ArticleToxicology research2017
Modelling compound cytotoxicity using conformal prediction and PubChem HTS data.
Article in Toxicology research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- Uncertainty Quantification with Domain Classification Models for Acetylcholinesterase Inhibition.Journal of chemical information and modeling · 2026Article
- Reliable Uncertainty Under Class Imbalance and Distribution Shift: Class-Conditional Conformal Prediction of Multiple Sclerosis.medRxiv : the preprint server for health sciences · 2026Article
- Structural Similarity, Activity, and Toxicity of Mycotoxins: Combining Insights from Unsupervised and Supervised Machine Learning Algorithms.Journal of agricultural and food chemistry · 2025Article
- A Systematic Quantitative Approach to Rational Drug Design and the Discovery of Novel Human Antigen R (HuR) Inhibitors.Anti-cancer agents in medicinal chemistry · 2025Article
- CPSign: conformal prediction for cheminformatics modeling.Journal of cheminformatics · 2024Article
- Machine Learning Uncovers Natural Product Modulators of the 5-Lipoxygenase Pathway and Facilitates the Elucidation of Their Biological Mechanisms.ACS chemical biology · 2024Article
- Review of machine learning and deep learning models for toxicity prediction.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data.Scientific reports · 2022Article
- The effect of noise on the predictive limit of QSAR models.Journal of cheminformatics · 2021Article
- Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning.Journal of cheminformatics · 2021Article
- Assigning confidence to molecular property prediction.Expert opinion on drug discovery · 2021Article
- NICEdrug.ch, a workflow for rational drug design and systems-level analysis of drug metabolism.eLife · 2021Article
- CombiningChemical research in toxicology · 2021Article
- Revealing cytotoxic substructures in molecules using deep learning.Journal of computer-aided molecular design · 2020Article
- Predictive models for estimating cytotoxicity on the basis of chemical structures.Bioorganic & medicinal chemistry · 2020Article
- KnowTox: pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development.Journal of cheminformatics · 2020Article
- Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning.Chemical science · 2019Article
- Large scale comparison of QSAR and conformal prediction methods and their applications in drug discovery.Journal of cheminformatics · 2019Article
- Maximizing gain in high-throughput screening using conformal prediction.Journal of cheminformatics · 2018Article
- In silico toxicology: From structure-activity relationships towards deep learning and adverse outcome pathways.Wiley interdisciplinary reviews. Computational molecular scienceReview
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The assessment of compound cytotoxicity is an important part of the drug discovery process. Accurate predictions of cytotoxicity have the potential to expedite decision making and save considerable time and effort. In this work we apply class conditional conformal prediction to model the cytotoxicity of compounds based on 16 high throughput cytotoxicity assays from PubChem. The data span 16 cell lines and comprise more than 440 000 unique compounds. The data sets are heavily imbalanced with only 0.8% of the tested compounds being cytotoxic. We trained one classification model for each cell line and validated the performance with respect to validity and accuracy. The generated models deliver high quality predictions for both toxic and non-toxic compounds despite the imbalance between the two classes. On external data collected from the same assay provider as one of the investigated cell lines the model had a sensitivity of 74% and a specificity of 65% at the 80% confidence level among the compounds assigned to a single class. Compared to previous approaches for large scale cytotoxicity modelling, this represents a balanced performance in the prediction of the toxic and non-toxic classes. The conformal prediction framework also allows the modeller to control the error frequency of the predictions, allowing predictions of cytotoxicity outcomes with confidence.
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.