标题
Evaluation guidelines for machine learning tools in the chemical sciences
作者
关键词
-
出版物
Nature Reviews Chemistry
Volume -, Issue -, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2022-05-24
DOI
10.1038/s41570-022-00391-9
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Origins of structural and electronic transitions in disordered silicon
- (2021) Volker L. Deringer et al. NATURE
- Bayesian reaction optimization as a tool for chemical synthesis
- (2021) Benjamin J. Shields et al. NATURE
- Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data
- (2021) Andreas Bender et al. DRUG DISCOVERY TODAY
- Computationally guided high-throughput design of self-assembling drug nanoparticles
- (2021) Daniel Reker et al. Nature Nanotechnology
- Inferring experimental procedures from text-based representations of chemical reactions
- (2021) Alain C. Vaucher et al. Nature Communications
- Allosteric Antagonist Modulation of TRPV2 by Piperlongumine Impairs Glioblastoma Progression
- (2021) João Conde et al. ACS Central Science
- Extraction of organic chemistry grammar from unsupervised learning of chemical reactions
- (2021) Philippe Schwaller et al. Science Advances
- Facts and Figures on Materials Science and Nanotechnology Progress and Investment
- (2021) Sepehr Talebian et al. ACS Nano
- Beam Search for Automated Design and Scoring of Novel ROR Ligands with Machine Intelligence**
- (2021) Michael Moret et al. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
- Best practices in machine learning for chemistry
- (2021) Nongnuch Artrith et al. Nature Chemistry
- Crowdsourced mapping of unexplored target space of kinase inhibitors
- (2021) Anna Cichońska et al. Nature Communications
- Benchmarks for interpretation of QSAR models
- (2021) Mariia Matveieva et al. Journal of Cheminformatics
- Deep Learning Algorithms for Interpretation of Upper Extremity Radiographs: Laterality and Technologist Initial Labels As Confounding Factors
- (2021) Paul H. Yi et al. AMERICAN JOURNAL OF ROENTGENOLOGY
- Deriving intuition in catalyst design with machine learning
- (2021) Tiago Rodrigues Chem
- Validating the validation: reanalyzing a large-scale comparison of deep learning and machine learning models for bioactivity prediction
- (2020) Matthew C. Robinson et al. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
- Improved protein structure prediction using potentials from deep learning
- (2020) Andrew W. Senior et al. NATURE
- Assessing the impact of generative AI on medicinal chemistry
- (2020) W. Patrick Walters et al. NATURE BIOTECHNOLOGY
- SciPy 1.0: fundamental algorithms for scientific computing in Python
- (2020) Pauli Virtanen et al. NATURE METHODS
- A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation
- (2020) Sukriti Singh et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- An Autonomous Chemical Robot Discovers the Rules of Inorganic Coordination Chemistry without Prior Knowledge
- (2020) Luzian Porwol et al. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
- Revealing cytotoxic substructures in molecules using deep learning
- (2020) Henry E. Webel et al. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
- Accelerated discovery of CO2 electrocatalysts using active machine learning
- (2020) Miao Zhong et al. NATURE
- A Structure-Based Platform for Predicting Chemical Reactivity
- (2020) Frederik Sandfort et al. Chem
- Self-driving laboratory for accelerated discovery of thin-film materials
- (2020) B. P. MacLeod et al. Science Advances
- SCAM Detective: Accurate Predictor of Small, Colloidally Aggregating Molecules
- (2020) Vinicius M. Alves et al. Journal of Chemical Information and Modeling
- A deep-learning view of chemical space designed to facilitate drug discovery
- (2020) Paul Maragakis et al. Journal of Chemical Information and Modeling
- Assessing the frontier: Active learning, model accuracy, and multi-objective candidate discovery and optimization
- (2020) Zachary del Rosario et al. JOURNAL OF CHEMICAL PHYSICS
- Machine Learning on DNA-Encoded Libraries: A New Paradigm for Hit Finding
- (2020) Kevin McCloskey et al. JOURNAL OF MEDICINAL CHEMISTRY
- A mobile robotic chemist
- (2020) Benjamin Burger et al. NATURE
- Human–computer collaboration for skin cancer recognition
- (2020) Philipp Tschandl et al. NATURE MEDICINE
- Applications of machine learning to diagnosis and treatment of neurodegenerative diseases
- (2020) Monika A. Myszczynska et al. Nature Reviews Neurology
- Improving the accuracy of medical diagnosis with causal machine learning
- (2020) Jonathan G. Richens et al. Nature Communications
- One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome
- (2020) Alice Capecchi et al. Journal of Cheminformatics
- Pruned Machine Learning Models to Predict Aqueous Solubility
- (2020) Alexander L. Perryman et al. ACS Omega
- Computational planning of the synthesis of complex natural products
- (2020) Barbara Mikulak-Klucznik et al. NATURE
- Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates
- (2020) Giorgio Pesciullesi et al. Nature Communications
- Designing and understanding light-harvesting devices with machine learning
- (2020) Florian Häse et al. Nature Communications
- Machine Learning Predictions of Block Copolymer Self‐Assembly
- (2020) Kun‐Hua Tu et al. ADVANCED MATERIALS
- Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet
- (2020) Andreas Bender et al. DRUG DISCOVERY TODAY
- Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
- (2020) Daniil Polykovskiy et al. Frontiers in Pharmacology
- Determining Multi‐Component Phase Diagrams with Desired Characteristics Using Active Learning
- (2020) Yuan Tian et al. Advanced Science
- Machine learning toward advanced energy storage devices and systems
- (2020) Tianhan Gao et al. iScience
- Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning
- (2019) Andrew F. Zahrt et al. SCIENCE
- How to explore chemical space using algorithms and automation
- (2019) Piotr S. Gromski et al. Nature Reviews Chemistry
- Data-driven prediction of battery cycle life before capacity degradation
- (2019) Kristen A. Severson et al. Nature Energy
- Exploring the GDB-13 chemical space using deep generative models
- (2019) Josep Arús-Pous et al. Journal of Cheminformatics
- Legal and practical challenges in classifying nanomaterials according to regulatory definitions
- (2019) Martin Miernicki et al. Nature Nanotechnology
- GuacaMol: Benchmarking Models for de Novo Molecular Design
- (2019) Nathan Brown et al. Journal of Chemical Information and Modeling
- In Need of Bias Control: Evaluating Chemical Data for Machine Learning in Structure-Based Virtual Screening
- (2019) Jochen Sieg et al. Journal of Chemical Information and Modeling
- Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It?
- (2019) Robert P. Sheridan Journal of Chemical Information and Modeling
- Validation strategies for target prediction methods
- (2019) Neann Mathai et al. BRIEFINGS IN BIOINFORMATICS
- Computational advances in combating colloidal aggregation in drug discovery
- (2019) Daniel Reker et al. Nature Chemistry
- Applications of machine learning in drug discovery and development
- (2019) Jessica Vamathevan et al. NATURE REVIEWS DRUG DISCOVERY
- Holistic prediction of enantioselectivity in asymmetric catalysis
- (2019) Jolene P. Reid et al. NATURE
- Learning Retrosynthetic Planning through Simulated Experience
- (2019) John S. Schreck et al. ACS Central Science
- Deep learning enables rapid identification of potent DDR1 kinase inhibitors
- (2019) Alex Zhavoronkov et al. NATURE BIOTECHNOLOGY
- Setting the standards for machine learning in biology
- (2019) David T. Jones NATURE REVIEWS MOLECULAR CELL BIOLOGY
- Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening
- (2019) Lieyang Chen et al. PLoS One
- 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
- Predictive Multivariate Linear Regression Analysis Guides Successful Catalytic Enantioselective Minisci Reactions of Diazines
- (2019) Jolene P. Reid et al. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
- Quantitative Structure–Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future
- (2019) Andrew F. Zahrt et al. CHEMICAL REVIEWS
- Reducing the Concepts of Data Science and Machine Learning to Tools for the Bench Chemist
- (2019) Richard A. Lewis et al. CHIMIA
- Rethinking drug design in the artificial intelligence era
- (2019) Petra Schneider et al. NATURE REVIEWS DRUG DISCOVERY
- Advancing drug discovery via GPU-based deep learning
- (2018) Erik Gawehn et al. Expert Opinion on Drug Discovery
- Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization
- (2018) Izhar Wallach 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
- Classifiers and their Metrics Quantified
- (2018) J. B. Brown Molecular Informatics
- Quantitative self-assembly prediction yields targeted nanomedicines
- (2018) Yosi Shamay et al. NATURE MATERIALS
- Predicting reaction performance in C–N cross-coupling using machine learning
- (2018) Derek T. Ahneman et al. SCIENCE
- Machine intelligence decrypts β-lapachone as an allosteric 5-lipoxygenase inhibitor
- (2018) Tiago Rodrigues 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
- MoleculeNet: a benchmark for molecular machine learning
- (2018) Zhenqin Wu et al. Chemical Science
- Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
- (2018) Rafael Gómez-Bombarelli et al. ACS Central Science
- Efficient Syntheses of Diverse, Medicinally Relevant Targets Planned by Computer and Executed in the Laboratory
- (2018) Tomasz Klucznik et al. Chem
- Prospectively Validated Proteochemometric Models for the Prediction of Small-Molecule Binding to Bromodomain Proteins
- (2018) Kathryn A. Giblin et al. Journal of Chemical Information and Modeling
- 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
- Minimum information reporting in bio–nano experimental literature
- (2018) Matthew Faria et al. Nature Nanotechnology
- Inverse molecular design using machine learning: Generative models for matter engineering
- (2018) Benjamin Sanchez-Lengeling et al. SCIENCE
- Phoenics: A Bayesian Optimizer for Chemistry
- (2018) Florian Häse et al. ACS Central Science
- Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
- (2018) Scott M. Lundberg et al. Nature Biomedical Engineering
- 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
- (2018) Wiktor Beker et al. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
- 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
- Use of Multiple Linear Regression Models for Setting Water Quality Criteria for Copper: A Complementary Approach to the Biotic Ligand Model
- (2017) Kevin V. Brix et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
- ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics
- (2017) Jiangming Sun et al. Journal of Cheminformatics
- Molecular de-novo design through deep reinforcement learning
- (2017) Marcus Olivecrona et al. Journal of Cheminformatics
- Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models
- (2017) Bowen Liu et al. ACS Central Science
- The Ecstasy and Agony of Assay Interference Compounds
- (2017) Courtney Aldrich et al. ACS Central Science
- Computer-Assisted Retrosynthesis Based on Molecular Similarity
- (2017) Connor W. Coley et al. ACS Central Science
- Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
- (2017) Marwin H. S. Segler et al. ACS Central Science
- Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach
- (2016) Rafael Gómez-Bombarelli et al. NATURE MATERIALS
- Standardizing the resolution claims for coherent microscopy
- (2016) Roarke Horstmeyer et al. Nature Photonics
- Multi-objective active machine learning rapidly improves structure–activity models and reveals new protein–protein interaction inhibitors
- (2016) D. Reker et al. Chemical Science
- De Novo Fragment Design for Drug Discovery and Chemical Biology
- (2015) Tiago Rodrigues et al. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
- Active-learning strategies in computer-assisted drug discovery
- (2015) Daniel Reker et al. DRUG DISCOVERY TODAY
- Beware of R2: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models
- (2015) D. L. J. Alexander et al. Journal of Chemical Information and Modeling
- Characterization of tandem organic solar cells
- (2015) Ronny Timmreck et al. Nature Photonics
- Multi-Objective Molecular De Novo Design by Adaptive Fragment Prioritization
- (2014) Michael Reutlinger et al. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
- The influence of negative training set size on machine learning-based virtual screening
- (2014) Rafał Kurczab et al. Journal of Cheminformatics
- Machine learning methods in chemoinformatics
- (2014) John B. O. Mitchell Wiley Interdisciplinary Reviews-Computational Molecular Science
- Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction.
- (2013) Robert P. Sheridan Journal of Chemical Information and Modeling
- Chemically Advanced Template Search (CATS) for Scaffold-Hopping and Prospective Target Prediction for ‘Orphan’ Molecules
- (2013) Michael Reutlinger et al. Molecular Informatics
- Similarity maps - a visualization strategy for molecular fingerprints and machine-learning methods
- (2013) Sereina Riniker et al. Journal of Cheminformatics
- Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking
- (2012) Michael M. Mysinger et al. JOURNAL OF MEDICINAL CHEMISTRY
- Healthy skepticism: assessing realistic model performance
- (2009) Scott P. Brown et al. DRUG DISCOVERY TODAY
- Comments on the Definition of theQ2Parameter for QSAR Validation
- (2009) Viviana Consonni et al. Journal of Chemical Information and Modeling
- Recommendations for evaluation of computational methods
- (2008) Ajay N. Jain et al. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started