Computational Toxicology最新文献

{"title":"The first report on chronic toxicity assessment of metals towards Ceriodaphnia dubia using QSTR technique: A step towards healthier and safer human health and eco-system","authors":"Ankur Kumar ,&nbsp;Joyita Roy ,&nbsp;Probir Kumar Ojha","doi":"10.1016/j.comtox.2025.100357","DOIUrl":"10.1016/j.comtox.2025.100357","url":null,"abstract":"<div><div>Exposure of humans and other living organisms to metals (including heavy metals) can lead to serious chronic and acute health effects, which may sometimes be life-threatening. As a result, assessing the toxicity of heavy metals is essential. However, experimental toxicity data for heavy metals is limited, and their toxicity estimation can be highly costly, lengthy analysis durations, and may require animal testing. Therefore, <em>in-silico</em> approaches such as quantitative structure–activity relationship (QSAR) are a suitable alternative. In this work, we have developed multi-endpoints MLR-QSAR models to assess the chronic toxicity of heavy metal towards <em>Ceriodaphnia dubia</em> using 48 data points and obeying the Organization for Economic Cooperation and Development (OECD) guidelines. Intra-endpoint uni-variate models were developed to fill the toxicity data gaps between the endpoints (acute to chronic). The statistical results of the developed models (individual models M1-M4; R² = 0.691–0.738, Q²_LOO = 0.542–0.578, Q²_F1 = 0.673–0.732, Q²_F2 = 0.552–0.580, MAE_95%data = 0.437–0.753; intra-endpoints models IEM1-IEM9; R² = 0.952–0.988, Q²_LOO = 0.907–0.987, Q²_F1 = 0.885–0.991, Q²_F2 = 0.979–0.991, MAE_95%data = 0.120–0.436) infer that the models are robust, reliable, reproducible, and predictive. The descriptors contributing to the development of the model imply that the release of electrons, formation of cations, higher electronegativity, and the presence of neutrons in the heavy metals significantly influence the toxicity caused by the metals. Thus, this study presents <em>in silico</em> models aimed at controlling the exposure of living organisms to toxic heavy metals. It assesses both acute and chronic toxicity, addresses gaps in toxicity data, and strives to create healthier and safer ecosystems by strictly following the principles of reduction, replacement, and refinement (the RRR framework).</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100357"},"PeriodicalIF":3.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of progesterone receptor binding potency, agonism and antagonism using machine learning models","authors":"Nemanja Milošević ,&nbsp;Nataša Sukur Milošević ,&nbsp;Svetlana Fa Nedeljkovic ,&nbsp;Bojana Stanic ,&nbsp;Nebojsa Andric","doi":"10.1016/j.comtox.2025.100351","DOIUrl":"10.1016/j.comtox.2025.100351","url":null,"abstract":"<div><div>The use of Machine Learning (ML) models to predict the binding potency of chemicals to estrogen and androgen receptors has become well-established, helping in the prioritization of chemicals for endocrine disruption testing. However, the potential of ML models for other endocrine targets, such as the progesterone receptor (PR), remains underexplored. In this study, we developed an ML model to predict PR binding affinity and assess the agonistic/antagonistic properties of chemicals. The model achieved a training accuracy of 99.72% and a validation accuracy of 74.46%. External validation was conducted on a dataset of approximately 10,000 chemicals, including 5720 compounds from the training set for which there is a known outcome. External predictions aligned closely with experimental <em>in vitro</em> data, achieving an accuracy of 96.85%. Additionally, the model successfully predicted PR binding affinity and agonistic/antagonistic properties for chemicals without available experimental data. In summary, this study highlights the potential of ML as an effective tool for prioritizing chemicals for future <em>in vitro</em> and <em>in vivo</em> testing of PR binding potency and agonistic/antagonistic properties of chemicals.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100351"},"PeriodicalIF":3.1,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An R-based predictive model for skin-sensitizing potential of substances with known structures","authors":"Yuri Hatakeyama ,&nbsp;Kosuke Imai ,&nbsp;Hayato Nishida ,&nbsp;Shiho Oeda ,&nbsp;Tomomi Atobe ,&nbsp;Morihiko Hirota","doi":"10.1016/j.comtox.2025.100350","DOIUrl":"10.1016/j.comtox.2025.100350","url":null,"abstract":"<div><div>Evaluation of skin-sensitizing potential is important to confirm the safety of cosmetics. As animal testing is no longer permitted, several alternative methods based on the adverse outcome pathway (AOP) approach have been reported. In addition, integrated approaches to testing and assessment (IATA), which combine the results of multiple alternative methods to assess skin sensitization potential, have been developed. We have reported an artificial neural network (ANN) model for sensitization risk assessment using commercial software, QwikNet. In the present study, we constructed a new sensitization prediction model for substances with known structures using the free and open-source software R for statistical analysis, and compared the results with those of the QwikNet model. The R model was confirmed to show similar predictive performance for estimated concentration three (EC3) which is the concentration of a test substance needed to produce a stimulation index of 3 to the QwikNet model on the same training set of 134 compounds. The accuracy, overpredicted rate, and underpredicted rate of the R model were 81.3%, 10.4%, and 8.2%, respectively, versus 79.9%, 10.4%, and 9.7% for the QwikNet model. In case studies of compounds not included in the training set, the R model showed generally good predictive ability. For less-well-predicted substances, additional <em>in silico</em> and read-across evaluations complemented the ANN model and improved the predictive accuracy. This study demonstrates that the ANN model is portable to the R software system. Furthermore, the combination of ANN prediction with <em>in silico</em> predictions and read-across taking account of substructures improves the prediction of skin-sensitizing potential in a weight-of-evidence approach.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100350"},"PeriodicalIF":3.1,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating marginal likelihood approximations of dose–response relationship models in Bayesian benchmark dose methods for risk assessment","authors":"Sota Minewaki ,&nbsp;Tomohiro Ohigashi ,&nbsp;Takashi Sozu","doi":"10.1016/j.comtox.2025.100347","DOIUrl":"10.1016/j.comtox.2025.100347","url":null,"abstract":"<div><div>Benchmark dose (BMD; a dose associated with a specified change in response) is used to determine the point of departure for the acceptable daily intake of substances for humans. Multiple dose–response relationship models are considered in the BMD method. The Bayesian model averaging (BMA) method is commonly used, where several models are averaged based on their posterior probabilities, which are determined by calculating the marginal likelihood (ML). Several ML approximation methods are employed in standard software packages, such as BBMD, <span>ToxicR</span>, and the EFSA platform for the BMD method, because the ML cannot be analytically calculated. Although ML values differ among approximation methods, resulting in BMD estimates, this phenomenon is neither widely recognized nor quantitatively evaluated. In this study, we evaluated the agreement of BMD estimates among five ML approximation methods in the BMA method. The five ML approximation methods are (1) maximum likelihood estimation (MLE)-based Schwarz criterion, (2) Markov chain Monte Carlo (MCMC)-based Schwarz criterion, (3) Laplace approximation, (4) density estimation, and (5) bridge sampling. We used eight dose–response relationship models and three prior distributions used in BBMD and <span>ToxicR</span> for 518 experimental datasets. The agreement among the approximation methods tended to be low in the non-informative prior distribution. Although the agreements tended to be high in the informative prior distribution, they were low in some approximation methods. Since the approximation method and the prior distribution affect the agreement, their selection should be carefully considered when implementing BMD methods.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100347"},"PeriodicalIF":3.1,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143947516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of OECD QSAR Toolbox profilers for genotoxicity assessment of pesticides using the MultiCase genotoxicity database","authors":"Monika Kemény ,&nbsp;Colin M. North ,&nbsp;Felix M. Kluxen ,&nbsp;Markus Frericks ,&nbsp;Dragana Vukelic ,&nbsp;Sishuo Cao ,&nbsp;Roustem Saiakhov ,&nbsp;Mounika Girireddy","doi":"10.1016/j.comtox.2025.100356","DOIUrl":"10.1016/j.comtox.2025.100356","url":null,"abstract":"<div><div>Quantitative Structure Activity Relationship (QSAR) models are widely used for genotoxicity assessment in regulatory settings. <em>In silico</em> profilers are a special case of models capturing mechanistic insights specific to a particular toxicological endpoint or reflecting chemistry-related attributes that may not be directly associated with a defined mechanism of toxicity. This study explores the accuracy of using such profilers as a lower tier in genotoxicity assessment to inform regulatory concerns. Relevant profilers in the OECD QSAR Toolbox are investigated using an external validation dataset derived from the MultiCASE Genotoxicity database, which contains AMES mutagenicity and in vivo micronucleus (MNT) experimental results. The MNT dataset includes the commercial in vivo MNT dataset expanded with pesticide data from regulatory documents. This analysis incorporates the use of metabolism simulations by the OECD QSAR Toolbox to assess their influence on profiler performance. The present findings show that the absence of profiler alerts correlates well with experimentally negative outcomes. However, the calculated accuracy for the MNT-related and AMES-related profilers varies considerably (41%-78% for MNT-related profilers and 62%-88% for AMES-related profilers using the full set with and without consideration of metabolism). Incorporating metabolism simulations increases accuracy by 4–6% for the full AMES-dataset, and 4–16% for the full MNT-dataset. Together, genotoxicity assessment using the Toolbox profilers should include a critical evaluation of any triggered alerts, considering the overall performance statistics of the profilers presented within this work. Results from third-party QSAR models provide critical insights to complement the expert review of any profiler positive result, as profilers alone are not recommended to be used directly for prediction purpose.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100356"},"PeriodicalIF":3.1,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can graph similarity metrics be helpful for analogue identification as part of a read-across approach?","authors":"Brett Hagan ,&nbsp;Imran Shah ,&nbsp;Grace Patlewicz","doi":"10.1016/j.comtox.2025.100353","DOIUrl":"10.1016/j.comtox.2025.100353","url":null,"abstract":"<div><div>Read-across is a technique used to fill data gaps for substances lacking specific hazard data. The technique relies on identifying source analogues with relevant data that are ‘similar’ to the substance of interest (target). Typically, source analogues are identified on the basis of structural similarity but the evaluation of their suitability for read-across depends on other contexts of similarity. This manuscript aimed to review the ways in which source analogues are identified for read-across using chemical fingerprint/scaffold approaches before describing graph-based approaches including; graph kernel, graph embedding, and deep learning. To demonstrate how these could be practically used for analogue identification, five different toxicity datasets of varying size and diversity were selected that had been the subject of previous read-across or QSAR analyses. One dataset was an analogue set whereas the other four datasets comprised substances evaluated for their skin sensitisation, skin irritation, fathead minnow aquatic toxicity and genotoxicity potential. The analogues and their associated similarities using the different graph based approaches were compared with the outcomes from two chemical fingerprint approaches (ToxPrints and Morgan). The results for each dataset are briefly described. Based on the examples evaluated, graph kernel approaches were found to have some promise, in contrast unsupervised whole graph embedding approaches were ineffective for all the datasets evaluated. Graph convolutional networks produced meaningful embeddings for the genotoxicity dataset evaluated. Depending on use case, availability and size of training data, graph similarity approaches have the potential to play a larger role in analogue identification and evaluation for read-across.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100353"},"PeriodicalIF":3.1,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of Nipah virus drugs from FDA-approved drugs: An integrated computational approach","authors":"Panneerselvam Theivendren ,&nbsp;Selvaraj Kunjiappan ,&nbsp;Parasuraman Pavadai ,&nbsp;Natarajan Kiruthiga ,&nbsp;Anusuya Murugavel ,&nbsp;Avinash Dayalan","doi":"10.1016/j.comtox.2025.100354","DOIUrl":"10.1016/j.comtox.2025.100354","url":null,"abstract":"<div><div>The Nipah virus (NiV) is a highly virulent zoonotic pathogen that presents a substantial risk to public health, as limited therapeutic interventions are available. The present study utilizes a computational methodology to discover pharmaceutical substances that have been received from the database consisting of 4344 U.S. Food and Drug Administration (FDA)-approved drugs and have the potential to be repurposed to treat NiV infection. We have used molecular docking and dynamics simulation to evaluate the binding affinity and stability of the drugs against the key viral target, Ephrin-B2. The findings of our study demonstrate the presence of numerous FDA-approved drugs that display favourable binding interactions with the target of Ephrin-B2. Within this FDA-approved data set of drugs, we have identified certain FDA-approved drugs, such as Guamecycline, Ergotamine, Sancycline, Entrectinib, and Atogepant, which showed considerably better binding scores. The dynamic behaviour of ligand–protein interaction was evaluated using molecular dynamics simulation, which offered valuable insights into drug-target complexes’ temporal stability and conformational alterations. The results of docking studies indicate to active ingredients Guamecycline, Ergotamine, Sancycline, Entrectinib and Atogepant having notable inhibition of the Ephrin-B2 protein. According to the findings from the MD simulation, it was noted that Guamecycline displayed significant interaction with the Ephrin-B2 protein. Therefore, Guamecycline shows potential as a suitable primary chemical for treating NiV. Further, the sub-structures of Guamecycline were used to optimize and substantiate the stability of Guamecycline; in this relation sub, structure <strong>ZINC000169368545</strong> was correlated with Guamecycline, and the observed result showed that the Guamecycline was better lead moiety to inhibit the target Ephrin-B2.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100354"},"PeriodicalIF":3.1,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143947515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dual approach to flavonoid toxicity assessment: Bridging computational and experimental paradigms","authors":"Mriganka Das ,&nbsp;Sibashish Kityania ,&nbsp;Priyakshi Nath ,&nbsp;Rajat Nath ,&nbsp;Rashed N. Herqash ,&nbsp;Abdelaaty A. Shahat ,&nbsp;Deepa Nath ,&nbsp;Anupam Das Talukdar","doi":"10.1016/j.comtox.2025.100355","DOIUrl":"10.1016/j.comtox.2025.100355","url":null,"abstract":"<div><div>Flavonoids form a structurally diverse group of polyphenolic compounds with high ethnopharmacological relevance, primarily attributed to their antimicrobial and anticancer activity mediated by modulation of oxidative stress, induction of apoptosis, and regulation of the cell cycle. Their translatability to the clinic is critically hindered by multifaceted toxicities involving nephrotoxicity, cardiotoxicity, and respiratory issues often traceable to conserved structural motifs. In response, we adopted an integrative dual-methodological approach that linked thorough data mining across PubMed, Google Scholar, and PubChem for pharmacokinetic parameters and SMILES-based structural information to computational toxicity prediction using ProTox 3.0 and ADMET AI in order to unravel mechanistic endpoints of toxicity.Chemical drawing utilities like ChemSketch and ChemDraw supported the structural evaluations, and cross-referring DrugBank and <span><span>ClinicalTrials.gov</span><svg><path></path></svg></span> gave validation for clinical relevance. This computational model was further validated using in vitro and in vivo model systems, guaranteeing a comprehensive evaluation of flavonoid toxicity and therapeutic potential. Although flavonoids show great antimicrobial and anticancer potential, the translational roadblock arises from discrepancies between predictive models of toxicity and empirical validation, requiring sophisticated structure–activity relationship (SAR) analysis and integrative approaches to bridge computational-experimental gaps and enhance clinical relevance. This research highlights the need for a dual investigative approach, blending <em>in silico</em> and experimental paradigms, to maximize the predictive validity and translational potential of flavonoid-derived therapeutics.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100355"},"PeriodicalIF":3.1,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An exploration of the use of hybrid fingerprints in Generalized Read-Across and their impact on predictive performance for selected in vivo toxicity outcomes","authors":"Aubrey Leary ,&nbsp;Imran Shah ,&nbsp;Grace Patlewicz","doi":"10.1016/j.comtox.2025.100349","DOIUrl":"10.1016/j.comtox.2025.100349","url":null,"abstract":"<div><div>Read-across is a cost-efficient means of generating information for hazard assessment. Approaches such as Generalized Read-Across (GenRA) facilitate objective and reproducible read-across for untested substances. GenRA is a web application, and its prediction engine is also available as a python package (genra-py). Recent updates permit source analogues to be identified using ‘hybrid’ fingerprints, i.e. analogues identified based on more than one type of similarity measure. Herein, the performance of hybrid fingerprints relative to Morgan chemical fingerprints was evaluated for a selection of acute and chronic <em>in vivo</em> toxicity outcomes. Grid search and cross-validation on a dataset of 5,830 chemicals with rodent acute oral toxicity (LD₅₀) values were used to tune the hybrid weight hyperparameter for up to four chemical fingerprints (Morgan, Torsion, ToxPrint and Analog Identification Methodology (AIM)). The optimal hybrid fingerprint derived (52.12% Morgan, 23.40% ToxPrint, 12.44% AIM, 12.04% Torsion) outperformed Morgan fingerprints across all 10 folds of a cross-validation procedure (mean test set coefficient of determination (R²) 0.517 (Morgan) vs. 0.557 (hybrid)). The hybrid fingerprint was then used to make toxicity predictions for 2 other datasets, a set of 3,266 chemicals with oral chronic human equivalent benchmark dose values (mean test set R² 0.445 vs. 0.417 for Morgan) and a set of 9,443 chemicals with acute mammalian oral hazard classifications (mean balanced accuracy (BA) 0.577 vs 0.553 for Morgan). Overall, performance improved when using the hybrid fingerprint tuned for the acute toxicity dataset. Using the custom hybrid option in GenRA results in improved read-across predictions relative to current defaults.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100349"},"PeriodicalIF":3.1,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consensus model for skin sensitization assessment using a rule-based model and LLNA and GPMT statistics-based models","authors":"Ryoichi Murakami ,&nbsp;Mika Imamura ,&nbsp;Masakazu Tateshita ,&nbsp;Hajime Kojima ,&nbsp;Yasushi Hikida","doi":"10.1016/j.comtox.2025.100348","DOIUrl":"10.1016/j.comtox.2025.100348","url":null,"abstract":"<div><div>The potential for skin sensitization has traditionally been assessed <em>in vivo</em>; however, animal welfare concerns, the trend toward restrictions, and the prohibition of the use of animals have led to a shift toward the use of non-animal alternatives such as <em>in vitro</em> and <em>in silico</em> tools. <em>In silico</em> tools mainly include rule-based and statistics-based models. Although the use of multiple computational methods is recommended, many tools consist of only one method. Furthermore, skin sensitization develops through multiple key event (KE)/adverse outcome (AO) pathways, but many <em>in silico</em> tools consist of only one KE/AO. We constructed a consensus model based on three different independent skin sensitization KE/AOs from a rule-based model, a local lymph node assay (LLNA) statistics-based model, and a guinea pig maximization test (GPMT) statistics-based model. The rule-based model is based on KE1 and considers the metabolism of pre- and pro-haptens. The LLNA and GPMT statistics-based models are based on KE4 and AO, respectively, and characterized by the use of approximately 2000 and 3000 chemicals in the training dataset, respectively. These models use larger datasets than those previously reported. The constructed consensus model was tested on chemicals labeled with human results from OECD Guideline 497. The results showed that the performance of the majority-voting model was the highest, with a balanced accuracy of 78%. The model combines a wide range of chemical spaces with high prediction accuracy.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100348"},"PeriodicalIF":3.1,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}