Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery

Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks

(2022) Philipp Seidl et al.   Journal of Chemical Information and Modeling

Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability, and explainability

(2022) Thijs Stuyver et al.   JOURNAL OF CHEMICAL PHYSICS

A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

(2022) Tobias Gensch et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Using Data Science To Guide Aryl Bromide Substrate Scope Analysis in a Ni/Photoredox-Catalyzed Cross-Coupling with Acetals as Alcohol-Derived Radical Sources

(2022) Stavros K. Kariofillis et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

HobPre: accurate prediction of human oral bioavailability for small molecules

(2022) Min Wei et al.   Journal of Cheminformatics

Routescore: Punching the Ticket to More Efficient Materials Development

(2022) Martin Seifrid et al.   ACS Central Science

Machine Learning for Chemical Reactivity The Importance of Failed Experiments

(2022) Felix Strieth-Kalthoff et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Unified Deep Learning Model for Multitask Reaction Predictions with Explanation

(2022) Jieyu Lu et al.   Journal of Chemical Information and Modeling

AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge

(2022) Shoichi Ishida et al.   Journal of Chemical Information and Modeling

RetroGNN: Fast Estimation of Synthesizability for Virtual Screening and De Novo Design by Learning from Slow Retrosynthesis Software

(2022) Cheng-Hao Liu et al.   Journal of Chemical Information and Modeling

Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki–Miyaura Coupling

(2022) Wiktor Beker et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Permutation Invariant Graph-to-Sequence Model for Template-Free Retrosynthesis and Reaction Prediction

(2022) Zhengkai Tu et al.   Journal of Chemical Information and Modeling

Machine Learning Yield Prediction from NiCOlit, a Small-Size Literature Data Set of Nickel Catalyzed C–O Couplings

(2022) Jules Schleinitz et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction

(2022) Simon Viet Johansson et al.   Molecular Informatics

Valid, Plausible, and Diverse Retrosynthesis Using Tied Two-Way Transformers with Latent Variables

(2021) Eunji Kim et al.   Journal of Chemical Information and Modeling

Multilabel Classification Models for the Prediction of Cross-Coupling Reaction Conditions

(2021) Michael R. Maser et al.   Journal of Chemical Information and Modeling

Substructure-based neural machine translation for retrosynthetic prediction

(2021) Umit V. Ucak et al.   Journal of Cheminformatics

Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties

(2021) Liliana C. Gallegos et al.   ACCOUNTS OF CHEMICAL RESEARCH

The Genesis of Molecular Volcano Plots

(2021) Matthew D. Wodrich et al.   ACCOUNTS OF CHEMICAL RESEARCH

Single-Step Retrosynthesis Prediction Based on the Identification of Potential Disconnection Sites Using Molecular Substructure Fingerprints

(2021) Haris Hasic et al.   Journal of Chemical Information and Modeling

Bayesian reaction optimization as a tool for chemical synthesis

(2021) Benjamin J. Shields et al.   NATURE

Cross-validation strategies in QSPR modelling of chemical reactions

(2021) A. Rakhimbekova et al.   SAR AND QSAR IN ENVIRONMENTAL RESEARCH

Discovery of novel chemical reactions by deep generative recurrent neural network

(2021) William Bort et al.   Scientific Reports

Machine learning of free energies in chemical compound space using ensemble representations: Reaching experimental uncertainty for solvation

(2021) Jan Weinreich et al.   JOURNAL OF CHEMICAL PHYSICS

Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias

(2021) Dávid Péter Kovács et al.   Nature Communications

Extraction of organic chemistry grammar from unsupervised learning of chemical reactions

(2021) Philippe Schwaller et al.   Science Advances

Data Science Meets Physical Organic Chemistry

(2021) Jennifer M. Crawford et al.   ACCOUNTS OF CHEMICAL RESEARCH

RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions

(2021) Xiaorui Wang et al.   CHEMICAL ENGINEERING JOURNAL

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

(2021) Aditya Nandy et al.   CHEMICAL REVIEWS

Clustering of Synthetic Routes Using Tree Edit Distance

(2021) Samuel Genheden et al.   Journal of Chemical Information and Modeling

Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits

(2021) Mikołaj Sacha et al.   Journal of Chemical Information and Modeling

Retrosynthesis prediction using grammar-based neural machine translation: An information-theoretic approach

(2021) Vipul Mann et al.   COMPUTERS & CHEMICAL ENGINEERING

Influence of Template Size, Canonicalization, and Exclusivity for Retrosynthesis and Reaction Prediction Applications

(2021) Esther Heid et al.   Journal of Chemical Information and Modeling

The Open Reaction Database

(2021) Steven M. Kearnes et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Discovering Catalytic Reaction Networks Using Deep Reinforcement Learning from First-Principles

(2021) Tian Lan et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

A Data-Driven Approach to the Development and Understanding of Chiroptical Sensors for Alcohols with Remote γ-Stereocenters

(2021) Jordan J. Dotson et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Machine learning modelling of chemical reaction characteristics: yesterday, today, tomorrow

(2021) Timur I. Madzhidov et al.   MENDELEEV COMMUNICATIONS

Atom‐to‐atom Mapping: A Benchmarking Study of Popular Mapping Algorithms and Consensus Strategies

(2021) Arkadii Lin et al.   Molecular Informatics

Molecular graph enhanced transformer for retrosynthesis prediction

(2021) Kelong Mao et al.   NEUROCOMPUTING

Univariate classification of phosphine ligation state and reactivity in cross-coupling catalysis

(2021) Samuel H. Newman-Stonebraker et al.   SCIENCE

The Exploration of Chemical Reaction Networks

(2020) Jan P. Unsleber et al.   Annual Review of Physical Chemistry

Learning Molecular Representations for Medicinal Chemistry

(2020) Kangway V. Chuang et al.   JOURNAL OF MEDICINAL CHEMISTRY

Deep Learning of Activation Energies

(2020) Colin A. Grambow et al.   Journal of Physical Chemistry Letters

Transfer Learning: Making Retrosynthetic Predictions Based on a Small Chemical Reaction Dataset Scale to a New Level

(2020) Renren Bai et al.   MOLECULES

Data-Driven Advancement of Homogeneous Nickel Catalyst Activity for Aryl-Ether Cleavage

(2020) Manuel Cordova et al.   ACS Catalysis

Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization

(2020) Jon Paul Janet et al.   ACS Central Science

Molecular Representation: Going Long on Fingerprints

(2020) Lagnajit Pattanaik et al.   Chem

Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning

(2020) Michael E. Fortunato et al.   Journal of Chemical Information and Modeling

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

(2020) Jeremy J. Henle et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features

(2020) Zhuoran Qiao et al.   JOURNAL OF CHEMICAL PHYSICS

Quantum chemical accuracy from density functional approximations via machine learning

(2020) Mihail Bogojeski et al.   Nature Communications

Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates

(2020) Giorgio Pesciullesi et al.   Nature Communications

CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration

(2020) Ryosuke Shibukawa et al.   Journal of Cheminformatics

AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning

(2020) Samuel Genheden et al.   Journal of Cheminformatics

Enhancing Retrosynthetic Reaction Prediction with Deep Learning using Multiscale Reaction Classification

(2019) Javier L. Baylon et al.   Journal of Chemical Information and Modeling

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

(2019) Andrew F. Zahrt et al.   SCIENCE

Navigating around Patented Routes by Preserving Specific Motifs along Computer-Planned Retrosynthetic Pathways

(2019) Karol Molga et al.   Chem

How to explore chemical space using algorithms and automation

(2019) Piotr S. Gromski et al.   Nature Reviews Chemistry

Automatic mapping of atoms across both simple and complex chemical reactions

(2019) Wojciech Jaworski et al.   Nature Communications

Computational Ligand Descriptors for Catalyst Design

(2019) Derek J. Durand et al.   CHEMICAL REVIEWS

Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists

(2019) Jessica G. Freeze et al.   CHEMICAL REVIEWS

Design and Optimization of Catalysts Based on Mechanistic Insights Derived from Quantum Chemical Reaction Modeling

(2019) Seihwan Ahn et al.   CHEMICAL REVIEWS

Deep Learning in Chemistry

(2019) Adam C. Mater et al.   Journal of Chemical Information and Modeling

RDChiral: An RDKit Wrapper for Handling Stereochemistry in Retrosynthetic Template Extraction and Application

(2019) Connor W. Coley et al.   Journal of Chemical Information and Modeling

Learning Retrosynthetic Planning through Simulated Experience

(2019) John S. Schreck et al.   ACS Central Science

Development and Application of a Data-Driven Reaction Classification Model: Comparison of an Electronic Lab Notebook and Medicinal Chemistry Literature

(2019) Gian Marco Ghiandoni et al.   Journal of Chemical Information and Modeling

A robotic platform for flow synthesis of organic compounds informed by AI planning

(2019) Connor W. Coley et al.   SCIENCE

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

(2019) Philippe Schwaller et al.   ACS Central Science

Synthetic organic chemistry driven by artificial intelligence

(2019) A. Filipa de Almeida et al.   Nature Reviews Chemistry

Autonomous discovery in the chemical sciences part II: Outlook

(2019) Connor W Coley et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Autonomous discovery in the chemical sciences part I: Progress

(2019) Klavs F. Jensen et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

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

(2019) Andrew F. Zahrt et al.   CHEMICAL REVIEWS

Predicting Retrosynthetic Reactions Using Self-Corrected Transformer Neural Networks

(2019) Shuangjia Zheng et al.   Journal of Chemical Information and Modeling

SCScore: Synthetic Complexity Learned from a Reaction Corpus

(2018) Connor W. Coley et al.   Journal of Chemical Information and Modeling

Deoxyfluorination with Sulfonyl Fluorides: Navigating Reaction Space with Machine Learning

(2018) Matthew K. Nielsen et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

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

(2018) Derek T. Ahneman et al.   SCIENCE

A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow

(2018) Damith Perera et al.   SCIENCE

Predictive and mechanistic multivariate linear regression models for reaction development

(2018) Celine B. Santiago et al.   Chemical Science

Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts

(2018) Benjamin Meyer et al.   Chemical Science

“Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models

(2018) Philippe Schwaller et al.   Chemical 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

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Planning chemical syntheses with deep neural networks and symbolic AI

(2018) Marwin H. S. Segler et al.   NATURE

Phoenics: A Bayesian Optimizer for Chemistry

(2018) Florian Häse et al.   ACS Central Science

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

(2018) Wiktor Beker et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Computational Design of Synthetic Enzymes

(2018) Valerie Vaissier Welborn et al.   CHEMICAL REVIEWS

A Predictive Tool for Electrophilic Aromatic Substitutions Using Machine Learning

(2018) Anna Tomberg et al.   JOURNAL OF ORGANIC CHEMISTRY

Exploration of Reaction Pathways and Chemical Transformation Networks

(2018) Gregor N. Simm et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Using Machine Learning To Predict Suitable Conditions for Organic Reactions

(2018) Hanyu Gao et al.   ACS Central Science

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

(2017) Zachary W. Ulissi et al.   Nature Communications

Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models

(2017) Bowen Liu et al.   ACS Central Science

Computer-Assisted Retrosynthesis Based on Molecular Similarity

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

Prediction of Organic Reaction Outcomes Using Machine Learning

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

Optimizing Chemical Reactions with Deep Reinforcement Learning

(2017) Zhenpeng Zhou et al.   ACS Central Science

The Development of Multidimensional Analysis Tools for Asymmetric Catalysis and Beyond

(2016) Matthew S. Sigman et al.   ACCOUNTS OF CHEMICAL RESEARCH

Feedback in Flow for Accelerated Reaction Development

(2016) Brandon J. Reizman et al.   ACCOUNTS OF CHEMICAL RESEARCH

Computer-Assisted Synthetic Planning: The End of the Beginning

(2016) Sara Szymkuć et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms

(2016) Connie W. Gao et al.   COMPUTER PHYSICS COMMUNICATIONS

Mastering the game of Go with deep neural networks and tree search

(2016) David Silver et al.   NATURE

Machine-learning-assisted materials discovery using failed experiments

(2016) Paul Raccuglia et al.   NATURE

Impact of Preferential π-Binding in Catalyst-Transfer Polycondensation of Thiazole Derivatives

(2016) Mitchell L. Smith et al.   ACS Macro Letters

Neural Networks for the Prediction of Organic Chemistry Reactions

(2016) Jennifer N. Wei et al.   ACS Central Science

Expert System for Predicting Reaction Conditions: The Michael Reaction Case

(2015) G. Marcou et al.   Journal of Chemical Information and Modeling

Development of a Novel Fingerprint for Chemical Reactions and Its Application to Large-Scale Reaction Classification and Similarity

(2015) Nadine Schneider et al.   Journal of Chemical Information and Modeling

Sampling reactive pathways with random walks in chemical space: Applications to molecular dissociation and catalysis

(2015) Scott Habershon   JOURNAL OF CHEMICAL PHYSICS

Heuristics-Guided Exploration of Reaction Mechanisms

(2015) Maike Bergeler et al.   Journal of Chemical Theory and Computation

Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry

(2014) Dmitrij Rappoport et al.   Journal of Chemical Theory and Computation

Discovering chemistry with an ab initio nanoreactor

(2014) Lee-Ping Wang et al.   Nature Chemistry

Algorithm for Reaction Classification

(2013) Hans Kraut et al.   Journal of Chemical Information and Modeling

Automated discovery of chemically reasonable elementary reaction steps

(2013) Paul M. Zimmerman   JOURNAL OF COMPUTATIONAL CHEMISTRY

Systematic exploration of the mechanism of chemical reactions: the global reaction route mapping (GRRM) strategy using the ADDF and AFIR methods

(2013) Satoshi Maeda et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

ReactionPredictor: Prediction of Complex Chemical Reactions at the Mechanistic Level Using Machine Learning

(2012) Matthew A. Kayala et al.   Journal of Chemical Information and Modeling

An Evolutionary Algorithm for de Novo Optimization of Functional Transition Metal Compounds

(2012) Yunhan Chu et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

RASA: A Rapid Retrosynthesis-Based Scoring Method for the Assessment of Synthetic Accessibility of Drug-like Molecules

(2011) Qi Huang et al.   Journal of Chemical Information and Modeling

Learning to Predict Chemical Reactions

(2011) Matthew A. Kayala et al.   Journal of Chemical Information and Modeling

The Medicinal Chemist’s Toolbox: An Analysis of Reactions Used in the Pursuit of Drug Candidates

(2011) Stephen D. Roughley et al.   JOURNAL OF MEDICINAL CHEMISTRY

Extended-Connectivity Fingerprints

(2010) David Rogers et al.   Journal of Chemical Information and Modeling

Route Designer: A Retrosynthetic Analysis Tool Utilizing Automated Retrosynthetic Rule Generation

(2009) James Law et al.   Journal of Chemical Information and Modeling

Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions

(2009) Peter Ertl et al.   Journal of Cheminformatics