ArticleJournal of computer-aided molecular design2025
OPTUNA optimization for predicting chemical respiratory toxicity using ML models.
Article in Journal of computer-aided molecular design, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- AI/ML-based computational models for toxicity prediction.Environmental science and pollution research international · 2026Review
- Revisiting successful aging through a machine learning approach to quantifying the influence of chronic diseases.Scientific reports · 2025Article
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
Predicting molecular toxicity is an important stage in the process of drug discovery. It is directly related to medical destiny and human health. This paper presents an enhanced model for chemical respiratory toxicity prediction. It used a combination of molecular descriptors and term frequency - inverse document frequency (TF-IDF) based models with different machine learning algorithms. To address class imbalance, SMOTE is applied. Appropriate hyper-parameter tuning is required to generate a better system with a classifier. So, we adjusted the hyper-parameters of various models and used the adjusted parameters to train the model. We tuned hyper-parameters using OPTUNA. Internal and external validation were used to confirm the models' performance. According to the results, the model's internal validation accuracy and AUC using the random forest approach were 88.6% and 93.2%. For external validation, the model's accuracy value using random forest and Gradient Boosting Classifier were 92.2% with AUC 97%. Comparing these results with previous studies shows that our model performs better compared to them.
Indexed as
Identifiers
40285895What 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.