A benchmark dataset and interpretable deep learning framework for drug-induced developmental neurotoxicity prediction.
The 13 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
- [1] § Materials and methods › Model construction and training strategy › Model training and evaluation ↔ 3-DNN_MACCS_modeling/DNN-test_selection.py, lines 33–67 · score 0.70 · monitoring validation, model selection, cross validation, weights, optimal, epochs
- [2] § Materials and methods › Performance metrics and statistical analysis ↔ 3-DNN_MACCS_modeling/DNN-independent validation.py, lines 208–293 · score 0.69 · F1 score, Matthew, TN, TP, FN, precision
- [3] § Materials and methods › Performance metrics and statistical analysis ↔ 3-DNN_MACCS_modeling/DNN-test_selection.py, lines 560–599 · score 0.67 · predicted toxic, evaluation metrics, F1 score, precision, recall, accuracy
- [4] § Materials and methods › Model construction and training strategy › Model training and evaluation ↔ 3-DNN_MACCS_modeling/DNN-training-5-fold-CV.py, lines 34–65 · score 0.65 · fold cross validation, network architectures, overfitting, epochs, training, Model
- [5] § Results and discussion › Analysis of the training stability and generalization ability of the DNN_MACCS model ↔ 3-DNN_MACCS_modeling/DNN-test_selection.py, lines 889–968 · score 0.64 · developmental neurotoxicity prediction, validation AUC, F1 score, dnn maccs, overfitting, configuration
- [6] § Materials and methods › Model interpretability analysis ↔ 4-applicability_domain_SHAP_analysis/SHAP-analysisl.py, lines 1056–1121 · score 0.62 · SHapley, exPlanations, Model interpretability, Additive, global, transparency
- [7] § Materials and methods › Benchmark dataset preparation › Data partitioning of the benchmark dataset ↔ 1-Benchmark_Dataset_Preparation/plot_umap_chemical_space.py, the whole file · a weak match · score 0.59 · chemical space, DNT positive, UMAP, benchmark, SMILES, fingerprints
- [8] § Results and discussion › Strengths and limitations ↔ 4-applicability_domain_SHAP_analysis/Application_Domain_UMAP_tSNE.py, lines 429–462 · score 0.58 · broader chemical, applicability domain, experimentally validated, model generalizability, chemical space, expand
- [9] § Results and discussion › Analysis of the training stability and generalization ability of the DNN_MACCS model ↔ 3-DNN_MACCS_modeling/DNN-test_selection.py, lines 889–968 · score 0.56 · potential overfitting, best validation, dnn maccs, loss, epochs, accuracy
- [10] § Materials and methods › Benchmark dataset preparation › Preprocessing of positive samples ↔ 1-Benchmark_Dataset_Preparation/DNTREF_curation.py, lines 25–46 · score 0.54 · heavy atoms, mixtures, parent, curation, SMILES
- [11] § Materials and methods › Chemical space visualization and applicability domain analysis ↔ 4-applicability_domain_SHAP_analysis/Application_Domain_UMAP_tSNE.py, lines 198–201 · score 0.52 · Dimensionality reduction, UMAP, Neighbor, SNE, domain
- [12] § Materials and methods › Benchmark dataset preparation ↔ 3-DNN_MACCS_modeling/DNN-test_selection.py, lines 1014–1134 · score 0.52 · model training, developmental neurotoxicity prediction, pipeline, selection, preprocessing
- [13] § Materials and methods › Benchmark dataset preparation › Preprocessing of presumed negative samples and construction of a balanced benchmark dataset ↔ 1-Benchmark_Dataset_Preparation/hard_negative_matching.py, lines 54–90 · score 0.50 · hard negative matching, greedy, MW, log, benchmark
Paper
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The authors' code
Python · 1,147 lines · 51 KB · no license · 5 matches
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Overview
- Shandong Key Laboratory of Digital Diagnosis and Treatment of Thoracic Oncology, Shandong Engineering Research Center of Precision Diagnosis and Treatment Technology for Neuro-Oncology, Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital Jinan 250014 China
- Department of Pharmacy, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences Beijing 100730 China
- State Key Laboratory of Neurology and Oncology Drug Development, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital Jinan 250014 China
Abstract
Developmental neurotoxicity (DNT) represents a critical yet underevaluated toxicity endpoint within current chemical safety assessment frameworks, particularly in the context of drug development. In the present study, we constructed a standardized benchmark dataset comprising 2724 structurally curated compounds, including 1362 DNT-positive chemicals derived from the Developmental Neurotox Reference List (DNTREF) of the U.S. Environmental Protection Agency (EPA) and 1362 property-matched presumed negative compounds (non-neuroactive drug-like compounds) selected from the ChEMBL database. Using this benchmark dataset, we systematically evaluated seven representative quantitative structure activity relationship (QSAR) modeling paradigms, covering traditional machine learning, deep neural networks, molecular language models, and graph neural networks. Across all models, the area under the receiver operating characteristic curve (AUC) values ranged from 0.85 to 0.90 on an independent test set. Among these, a deep neural network based on MACCS fingerprints achieved the highest sensitivity in identifying DNT-positive compounds, supporting its utility in early-stage drug safety screening where minimizing false negatives is critical. Furthermore, SHAP-based interpretability analysis revealed key structural motifs associated with DNT, highlighting the roles of hydrophobic aromatic frameworks, electrophilic reactivity, and polar functional groups in modulating predicted neurodevelopmental risk. Collectively, this study provides a reproducible benchmark dataset and an interpretable QSAR framework that supports early DNT risk prioritization and offers actionable structural insights for medicinal chemistry optimization in drug discovery.
Reproduced under the paper's license (CC BY-NC), from the paper cited above.
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lixiao1688/DNT_Benchmark_Dataset_Model
514073b77308152898322ed1e5380f12c0f7b426, 27 January 2026Availability: 1 check, the latest on 27 September 2026: the link answers
- 27 September 2026: the link answers
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aration/ — Python, 136 lines, 1 match, shown from its sourceDNTREF_curation.py - 1-Benchmark_Dataset_Prep
aration/ — Python, 105 lines, 1 match, shown from its sourcehard_negative_matching.p y - 1-Benchmark_Dataset_Prep
aration/ — Python, 59 lines, 1 match, shown from its sourceplot_umap_chemical_space .py - 3-DNN_MACCS_modeling/
DNN-independent validation.py — Python, 778 lines, 1 match, shown from its source - 3-DNN_MACCS_modeling/
DNN-test_selection.py — Python, 1,147 lines, 5 matches, shown from its source - 3-DNN_MACCS_modeling/
DNN-training-5-fold-CV.p — Python, 628 lines, 1 match, shown from its sourcey - 4-applicability_domain_S
HAP_analysis/ — Python, 462 lines, 2 matches, shown from its sourceApplication_Domain_UMAP_ tSNE.py - 4-applicability_domain_S
HAP_analysis/ — Python, 1,220 lines, 1 match, shown from its sourceSHAP-analysisl.py - README.md — Text, 46 lines, shown from its source
The paper's code and data availability statement is in the Data section.
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Data
No dataset and no data link were found in the paper.
Data and model availability
The developmental neurotoxicity benchmark dataset constructed in this study includes standardized SMILES representations of all compounds, corresponding binary toxicity labels, and the fixed training, validation, and independent test set partitions used for model development and evaluation. These data are deposited in a public repository to ensure full reproducibility of the reported results. The dataset is expected to serve as a valuable resource for the computational toxicology and drug discovery communities, enabling further methodological development and benchmarking.
In addition, the scripts for MACCS fingerprint generation, the trained weights of the optimal DNN_MACCS model, and all scripts used for model training, inference, and SHAP-based interpretability analysis will be made publicly available.
All scripts for AD analysis and chemical space visualization using UMAP and t-SNE will also be released to support transparent assessment of model scope and result interpretation.
Reproduced under the paper's license (CC BY-NC), from the paper cited above.
Data availability
The data utilized in this study were derived from the DNTREF from U.S. EPA, and ChEMBL database. Complete data (including processed datasets, and all scripts necessary to reproduce the study) is available on GitHub at https://
Supplementary information (SI): detailed data processing for benchmark dataset; detailed network architectures, hyperparameter settings, and training procedures; a summary of predictive performance for all models on validation set; unified chemical standardization process for data collection, structure preprocessing, and sample selection; training history and model comparison of the DNN_MACCS model. See DOI: https://
Reproduced under the paper's license (CC BY-NC), from the paper cited above.
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 4 funders, 46 references.
Cite
This paper
Ma, H., Zhang, W., Liu, F., Ni, R., Wang, X., Sun, Y., Sun, X., & Li, X. (2026). A benchmark dataset and interpretable deep learning framework for drug-induced developmental neurotoxicity prediction. RSC advances, 16(36), 37450-37464. https://
BibTeX
@article{ma2026benchmark
author = {Ma, Hongting and Zhang, Wenhui and Liu, Fengxi and Ni, Rong and Wang, Xue and Sun, Yanying and Sun, Xuelin and Li, Xiao},
title = {{A benchmark dataset and interpretable deep learning framework for drug-induced developmental neurotoxicity prediction}},
journal = {RSC advances},
year = {2026},
month = jul,
volume = {16},
number = {36},
pages = {37450--37464},
publisher = {Royal Society of Chemistry},
issn = {2046-2069},
doi = {10.1039/
url = {https://
pmid = {42440932},
pmcid = {PMC13334446}
}
RIS
TY - JOUR
AU - Ma, Hongting
AU - Zhang, Wenhui
AU - Liu, Fengxi
AU - Ni, Rong
AU - Wang, Xue
AU - Sun, Yanying
AU - Sun, Xuelin
AU - Li, Xiao
TI - A benchmark dataset and interpretable deep learning framework for drug-induced developmental neurotoxicity prediction
T2 - RSC advances
J2 - RSC Adv
PY - 2026
DA - 2026/
VL - 16
IS - 36
SP - 37450
EP - 37464
SN - 2046-2069
PB - Royal Society of Chemistry
DO - 10.1039/
UR - https://
LA - en
ER -
CSL-JSON
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