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Literature DB >> 30442777

Response to Comment on "Predicting reaction performance in C-N cross-coupling using machine learning".

Jesús G Estrada¹, Derek T Ahneman¹, Robert P Sheridan², Spencer D Dreher³, Abigail G Doyle⁴.

Abstract

We demonstrate that the chemical-feature model described in our original paper is distinguishable from the nongeneralizable models introduced by Chuang and Keiser. Furthermore, the chemical-feature model significantly outperforms these models in out-of-sample predictions, justifying the use of chemical featurization from which machine learning models can extract meaningful patterns in the dataset, as originally described.

Mesh：

Year: 2018 PMID： 30442777 DOI： 10.1126/science.aat8763

Source DB: PubMed Journal: Science ISSN： 0036-8075 Impact factor: 47.728

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Cited

7 in total

1. Unified Deep Learning Model for Multitask Reaction Predictions with Explanation.

Authors: Jieyu Lu; Yingkai Zhang
Journal: J Chem Inf Model Date: 2022-03-10 Impact factor: 4.956

2. Mechanisms, Challenges, and Opportunities of Dual Ni/Photoredox-Catalyzed C(sp²)-C(sp³) Cross-Couplings.

Authors: Mingbin Yuan; Osvaldo Gutierrez
Journal: Wiley Interdiscip Rev Comput Mol Sci Date: 2021-09-21

3. Limitations of machine learning models when predicting compounds with completely new chemistries: possible improvements applied to the discovery of new non-fullerene acceptors.

Authors: Zhi-Wen Zhao; Marcos Del Cueto; Alessandro Troisi
Journal: Digit Discov Date: 2022-03-25

4. Iterative Supervised Principal Component Analysis Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis.

Authors: Xin Yi See; Xuelan Wen; T Alexander Wheeler; Channing K Klein; Jason D Goodpaster; Benjamin R Reiner; Ian A Tonks
Journal: ACS Catal Date: 2020-11-05 Impact factor: 13.084

Response to Comment on "Predicting reaction performance in C-N cross-coupling using machine learning".

1. Unified Deep Learning Model for Multitask Reaction Predictions with Explanation.

2. Mechanisms, Challenges, and Opportunities of Dual Ni/Photoredox-Catalyzed C(sp²)-C(sp³) Cross-Couplings.

3. Limitations of machine learning models when predicting compounds with completely new chemistries: possible improvements applied to the discovery of new non-fullerene acceptors.

4. Iterative Supervised Principal Component Analysis Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis.

Review 5. Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns.

6. Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.

7. Predicting glycosylation stereoselectivity using machine learning.

Response to Comment on "Predicting reaction performance in C-N cross-coupling using machine learning".

1. Unified Deep Learning Model for Multitask Reaction Predictions with Explanation.

2. Mechanisms, Challenges, and Opportunities of Dual Ni/Photoredox-Catalyzed C(sp2)-C(sp3) Cross-Couplings.

3. Limitations of machine learning models when predicting compounds with completely new chemistries: possible improvements applied to the discovery of new non-fullerene acceptors.

4. Iterative Supervised Principal Component Analysis Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis.

Review 5. Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns.

6. Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.

7. Predicting glycosylation stereoselectivity using machine learning.

2. Mechanisms, Challenges, and Opportunities of Dual Ni/Photoredox-Catalyzed C(sp²)-C(sp³) Cross-Couplings.