Hartwig amination5Leveraging Machine Learning for Enantioselective Catalysis: From Dream to Reality6Reaction Data Curation I: Chemical Structures and Transformations Standardization36Reactions’ Descriptors Selection and Yield Estimation Using Metaheuristic Algorithms and Voting Ensemble1Role of artificial intelligence in chemistry26Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives21Machine learning modelling of chemical reaction characteristics: yesterday, today, tomorrow13Quantum computing enhanced machine learning for physico-chemical applications2Application of Machine Learning and Reaction Optimization for the Iterative Improvement of Enantioselectivity of Cinchona-Derived Phase Transfer Catalysts27Chemical Reactivity Prediction: Current Methods and Different Application Areas17A transfer learning protocol for chemical catalysis using a recurrent neural network adapted from natural language processing37N-SVRG: Stochastic Variance Reduction Gradient with Noise Reduction Ability for Small Batch Samples3Quantum machine learning for chemistry and physics151Reaction classification and yield prediction using the differential reaction fingerprint DRFP178The effect of chemical representation on active machine learning towards closed-loop optimization55Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki–Miyaura Coupling189Computer-designed repurposing of chemical wastes into drugs86Machine Learning for Chemical Reactivity: The Importance of Failed Experiments217Maschinelles Lernen zur Vorhersage chemischer Reaktivität: Die Bedeutung “gescheiterter” Experimente8Machine Learning Prediction of Structure‐Performance Relationship in Organic Synthesis16Network search algorithms and scoring functions for advanced‐level computerized synthesis planning18Mechanistic Inference from Statistical Models at Different Data-Size Regimes37Artificial Intelligence for Retrosynthesis Prediction92Reaxtica: 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 prediction1Advancing molecular graphs with descriptors for the prediction of chemical reaction yields20DNN-PP: A novel Deep Neural Network approach and its applicability in drug-related property prediction25Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling208Artificial intelligence in oncological therapies2Inventorship in the Age of Artificial Intelligence2On the use of real-world datasets for reaction yield prediction141Synthesize in a Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis1Machine Learning C–N Couplings: Obstacles for a General-Purpose Reaction Yield Prediction48Critical assessment of synthetic accessibility scores in computer-assisted synthesis planning103Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights24Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges88Global reactivity models are impactful in industrial synthesis applications15A Machine Learning Approach to Model Interaction Effects: Development and Application to Alcohol Deoxyfluorination23Toxicity prediction using target, interactome, and pathway profiles as descriptors21Transform Descriptors for template free reaction classification1A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data46Extraction Yield Prediction for the Large-Scale Recovery of Botanicals1Machine learning approach for predicting the yield of pyrroles and dipyrromethanes condensation reactions with aldehydes13Extraction yield prediction for the large-scale recovery of cannabinoids6A machine learning approach for predicting the reactivity power of hypervalent iodine compounds7Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR397When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges64Advancing 2D material predictions: superior work function estimation with atomistic line graph neural networks8Every atom counts: predicting sites of reaction based on chemistry within two bonds0Navigating the landscape of enzyme design: from molecular simulations to machine learning155Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge34Computational synthesis design for controlled degradation and revalorization9Application and Prospect of Machine Learning in the Field of Materials and Chemical Engineering1Reproducing Reaction Mechanisms with Machine‐Learning Models Trained on a Large‐Scale Mechanistic Dataset13Reproducing Reaction Mechanisms with Machine‐Learning Models Trained on a Large‐Scale Mechanistic Dataset46‐Hydroxy Picolinohydrazides Promoted Cu(I)‐Catalyzed Hydroxylation Reaction in Water: Machine‐Learning Accelerated Ligands Design and Reaction Optimization46‐Hydroxy Picolinohydrazides Promoted Cu(I)‐Catalyzed Hydroxylation Reaction in Water: Machine‐Learning Accelerated Ligands Design and Reaction Optimization24Leveraging graph neural networks to predict Hammett’s constants for benzoic acid derivatives7Estimation of multicomponent reactions’ yields from networks of mechanistic steps12Systematic, computational discovery of multicomponent and one-pot reactions24Predicting reaction conditions: a data-driven perspective10Predicting and Explaining Yields with Machine Learning for Carboxylated Azoles and Beyond6C–H Aminoalkylation of 5-Membered Heterocycles: Influence of Descriptors, Data Set Size, and Data Quality on the Predictiveness of Machine Learning Models and Expansion of the Substrate Space Beyond 1,3-Azoles1Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions2A molecular descriptor-based correlation with the composition of acid-pretreated cornstalk cultivation medium for biohydrogen production using a machine learning approach6Exploring BERT for Reaction Yield Prediction: Evaluating the Impact of Tokenization, Molecular Representation, and Pretraining Data Augmentation9Predicting Reactivity with Machine Learning1Inter‐Hammett: Enhancing Interpretability in Hammett‘s Constant Prediction via Extracting Rules1Graph Neural Networks in Modern AI-Aided Drug Discovery87Robot-assisted mapping of chemical reaction hyperspaces and networks15Prioritizing Data Quality in Machine Learning for Thermophysical Property Prediction: A Case Study on Normal Boiling Points of Organic Compounds2Design and Experimental Validation of a Photocatalyst Recommender Based on a Large Language Model0Design and Experimental Validation of a Photocatalyst Recommender Based on a Large Language Model6Battery real-time power monitoring and control in automotive embedded systems0TransGAT-DTA: A multi-task framework for drug–target affinity prediction and conditional molecule generation1Multi-to-uni modal knowledge transfer pre-training for molecular representation learning5ReactionSeek: LLM-powered literature data mining and knowledge discovery in organic synthesis7A dynamic patching LLM framework for adaptive yield trend prediction0

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

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

  1. Automating drug discovery

    Nature Reviews Drug Discovery · 2017-12-15

    Gisbert Schneider

    Read original paper ↗ ·

  2. Towards operando computational modeling in heterogeneous catalysis

    Chemical Society Reviews · 2018-01-01

    Lukáš Grajciar, Christopher James Heard, A. A. Bondarenko, Mikhail V. Polynski, Jittima Meeprasert, Evgeny A. Pidko, Petr Nachtigall

    Read original paper ↗ ·

  3. Discovery and Enumeration of Organic‐Chemical and Biomimetic Reaction Cycles within the Network of Chemistry

    Angewandte Chemie International Edition · 2018-02-06

    Michał Dariusz Bajczyk, Piotr Dittwald, Agnieszka Wołos, Sara Szymkuć, Bartosz A. Grzybowski

    Read original paper ↗ ·

  4. Discovery and Enumeration of Organic‐Chemical and Biomimetic Reaction Cycles within the Network of Chemistry

    Angewandte Chemie · 2018-02-06

    Michał Dariusz Bajczyk, Piotr Dittwald, Agnieszka Wołos, Sara Szymkuć, Bartosz A. Grzybowski

    Read original paper ↗ ·

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

    Science · 2018-02-15

    Derek T. Ahneman, Jesús G. Estrada, Shishi Lin, Spencer D. Dreher, Abigail G. Doyle

    Read original paper ↗ ·

  6. Feasibility of Activation Energy Prediction of Gas‐Phase Reactions by Machine Learning

    Chemistry - A European Journal · 2018-02-23

    Sunghwan Choi, Yeonjoon Kim, Jin Woo Kim, Zeehyo Kim, Woo Youn Kim

    Read original paper ↗ ·

  7. Machine Learning for Organic Synthesis: Are Robots Replacing Chemists?

    Angewandte Chemie International Edition · 2018-04-27

    Boris Maryasin, Philipp Marquetand, Nuno Maulide

    Read original paper ↗ ·

  8. Maschinelles Lernen für die organische Synthese: Ersetzen Roboter Chemiker?

    Angewandte Chemie · 2018-04-27

    Boris Maryasin, Philipp Marquetand, Nuno Maulide

    Read original paper ↗ ·

  9. Machine Learning in Computer-Aided Synthesis Planning

    Accounts of Chemical Research · 2018-05-01

    Connor W. Coley, William H. Green, Klavs F. Jensen

    Read original paper ↗ ·

  10. Linguistic measures of chemical diversity and the “keywords” of molecular collections

    Scientific Reports · 2018-05-09

    Michał Woźniak, Agnieszka Wołos, Urszula Modrzyk, Rafał L. Górski, Jan Winkowski, Michał Dariusz Bajczyk, Sara Szymkuć, Bartosz A. Grzybowski, Maciej Eder

    Read original paper ↗ ·

  11. Determining influential descriptors for polymer chain conformation based on empirical force-fields and molecular dynamics simulations

    Chemical Physics Letters · 2018-05-16

    Ruimin Ma, Dezhao Huang, Teng Zhang, Tengfei Luo

    Read original paper ↗ ·

  12. Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies

    Journal of Chemical Information and Modeling · 2018-06-13

    Lorena Simón-Vidal, Oihane García‐Calvo, Uxue Oteo, Sonia Arrasate, Esther Lete, Nuria Sotomayor, Humberto González‐Díaz

    Read original paper ↗ ·

  13. Toward Predicting Efficiency of Organic Solar Cells via Machine Learning and Improved Descriptors

    Advanced Energy Materials · 2018-07-02

    Harikrishna Sahu, Weining Rao, Alessandro Troisi, Haibo Ma

    Read original paper ↗ ·

  14. Predictive models for kinetic and thermodynamic parameters of reactions

    Unknown journal · 2018-07-11

    Timur Rustemovich Gimadiev

    Read original paper ↗ ·

  15. Concluding Remarks and Perspectives

    Unknown journal · 2018-08-06

    Pei‐Qiang Huang, Richard P. Hsung, Zhi‐Xiong Ma, Zhu‐Jun Yao

    Read original paper ↗ ·

  16. Prediction of Chemical Reactions Using Statistical Models of Chemical Knowledge

    ChemRxiv · 2018-08-10

    Philipp‐Maximilian Jacob, Alexei A. Lapkin

    Read original paper ↗ ·

  17. Prediction of Chemical Reactions Using Statistical Models of Chemical Knowledge

    ChemRxiv · 2018-08-10

    Philipp‐Maximilian Jacob, Alexei A. Lapkin

    Read original paper ↗ ·

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

    Angewandte Chemie International Edition · 2018-11-06

    Wiktor Beker, Ewa P. Gajewska, Tomasz Badowski, Bartosz A. Grzybowski

    Read original paper ↗ ·

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

    Angewandte Chemie · 2018-11-06

    Wiktor Beker, Ewa P. Gajewska, Tomasz Badowski, Bartosz A. Grzybowski

    Read original paper ↗ ·

  20. Improving Vibrational Mode Interpretation Using Bayesian Regression

    Journal of Chemical Theory and Computation · 2018-12-10

    Filipe Teixeira, M. Natália D. S. Cordeiro

    Read original paper ↗ ·

  21. Computational design of syntheses leading to compound libraries or isotopically labelled targets

    Chemical Science · 2019-01-01

    Karol Molga, Piotr Dittwald, Bartosz A. Grzybowski

    Read original paper ↗ ·

  22. Designing promising molecules for organic solar cells via machine learning assisted virtual screening

    Journal of Materials Chemistry A · 2019-01-01

    Harikrishna Sahu, Feng Yang, Xiaobo Ye, Jing Ma, Wei‐Hai Fang, Haibo Ma

    Read original paper ↗ ·

  23. Reaction Conditions Data Mining : The application of Machine Learning towards predicting the future of process development

    KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2019-01-01

    Samuel Hallinder

    Read original paper ↗ ·

  24. Selection of cost-effective yet chemically diverse pathways from the networks of computer-generated retrosynthetic plans

    Chemical Science · 2019-01-01

    Tomasz Badowski, Karol Molga, Bartosz A. Grzybowski

    Read original paper ↗ ·

  25. Machine Learning for Understanding Compatibility of Organic–Inorganic Hybrid Perovskites with Post-Treatment Amines

    ACS Energy Letters · 2019-01-04

    Yongze Yu, Xuanheng Tan, Shougui Ning, Yiying Wu

    Read original paper ↗ ·

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

    Science · 2019-01-17

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

    Read original paper ↗ ·

  27. An Introduction to Closed‐Loop Process Optimization and Online Analysis

    Unknown journal · 2019-01-24

    Christopher S. Horbaczewskyj, Charlotte E. Willans, Alexei A. Lapkin, Richard A. Bourne

    Read original paper ↗ ·

  28. Computational Ligand Descriptors for Catalyst Design

    Chemical Reviews · 2019-02-25

    Derek J. Durand, Natalie Fey

    Read original paper ↗ ·

  29. Enhancing Acute Oral Toxicity Predictions by using Consensus Modeling and Algebraic Form-Based 0D-to-2D Molecular Encodes

    Chemical Research in Toxicology · 2019-05-08

    César R. García‐Jacas, Yovani Marrero‐Ponce, Fernando Cortés‐Guzmán, José Suárez‐Lezcano, Félix Martínez-Rios, Luis A. García‐González, Mario Pupo-Meriño, Karina Martínez‐Mayorga

    Read original paper ↗ ·

  30. Identifying Physico-Chemical Laws from the Robotically Collected Data

    ChemRxiv · 2019-07-03

    Liwei Cao, Danilo Russo, Vassilios S. Vassiliadis, Alexei A. Lapkin

    Read original paper ↗ ·

  31. Identifying Physico-Chemical Laws from the Robotically Collected Data

    ChemRxiv · 2019-07-03

    Liwei Cao, Danilo Russo, Vassilios S. Vassiliadis, Alexei A. Lapkin

    Read original paper ↗ ·

  32. Synthetic organic chemistry driven by artificial intelligence

    Nature Reviews Chemistry · 2019-08-21

    A. Filipa Almeida, Rui Moreira, Tiago Rodrigues

    Read original paper ↗ ·

  33. Rational Design of Continuous Flow Processes for Synthesis of Functional Molecules

    Elsevier eBooks · 2019-10-18

    Alexei A. Lapkin

    Read original paper ↗ ·

  34. A new formulation for symbolic regression to identify physico-chemical laws from experimental data

    Chemical Engineering Journal · 2019-11-08

    Pascal Neumann, Liwei Cao, Danilo Russo, Vassilios S. Vassiliadis, Alexei A. Lapkin

    Read original paper ↗ ·

  35. Quantitative Structure–Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future

    Chemical Reviews · 2019-12-30

    Andrew F. Zahrt, Soumitra V. Athavale, Scott E. Denmark

    Read original paper ↗ ·

  36. Digitising chemical synthesis in automated and robotic flow

    Chemical Science · 2020-01-01

    Tomas Hardwick, Nisar Ahmed

    Read original paper ↗ ·

  37. A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation

    Proceedings of the National Academy of Sciences · 2020-01-08

    Sukriti Singh, Monika Pareek, Avtar Changotra, Sayan Banerjee, Bangaru Bhaskararao, P. Balamurugan, Raghavan B. Sunoj

    Read original paper ↗ ·

  38. Applying machine learning algorithms in estimating the performance of heterogeneous, multi-component materials as oxygen carriers for chemical-looping processes

    Chemical Engineering Journal · 2020-01-09

    Yongliang Yan, Tobias Mattisson, Patrick Moldenhauer, Edward John Anthony, Peter T. Clough

    Read original paper ↗ ·

  39. Identification of the Side Products That Diminish the Yields of the Monoamidated Product in Metal-Catalyzed C–H Amidation of 2-Phenylpyridine with Arylisocyanates

    The Journal of Organic Chemistry · 2020-01-23

    Alasdair I. McKay, Weam A. O. Altalhi, Lachlan E. McInnes, Milena L. Czyz, Allan J. Canty, Paul S. Donnelly, Richard A. J. O’Hair

    Read original paper ↗ ·

  40. Quantum Chemical Reaction Prediction Method Based on Machine Learning

    Bulletin of the Chemical Society of Japan · 2020-03-12

    Mikito Fujinami, Junji Seino, Hiromi Nakai

    Read original paper ↗ ·

  41. A Structure-Based Platform for Predicting Chemical Reactivity

    Chem · 2020-03-17

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

    Read original paper ↗ ·

  42. Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning

    Angewandte Chemie International Edition · 2020-05-02

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

    Read original paper ↗ ·

  43. Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning

    Angewandte Chemie · 2020-05-02

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

    Read original paper ↗ ·

  44. Learning Molecular Representations for Medicinal Chemistry

    Journal of Medicinal Chemistry · 2020-05-04

    Kangway V. Chuang, Laura M. Gunsalus, Michael J. Keiser

    Read original paper ↗ ·

  45. Machine Learning to Reduce Reaction Optimization Lead Time – Proof of Concept with Suzuki, Negishi and Buchwald-Hartwig Cross-Coupling Reactions

    ChemRxiv · 2020-07-06

    Fernando F. Huerta, Samuel Hallinder, Alexander B. E. Minidis

    Read original paper ↗ ·

  46. Machine Learning to Reduce Reaction Optimization Lead Time – Proof of Concept with Suzuki, Negishi and Buchwald-Hartwig Cross-Coupling Reactions

    ChemRxiv · 2020-07-07

    Fernando F. Huerta, Samuel Hallinder, Alexander B. E. Minidis

    Read original paper ↗ ·

  47. Models of necessity

    Beilstein Journal of Organic Chemistry · 2020-07-13

    Timothy Clark, Martin G. Hicks

    Read original paper ↗ ·

  48. Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques

    PeerJ Computer Science · 2020-07-27

    Alba Arceo-Vilas, Carlos Fernandez-Lozano, Salvador Pita, Sónia Pértega-Díaz, Alejandro Pazos

    Read original paper ↗ ·

  49. Peer Review #1 of "Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques (v0.1)"

    Unknown journal · 2020-07-27

    Read original paper ↗ ·

  50. Peer Review #2 of "Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques (v0.1)"

    Unknown journal · 2020-07-27

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