ArticleScientific reports2023
Making sense of chemical space network shows signs of criticality.
Article in Scientific reports, 2023. 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
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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.
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Who cites it
11 citing papers in PubMed.
- Article
- Enhancing Drug Response Prediction in Epilepsy with Emerging Multimodal Models: Focus on Clinical, Pharmacologic, and Genomic Factors.CNS drugs · 2026Review
- Titania: an integrated tool for in silico molecular property prediction and NAM-based modeling.Molecular diversity · 2025Article
- From High Dimensions to Human Insight: Exploring Dimensionality Reduction for Chemical Space Visualization.Molecular informatics · 2025Article
- Applicability Domain for Trustable Predictions.Methods in molecular biology (Clifton, N.J.) · 2025Article
- QSAR: Using the Past to Study the Present.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Machine Learning in Early Prediction of Metabolism of Drugs.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Approaching Pharmacological Space: Events and Components.Methods in molecular biology (Clifton, N.J.) · 2025Article
- TIRESIA and TISBE: Explainable Artificial Intelligence Based Web Platforms for the Transparent Assessment of the Developmental Toxicity of Chemicals and Drugs.Methods in molecular biology (Clifton, N.J.) · 2025Article
- From Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors.ChemMedChem · 2024Article
- Recent Advances in the Discovery of Novel Drugs on Natural Molecules.Biomedicines · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chemical space modelling has great importance in unveiling and visualising latent information, which is critical in predictive toxicology related to drug discovery process. While the use of traditional molecular descriptors and fingerprints may suffer from the so-called curse of dimensionality, complex networks are devoid of the typical drawbacks of coordinate-based representations. Herein, we use chemical space networks (CSNs) to analyse the case of the developmental toxicity (Dev Tox), which remains a challenging endpoint for the difficulty of gathering enough reliable data despite very important for the protection of the maternal and child health. Our study proved that the Dev Tox CSN has a complex non-random organisation and can thus provide a wealth of meaningful information also for predictive purposes. At a phase transition, chemical similarities highlight well-established toxicophores, such as aryl derivatives, mostly neurotoxic hydantoins, barbiturates and amino alcohols, steroids, and volatile organic compounds ether-like chemicals, which are strongly suspected of the Dev Tox onset and can thus be employed as effective alerts for prioritising chemicals before testing.
Identifiers
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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.