ArticleJournal of cheminformatics2020
From Big Data to Artificial Intelligence: chemoinformatics meets new challenges.
Article in Journal of cheminformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- NMR-AI: An Open Platform for NMR-Enhanced Molecular Representations and Physicochemical Property Prediction.Journal of chemical information and modeling · 2026Article
- Toward Environment-Sensitive Molecular Inference via Mixed Integer Linear Programming.ACS omega · 2025Article
- Advanced machine learning for innovative drug discovery.Journal of cheminformatics · 2025Article
- Machine learning-driven generation and screening of potential ionic liquids for cellulose dissolution.Journal of cheminformatics · 2025Article
- The pursuit of accurate predictive models of the bioactivity of small molecules.Chemical science · 2024Review
- Quadratic descriptors and reduction methods in a two-layered model for compound inference.Frontiers in genetics · 2024Article
- Trends and Applications in Computationally Driven Drug Repurposing.International journal of molecular sciences · 2023Article
- Molecular Property Prediction by Combining LSTM and GAT.Biomolecules · 2023Article
- Recent advances in the discovery of novel marine natural products and mycosporine-like amino acid UV-absorbing compounds.Applied microbiology and biotechnology · 2021Review
- QPHAR: quantitative pharmacophore activity relationship: method and validation.Journal of cheminformatics · 2021Article
- Trade-off Predictivity and Explainability for Machine-Learning Powered Predictive Toxicology: An in-Depth Investigation with Tox21 Data Sets.Chemical research in toxicology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing volume of biomedical data in chemistry and life sciences requires development of new methods and approaches for their analysis. Artificial Intelligence and machine learning, especially neural networks, are increasingly used in the chemical industry, in particular with respect to Big Data. This editorial highlights the main results presented during the special session of the International Conference on Neural Networks organized by "Big Data in Chemistry" project and draws perspectives on the future progress of the field.
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.