ArticleScientific reports2018
DPubChem: a web tool for QSAR modeling and high-throughput virtual screening.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
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
12 citing papers in PubMed, 66 citations in OpenAlex.
- Drug Repurposing: Insights into Current Advances and Future Applications.Current medicinal chemistry · 2025Review
- Leveraging Cell Painting Images to Expand the Applicability Domain and Actively Improve Deep Learning Quantitative Structure-Activity Relationship Models.Chemical research in toxicology · 2023Article
- FP-MAP: an extensive library of fingerprint-based molecular activity prediction tools.Frontiers in chemistry · 2023Article
- Application of Computational Biology and Artificial Intelligence in Drug Design.International journal of molecular sciences · 2022Review
- Gains from no real PAINS: Where 'Fair Trial Strategy' stands in the development of multi-target ligands.Acta pharmaceutica Sinica. B · 2021Review
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.Molecular diversity · 2021Review
- Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace.Briefings in bioinformatics · 2021Review
- Machine and deep learning approaches for cancer drug repurposing.Seminars in cancer biology · 2021Review
- Repositioning of 8565 Existing Drugs for COVID-19.The journal of physical chemistry letters · 2020Article
- Review
- Methodology of aiQSAR: a group-specific approach to QSAR modelling.Journal of cheminformatics · 2019Article
- Integrated bioinformatics analysis of As, Au, Cd, Pb and Cu heavy metal responsive marker genes throughPeerJ · 2019Article
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 at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput screening (HTS) performs the experimental testing of a large number of chemical compounds aiming to identify those active in the considered assay. Alternatively, faster and cheaper methods of large-scale virtual screening are performed computationally through quantitative structure-activity relationship (QSAR) models. However, the vast amount of available HTS heterogeneous data and the imbalanced ratio of active to inactive compounds in an assay make this a challenging problem. Although different QSAR models have been proposed, they have certain limitations, e.g., high false positive rates, complicated user interface, and limited utilization options. Therefore, we developed DPubChem, a novel web tool for deriving QSAR models that implement the state-of-the-art machine-learning techniques to enhance the precision of the models and enable efficient analyses of experiments from PubChem BioAssay database. DPubChem also has a simple interface that provides various options to users. DPubChem predicted active compounds for 300 datasets with an average geometric mean and F
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.