MetaRF: attention-based random forest for reaction yield prediction with a few trails

[1]  Maxime Langevin,et al.  Machine Learning Yield Prediction from NiCOlit, a Small-Size Literature Data Set of Nickel Catalyzed C-O Couplings. , 2022, Journal of the American Chemical Society.

[2]  Zelin Zhang,et al.  XGBoost‐based intelligence yield prediction and reaction factors analysis of amination reaction , 2021, J. Comput. Chem..

[3]  John E. Herr,et al.  On the use of real-world datasets for reaction yield prediction , 2021, Chemical Science.

[4]  Meng-long Li,et al.  Prediction of multicomponent reaction yields using machine learning , 2021 .

[5]  Wei Mao,et al.  Radar target recognition based on few-shot learning , 2021, Multimedia Systems.

[6]  Jean-Louis Reymond,et al.  Reaction classification and yield prediction using the differential reaction fingerprint DRFP , 2021, Digital discovery.

[7]  Florian Becker,et al.  GemNet: Universal Directional Graph Neural Networks for Molecules , 2021, NeurIPS.

[8]  Farzana Anowar,et al.  Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE) , 2021, Comput. Sci. Rev..

[9]  A. Żurański,et al.  Predicting Reaction Yields via Supervised Learning. , 2021, Accounts of chemical research.

[10]  D. Klein,et al.  Calibrate Before Use: Improving Few-Shot Performance of Language Models , 2021, ICML.

[11]  Ryan P. Adams,et al.  Bayesian reaction optimization as a tool for chemical synthesis , 2021, Nature.

[12]  Jean-Louis Reymond,et al.  Mapping the space of chemical reactions using attention-based neural networks , 2020, Nature Machine Intelligence.

[13]  Alain C. Vaucher,et al.  Prediction of chemical reaction yields using deep learning , 2020, Mach. Learn. Sci. Technol..

[14]  Yiping Ren,et al.  The predictive performances of random forest models with limited sample size and different species traits , 2020 .

[15]  Regina Barzilay,et al.  Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis , 2020, Journal of medicinal chemistry.

[16]  Brian C. Barnes,et al.  Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning , 2020, J. Chem. Inf. Model..

[17]  Francis L Martin,et al.  Improving data splitting for classification applications in spectrochemical analyses employing a random-mutation Kennard-Stone algorithm approach , 2019, Bioinform..

[18]  Michael J. Keiser,et al.  Comment on “Predicting reaction performance in C–N cross-coupling using machine learning” , 2018, Science.

[19]  Robert P Sheridan,et al.  Response to Comment on “Predicting reaction performance in C–N cross-coupling using machine learning” , 2018, Science.

[20]  Philipp Berens,et al.  The art of using t-SNE for single-cell transcriptomics , 2018, Nature Communications.

[21]  Stephen Gabrielson,et al.  SciFinder , 2018, Journal of the Medical Library Association : JMLA.

[22]  Leroy Cronin,et al.  Controlling an organic synthesis robot with machine learning to search for new reactivity , 2018, Nature.

[23]  Yun Xu,et al.  On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning , 2018, Journal of Analysis and Testing.

[24]  Connor W. Coley,et al.  Machine Learning in Computer-Aided Synthesis Planning. , 2018, Accounts of chemical research.

[25]  Derek T. Ahneman,et al.  Predicting reaction performance in C–N cross-coupling using machine learning , 2018, Science.

[26]  T. Matsumura,et al.  Machine Learning Approach for Prediction of Reaction Yield with Simulated Catalyst Parameters , 2018 .

[27]  Paul Richardson,et al.  A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow , 2018, Science.

[28]  Regina Barzilay,et al.  Prediction of Organic Reaction Outcomes Using Machine Learning , 2017, ACS central science.

[29]  Sergey Levine,et al.  Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.

[30]  Wentian Li,et al.  Application of t-SNE to Human Genetic Data , 2017, bioRxiv.

[31]  Byunghan Lee,et al.  Deep learning in bioinformatics , 2016, Briefings Bioinform..

[32]  Weixu,et al.  Effectiveness of the Euclidean distance in high dimensional spaces , 2015 .

[33]  Daniel M. Lowe,et al.  Development of a Novel Fingerprint for Chemical Reactions and Its Application to Large-Scale Reaction Classification and Similarity , 2015, J. Chem. Inf. Model..

[34]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[35]  Daniel M. Lowe Extraction of chemical structures and reactions from the literature , 2012 .

[36]  Barbara Hammer,et al.  Linear basis-function t-SNE for fast nonlinear dimensionality reduction , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[37]  S. Garrigues,et al.  Protein determination in serum and whole blood by attenuated total reflectance infrared spectroscopy , 2012, Analytical and Bioanalytical Chemistry.

[38]  Kimito Funatsu,et al.  Non-linear modeling and chemical interpretation with aid of support vector machine and regression. , 2010, Current computer-aided drug design.

[39]  Jonathan Goodman,et al.  Computer Software Review: Reaxys , 2009, J. Chem. Inf. Model..

[40]  Michel Verleysen,et al.  On the Effects of Dimensionality on Data Analysis with Neural Networks , 2009, IWANN.

[41]  L. Breiman Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[42]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[43]  E J Corey,et al.  Computer-assisted design of complex organic syntheses. , 1969, Science.

[44]  L. A. Stone,et al.  Computer Aided Design of Experiments , 1969 .

[45]  A. Żurański,et al.  Auto-QChem: an automated workflow for the generation and storage of DFT calculations for organic molecules , 2022, Reaction Chemistry & Engineering.

[46]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[47]  Geoffrey E. Hinton,et al.  Stochastic Neighbor Embedding , 2002, NIPS.