ArticleMolecular informatics2016
BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry.
Article in Molecular informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed.
- 1,5-Disubstituted Tetrazoles as Promising Anticancer Resistance Agents: A Chemoinformatic Characterization.ACS omega · 2026Article
- Big Data for Drug Discovery: Some Historical Landscape, Considerations, and Applications for a Medicinal Chemist.ACS omega · 2026Review
- SERS Cheminformatics: Opportunities for Data-Driven Discovery and Applications.ACS central science · 2025Review
- From molecules to data: the emerging impact of chemoinformatics in chemistry.Journal of cheminformatics · 2025Review
- NMRExtractor: leveraging large language models to construct an experimental NMR database from open-source scientific publications.Chemical science · 2025Article
- MolCompass: multi-tool for the navigation in chemical space and visual validation of QSAR/QSPR models.Journal of cheminformatics · 2024Article
- Stereochemically-aware bioactivity descriptors for uncharacterized chemical compounds.Journal of cheminformatics · 2024Article
- Implementation of IFPTML Computational Models in Drug Discovery Against Flaviviridae Family.Journal of chemical information and modeling · 2024Article
- Trends and Applications in Computationally Driven Drug Repurposing.International journal of molecular sciences · 2023Article
- Global reactivity models are impactful in industrial synthesis applications.Journal of cheminformatics · 2023Article
- Machine Learning Prediction of Mycobacterial Cell Wall Permeability of Drugs and Drug-like Compounds.Molecules (Basel, Switzerland) · 2023Article
- Discovery of TIGIT inhibitors based on DEL and machine learning.Frontiers in chemistry · 2022Article
- GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics.Journal of cheminformatics · 2021Article
- Generative Algorithm for Molecular Graphs Uncovers Products of Oil Oxidation.Journal of chemical information and modeling · 2021Article
- Troubleshooting unstable molecules in chemical space.Chemical science · 2021Article
- Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods.Journal of chemical information and modeling · 2021Article
- Big data and machine learning for materials science.Discover materials · 2021Review
- From Big Data to Artificial Intelligence: chemoinformatics meets new challenges.Journal of cheminformatics · 2020Article
- Big Data in metagenomics: Apache Spark vs MPI.PloS one · 2020Article
- Open Innovation in Medical and Pharmaceutical Research: A Literature Landscape Analysis.Frontiers in pharmacology · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing volume of biomedical data in chemistry and life sciences requires the development of new methods and approaches for their handling. Here, we briefly discuss some challenges and opportunities of this fast growing area of research with a focus on those to be addressed within the BIGCHEM project. The article starts with a brief description of some available resources for "Big Data" in chemistry and a discussion of the importance of data quality. We then discuss challenges with visualization of millions of compounds by combining chemical and biological data, the expectations from mining the "Big Data" using advanced machine-learning methods, and their applications in polypharmacology prediction and target de-convolution in phenotypic screening. We show that the efficient exploration of billions of molecules requires the development of smart strategies. We also address the issue of secure information sharing without disclosing chemical structures, which is critical to enable bi-party or multi-party data sharing. Data sharing is important in the context of the recent trend of "open innovation" in pharmaceutical industry, which has led to not only more information sharing among academics and pharma industries but also the so-called "precompetitive" collaboration between pharma companies. At the end we highlight the importance of education in "Big Data" for further progress of this area.
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.