ArticleProceedings of the National Academy of Sciences of the United States of America2019
A neural network protocol for electronic excitations of
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Cost-Effective Multi-Channel MolOrbImage for Machine-Learned Excited-State Properties of Practical Photofunctional Materials.Journal of chemical theory and computation · 2026Article
- Article
- MMEASE: enhanced analytical workflow for single-cell metabolomics.Nucleic acids research · 2025Article
- Universal machine learning for the response of atomistic systems to external fields.Nature communications · 2023Article
- Article
- Identifying structure-absorption relationships and predicting absorption strength of non-fullerene acceptors for organic photovoltaics.Energy & environmental science · 2022Article
- Reconstruction of Nuclear Ensemble Approach Electronic Spectra Using Probabilistic Machine Learning.Journal of chemical theory and computation · 2022Article
- Machine learning recognition of protein secondary structures based on two-dimensional spectroscopic descriptors.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Accurate Machine Learning Prediction of Protein Circular Dichroism Spectra with Embedded Density Descriptors.JACS Au · 2021Article
- Unsupervised Machine Learning Neural Gas Algorithm for Accurate Evaluations of the Hessian Matrix in Molecular Dynamics.Journal of chemical theory and computation · 2021Article
- Machine Learning for Electronically Excited States of Molecules.Chemical reviews · 2021Article
- Review
- AI-based spectroscopic monitoring of real-time interactions between SARS-CoV-2 and human ACE2.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Molecular excited states through a machine learning lens.Nature reviews. Chemistry · 2021Review
- Deep Learning Optical Spectroscopy Based on Experimental Database: Potential Applications to Molecular Design.JACS Au · 2021Article
- Machine learning dielectric screening for the simulation of excited state properties of molecules and materials.Chemical science · 2021Article
- Machine Learning for Absorption Cross Sections.The journal of physical chemistry. A · 2020Article
- Toward the Prediction of Multi-Spin State Charges of a Heme Model by Random Forest Regression.Frontiers in chemistry · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
UV absorption is widely used for characterizing proteins structures. The mapping of UV spectra to atomic structure of proteins relies on expensive theoretical simulations, circumventing the heavy computational cost which involves repeated quantum-mechanical simulations of excited-state properties of many fluctuating protein geometries, which has been a long-time challenge. Here we show that a neural network machine-learning technique can predict electronic absorption spectra of
Indexed as
Identifiers
What OpenQuestion holds
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.