Optimized Machine Learning Techniques Enable Prediction of Organic Dyes Photophysical Properties: Absorption Wavelengths, Emission Wavelengths, and Quantum Yields DOI
Kapil Dev Mahato, Uday Kumar

Published: Jan. 1, 2023

Applications of organic dyes, ranging from basic research to industry, are functions their photophysical properties. Two important aspects— (1) knowledge the properties existing dyes long before real applications and (2) discovery new with desired for either upgradation or development applications—are needed be addressed. These two cases coupled together common goal estimating high accuracy at minimum cost time money hard-core laboratory experiment. For this purpose, machine learning-based techniques most suitable approach. In study, we used optimized machine-learning assess a dataset 3066 which were evaluated using three evaluation parameters: Root Mean Squared Error (RMSE), Absolute (MAE), coefficient determination (R2). The Quadratic Support Vector Machine (QSVM) was best predictive model RMSE-16.614, MAE-10.837, R2-0.961 absorption wavelengths RMSE-23.636, MAE-16.278, R2-0.929 emission wavelengths. R2 values 0.7% 0.4% greater than Gradient Boost Regression Tree (GBRT) model's recently reported 0.954 0.925 wavelengths, respectively. Furthermore, estimated quantum yield found that Coarse Gaussian (CGSVM) outperformed all examined models. more validation these models, compared predicted results experimental selective dyes. proposed automated approach can predicting without much computer programming knowledge.

Language: Английский

Controlling Stereoselectivity with Noncovalent Interactions in Chiral Phosphoric Acid Organocatalysis DOI
Isaiah O. Betinol, Yutao Kuang,

Brian P. Mulley

et al.

Chemical Reviews, Journal Year: 2025, Volume and Issue: unknown

Published: March 18, 2025

Chiral phosphoric acids (CPAs) have emerged as highly effective Brønsted acid catalysts in an expanding range of asymmetric transformations, often through novel multifunctional substrate activation modes. Versatile and broadly appealing, these benefit from modular tunable structures, compatibility with additives. Given the unique types noncovalent interactions (NCIs) that can be established between CPAs various reactants─such hydrogen bonding, aromatic interactions, van der Waals forces─it is unsurprising catalyst systems become a promising approach for accessing diverse chiral product outcomes. This review aims to provide in-depth exploration mechanisms by which impart stereoselectivity, positioning NCIs central feature connects broad spectrum catalytic reactions. Spanning literature 2004 2024, it covers nucleophilic additions, radical atroposelective bond formations, highlighting applicability CPA organocatalysis. Special emphasis placed on structural mechanistic features govern CPA-substrate well tools techniques developed enhance our understanding their behavior. In addition emphasizing details stereocontrolling elements individual reactions, we carefully structured this natural progression specifics broader, class-level perspective. Overall, findings underscore critical role catalysis significant contributions advancing synthesis.

Language: Английский

Citations

3
Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective DOI

Yuheng Ding,

Bo Qiang, Qixuan Chen

et al.

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(8), P. 2955 - 2970

Published: March 15, 2024

Chemical reactions serve as foundational building blocks for organic chemistry and drug design. In the era of large AI models, data-driven approaches have emerged to innovate design novel reactions, optimize existing ones higher yields, discover new pathways synthesizing chemical structures comprehensively. To effectively address these challenges with machine learning it is imperative derive robust informative representations or engage in feature engineering using extensive data sets reactions. This work aims provide a comprehensive review established reaction featurization approaches, offering insights into selection features wide array tasks. The advantages limitations employing SMILES, molecular fingerprints, graphs, physics-based properties are meticulously elaborated. Solutions bridge gap between different will also be critically evaluated. Additionally, we introduce frontier pretraining, holding promise an innovative yet unexplored avenue.

Language: Английский

Citations

10
Optimized Machine learning techniques Enable prediction of organic dyes photophysical Properties: Absorption Wavelengths, emission Wavelengths, and quantum yields DOI
Kapil Dev Mahato, Uday Kumar

Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy, Journal Year: 2023, Volume and Issue: 308, P. 123768 - 123768

Published: Dec. 15, 2023

Language: Английский

Citations

20
Connecting the Complexity of Stereoselective Synthesis to the Evolution of Predictive Tools DOI Creative Commons
Jiajing Li, Jolene P. Reid

Chemical Science, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

This review provides an overview of predictive tools in asymmetric synthesis. The evolution methods from simple qualitative pictures to complicated quantitative approaches is connected with the increased complexity stereoselective

Language: Английский

Citations

0
Predictive Modeling of PFAS Behavior and Degradation in Novel Treatment Scenarios: A Review DOI Creative Commons
David B. Olawade,

James Ijiwade,

Oluwaseun Fapohunda

et al.

Process Safety and Environmental Protection, Journal Year: 2025, Volume and Issue: unknown, P. 106869 - 106869

Published: Feb. 1, 2025

Language: Английский

Citations

0
Computational Tools for the Prediction of Site- and Regioselectivity of Organic Reactions DOI Creative Commons
Lukas M. Sigmund,

Michele Assante,

Magnus J. Johansson

et al.

Chemical Science, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

This article reviews computational tools for the prediction of regio- and site-selectivity organic reactions. It spans from quantum chemical procedures to deep learning models showcases application presented tools.

Language: Английский

Citations

0
Evaluating Predictive Accuracy in Asymmetric Catalysis: A Machine Learning Perspective on Local Reaction Space DOI
Isaiah O. Betinol,

Aleksandra Demchenko,

Jolene P. Reid

et al.

ACS Catalysis, Journal Year: 2025, Volume and Issue: unknown, P. 6067 - 6077

Published: March 31, 2025

Language: Английский

Citations

0
Machine Learning Enables a Top-Down Approach to Mechanistic Elucidation in Asymmetric Catalysis DOI
Isaiah O. Betinol, Yutao Kuang, Junshan Lai

et al.

ACS Catalysis, Journal Year: 2025, Volume and Issue: unknown, P. 8799 - 8810

Published: May 9, 2025

Language: Английский

Citations

0
Machine Learning Enables a Top-Down Approach to Mechanistic Elucidation DOI Creative Commons
Isaiah O. Betinol, Yutao Kuang, Junshan Lai

et al.

Published: June 18, 2024

General reaction behavior is rarely reported in asymmetric catalysis, not simply because it difficult to achieve, but also due the methods used for its identification and study. Traditional approaches involve compartmentalization, where impact of individual components first analyzed, followed by assimilation using simple response structure matching techniques. However, extending this method accommodate complex conditions diverse reactions proves challenging. Here, we present a data-driven that relies on clusterwise linear regression derive predictively apply general mechanistic models enantioinduction, with minimal human intervention. When applied palladium-catalyzed decarboxylative allylic alkylation (DAAA) reaction, unexpected interactions governing enantioselectivity are revealed, supported high-level computations additional experiments. Our results demonstrate workflow as powerful new tool automating elucidation effectively identifying performance.

Language: Английский

Citations

1
Optimized Machine Learning Techniques Enable Prediction of Organic Dyes Photophysical Properties: Absorption Wavelengths, Emission Wavelengths, and Quantum Yields DOI
Kapil Dev Mahato, Uday Kumar

Published: Jan. 1, 2023

Applications of organic dyes, ranging from basic research to industry, are functions their photophysical properties. Two important aspects— (1) knowledge the properties existing dyes long before real applications and (2) discovery new with desired for either upgradation or development applications—are needed be addressed. These two cases coupled together common goal estimating high accuracy at minimum cost time money hard-core laboratory experiment. For this purpose, machine learning-based techniques most suitable approach. In study, we used optimized machine-learning assess a dataset 3066 which were evaluated using three evaluation parameters: Root Mean Squared Error (RMSE), Absolute (MAE), coefficient determination (R2). The Quadratic Support Vector Machine (QSVM) was best predictive model RMSE-16.614, MAE-10.837, R2-0.961 absorption wavelengths RMSE-23.636, MAE-16.278, R2-0.929 emission wavelengths. R2 values 0.7% 0.4% greater than Gradient Boost Regression Tree (GBRT) model's recently reported 0.954 0.925 wavelengths, respectively. Furthermore, estimated quantum yield found that Coarse Gaussian (CGSVM) outperformed all examined models. more validation these models, compared predicted results experimental selective dyes. proposed automated approach can predicting without much computer programming knowledge.

Language: Английский

Citations

1