ArticleJACS Au2021
Deep Learning Optical Spectroscopy Based on Experimental Database: Potential Applications to Molecular Design.
Article in JACS Au, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
38 citing papers in PubMed.
- Integrating Low-Cost xTB Electronic Priors for Predicting Electronic and Excited-State Properties of Organic Conjugated Molecules.Chemphyschem : a European journal of chemical physics and physical chemistry · 2026Article
- Interpretable Deep Learning of Molecule-Solvent Interactions Using a Solvatochromic Subgroup Contribution Framework.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Machine Learning Models with a Reject Option to Minimize Prediction Error: Application to Optical Properties of Dye Molecules.Research square · 2026Article
- Integrating molecular generation and fingerprints transferring for single-molecule theranostics targeting endoplasmic reticulum stress.Journal of advanced research · 2026Article
- A Contrastive-Learning-Based Pre-Training Framework for Optical Property Prediction of Low-Data Rhodamines with Interpretable Multitask Graph Neural Networks.Molecules (Basel, Switzerland) · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- DyeDactic workflow to predict halochromism of biosynthetic colourants.Communications chemistry · 2026Article
- Empowering photodynamic therapy with artificial intelligence: current trends and future directions.Frontiers in oncology · 2026Review
- A quantum chemical dataset of interacting molecular pairs for chemical reaction studies.Journal of cheminformatics · 2025Article
- Interpretable machine learning integrated with TD-DFT descriptors and SHAP analysis for predicting the maximum absorption wavelength of azo dyes.RSC advances · 2025Article
- Accelerated Chemical Space Generation of High Molar Extinction Organic Sensitizers via Machine Learning.Journal of fluorescence · 2025Article
- Machine learning spectroscopy to advance computation and analysis.Chemical science · 2025Review
- SERS Cheminformatics: Opportunities for Data-Driven Discovery and Applications.ACS central science · 2025Review
- Enhanced Prediction of Absorption and Emission Wavelengths of Organic Compounds through Hybrid Graph Neural Network Architectures.ACS omega · 2025Article
- Deep Learning-Driven Intelligent Fluorescent Probes: Advancements in Molecular Design for Accurate Food Safety Detection.Foods (Basel, Switzerland) · 2025Review
- Data-driven discovery of near-infrared type I photosensitizers for RNA-targeted tumor photodynamic therapy.Chemical science · 2025Article
- Integrating Machine Learning and SHAP Analysis to Advance the Rational Design of Benzothiadiazole Derivatives with Tailored Photophysical Properties.Journal of chemical information and modeling · 2025Article
- Three sample-sparing techniques to estimate the molar absorption coefficient of luminescent dyes.Biochemistry and biophysics reports · 2025Article
- Enhancing fluorescent probe design through multilayer interaction convolutional networks: advancing biosensing and bioimaging precision.Chemical science · 2025Article
- A modular artificial intelligence framework to facilitate fluorophore design.Nature communications · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate and reliable prediction of the optical and photophysical properties of organic compounds is important in various research fields. Here, we developed deep learning (DL) optical spectroscopy using a DL model and experimental database to predict seven optical and photophysical properties of organic compounds, namely, the absorption peak position and bandwidth, extinction coefficient, emission peak position and bandwidth, photoluminescence quantum yield (PLQY), and emission lifetime. Our DL model included the chromophore-solvent interaction to account for the effect of local environments on the optical and photophysical properties of organic compounds and was trained using an experimental database of 30 094 chromophore/solvent combinations. Our DL optical spectroscopy made it possible to reliably and quickly predict the aforementioned properties of organic compounds in solution, gas phase, film, and powder with the root mean squared errors of 26.6 and 28.0 nm for absorption and emission peak positions, 603 and 532 cm
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.