ArticleACS central science2018
Learning a Local-Variable Model of Aromatic and Conjugated Systems.
Article in ACS central science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 22 citations in OpenAlex.
- Group Contribution Method Supervised Neural Network for Precise Design of Organic Nonlinear Optical Materials.Precision chemistry · 2024Article
- Extending machine learning beyond interatomic potentials for predicting molecular properties.Nature reviews. Chemistry · 2022Review
- Artificial Intelligence for Autonomous Molecular Design: A Perspective.Molecules (Basel, Switzerland) · 2021Review
- Deep Learning Coordinate-Free Quantum Chemistry.The journal of physical chemistry. A · 2021Article
- Site-Level Bioactivity of Small-Molecules from Deep-Learned Representations of Quantum Chemistry.The journal of physical chemistry. A · 2020Article
- Deep Neural Networks for Multicomponent Molecular Systems.ACS omega · 2020Article
- Practical Model Selection for Prospective Virtual Screening.Journal of chemical information and modeling · 2019Article
- ACS Central Science Virtual Issue on Machine Learning.ACS central science · 2018Article
- Opportunities and obstacles for deep learning in biology and medicine.Journal of the Royal Society, Interface · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
A collection of new approaches to building and training neural networks, collectively referred to as deep learning, are attracting attention in theoretical chemistry. Several groups aim to replace computationally expensive
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.