Acute Toxicity-Supported Chronic Toxicity Prediction: A k-Nearest Neighbor Coupled Read-Across Strategy

Nearest neighbor pattern classification

Cover, T., & Hart, P. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1053964 · Cited by 16,936 · see citing works

The Principles of Humane Experimental Technique

(1960) · The Medical Journal of Australia · doi:10.5694/j.1326-5377.1960.tb73127.x · Cited by 5,002 · see citing works

PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints

Yap, C. W. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21707 · Cited by 2,979 · see citing works

Predicting new molecular targets for known drugs

Keiser, M. J., Setola, V., Irwin, J. J., Laggner, C., Abbas, A. I., Hufeisen, S. J., Jensen, N. H., Kuijer, M. B., Matos, R. C., Tran, T. B., et al. (2009) · Nature · doi:10.1038/nature08506 · Cited by 1,667 · see citing works

Estimation of toxic hazard—A decision tree approach

Cramer, G. M., Ford, R. A., & Hall, R. L. (1976) · Food and Cosmetics Toxicology · doi:10.1016/s0015-6264(76)80522-6 · Cited by 1,571 · see citing works

PubChem: a public information system for analyzing bioactivities of small molecules

Wang, Y., Xiao, J., Önal-Süzek, T., Zhang, J., Wang, J., & Bryant, S. H. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp456 · Cited by 1,465 · see citing works

ChemSpider: An Online Chemical Information Resource

Pence, H. E., & Williams, A. J. (2010) · Journal of Chemical Education · doi:10.1021/ed100697w · Cited by 1,277 · see citing works

Classification tools in chemistry. Part 1: linear models. PLS-DA

Ballabio, D., & Consonni, V. (2013) · Analytical Methods · doi:10.1039/c3ay40582f · Cited by 1,270 · see citing works

Polypharmacology: Challenges and Opportunities in Drug Discovery: Miniperspective

Anighoro, A., Bajorath, J., & Rastelli, G. (2014) · Journal of Medicinal Chemistry · doi:10.1021/jm5006463 · Cited by 1,131 · see citing works

The Chemistry Development Kit (CDK): An Open-Source Java Library for Chemo- and Bioinformatics

Steinbeck, C., Han, Y., Kühn, S., Horlacher, O., Luttmann, E., & Willighagen, E. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci025584y · Cited by 1,111 · see citing works

Electrotopological State Indices for Atom Types: A Novel Combination of Electronic, Topological, and Valence State Information

Hall, L. H., & Kier, L. B. (1995) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00028a014 · Cited by 770 · see citing works

Globally Harmonized System of Classification and Labelling of Chemicals (GHS)

(2016) · Globally harmonized system of classification and labelling of chemicals · doi:10.18356/a6a925c3-en · Cited by 511 · see citing works

Fundamentals of aquatic toxicology : methods and applications

Rand, G. M., & Petrocelli, S. R. (1985) · Hemisphere Pub. Corp. eBooks · Cited by 330 · see citing works

BINARY (PRESENCE-ABSENCE) SIMILARITY COEFFICIENTS

Cheetham, A. H., & Hazel, J. E. (1969) · Journal of Paleontology · Cited by 312 · see citing works

Kinetic Cell-Based Morphological Screening: Prediction of Mechanism of Compound Action and Off-Target Effects

Abassi, Y. A., Xi, B., Zhang, W., Ye, P., Kirstein, S. L., Gaylord, M. R., Feinstein, S. C., Wang, X., & Xu, X. Y. (2009) · Chemistry & Biology · doi:10.1016/j.chembiol.2009.05.011 · Cited by 254 · see citing works

The Nearest Neighbor Classification Rule with a Reject Option

Hellman, M. E. (1970) · IEEE Transactions on Systems Science and Cybernetics · doi:10.1109/tssc.1970.300339 · Cited by 219 · see citing works

Metabolic activation and drug toxicity

Nelson, S. D. (1982) · Journal of Medicinal Chemistry · doi:10.1021/jm00349a001 · Cited by 203 · see citing works

A generalized k-nearest neighbor rule

Patrick, E. A., & Fischer, F. P. (1970) · Information and Control · doi:10.1016/s0019-9958(70)90081-1 · Cited by 192 · see citing works

Targeted Cancer Therapies in the Twenty-First Century: Lessons From Imatinib

Stegmeier, F. P., Warmuth, M., Sellers, W. R., & Dorsch, M. (2010) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2009.297 · Cited by 192 · see citing works

K-Nearest Neighbor Classification Rule (pattern recognition) applied to nuclear magnetic resonance spectral interpretation

Kowalski, B. R., & Bender, C. F. (1972) · Analytical Chemistry · doi:10.1021/ac60316a008 · Cited by 191 · see citing works

Predictability of chronic toxicity from acute toxicity of chemicals in fish and aquatic invertebrates

Kenaga, E. E. (1982) · Environmental Toxicology and Chemistry · doi:10.1002/etc.5620010410 · Cited by 142 · see citing works

Use of category approaches, read-across and (Q)SAR: General considerations

Patlewicz, G. Y., Ball, N. S., Booth, E. D., Hulzebos, E., Zvinavashe, E., & Hennes, C. (2013) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2013.06.002 · Cited by 136 · see citing works

Quantitative and Mechanistic Read Across for Predicting the Skin Sensitization Potential of Alkenes Acting via Michael Addition

Enoch, S. J., Cronin, M., Schultz, T. W., & Madden, J. C. (2008) · Chemical Research in Toxicology · doi:10.1021/tx700322g · Cited by 116 · see citing works

Proposing a scientific confidence framework to help support the application of adverse outcome pathways for regulatory purposes

Patlewicz, G. Y., Simon, T. W., Rowlands, J. C., Budinsky, R. A., & Becker, R. A. (2015) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2015.02.011 · Cited by 108 · see citing works

TOXNET: An evolving web resource for toxicology and environmental health information

Wexler, P. (2001) · Toxicology · doi:10.1016/s0300-483x(00)00337-1 · Cited by 87 · see citing works

Hazard Evaluation Support System (HESS) for predicting repeated dose toxicity using toxicological categories

Sakuratani, Y., Zhang, H. Q., Nishikawa, S., Yamazaki, K., Yamada, T., Yamada, J., Gerova, K. D., Chankov, G., Mekenyan, O. G., & Hayashi, M. (2013) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2013.773375 · Cited by 79 · see citing works

Modeling Oral Rat Chronic Toxicity

Mazzatorta, P., Estévez, M. D., Coulet, M., & Schilter, B. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci8001974 · Cited by 61 · see citing works

The Sigma-Aldrich library of regulatory and safety data

Lenga, R. E., & Votoupal, K. L. (1993) · Medical Entomology and Zoology · Cited by 59 · see citing works

Toxicity of selected pesticides to freshwater shrimp, Paratya australiensis (Decapoda: Atyidae): Use of time series acute toxicity data to predict chronic lethality

Kumar, A., CORRELL, R. L., Grocke, S. G., & Bajet, C. M. (2009) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2009.09.001 · Cited by 59 · see citing works

Review of QSAR Models and Software Tools for predicting Acute and Chronic Systemic Toxicity

Lapenna, S., Gatnik, M. F., & Andrew, W. (2010) · Joint Research Centre (European Commission) · doi:10.2788/60766 · Cited by 55 · see citing works

Comparing the Potency of Chemicals with Multiple Modes of Action in Aquatic Toxicology: Acute Toxicity Due to Narcosis versus Reactive Toxicity of Acrylic Compounds

Freidig, A. P., Verhaar, H. J. M., & Hermens, J. L. M. (1999) · Environmental Science & Technology · doi:10.1021/es990251b · Cited by 54 · see citing works

Assessment of effect levels of chemicals from quantitative structure-activity relationship (QSAR) models. I. Chronic lowest-observed-adverse-effect level (LOAEL)

Mumtaz, M. M., Knauf, L. A., Reisman, D., Peirano, W. B., DeRosa, C. T., Gombar, V. K., Enslein, K., Carter, J. R., Blake, B. W., Huque, K. I., et al. (1995) · Toxicology Letters · doi:10.1016/0378-4274(95)03365-r · Cited by 51 · see citing works

Towards Global QSAR Model Building for Acute Toxicity: Munro Database Case Study

Chavan, S., Nicholls, I. A., Karlsson, B., Rosengren, A. M., Ballabio, D., Consonni, V., & Todeschini, R. (2014) · International Journal of Molecular Sciences · doi:10.3390/ijms151018162 · Cited by 45 · see citing works

A Fast K Nearest Neighbors Classification Algorithm

Pan, J., Qiao, Y., & Sun, S. (2004) · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · Cited by 35 · see citing works

k-nearest neighbour method: The influence of data transformations and metrics

Todeschini, R. (1989) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(89)80086-3 · Cited by 34 · see citing works

Chemical Category Formation and Read-Across for the Prediction of Toxicity

Enoch, S. J. (2009) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-1-4020-9783-6_7 · Cited by 33 · see citing works

A Critical Evaluation of the 2011 ECHA Reports on Compliance with the REACH and CLP Regulations and on the Use of Alternatives to Testing on Animals for Compliance with the REACH Regulation

Spielmann, H., Sauer, U. G., & Mekenyan, O. (2011) · Alternatives to Laboratory Animals · doi:10.1177/026119291103900509 · Cited by 29 · see citing works

A k-NN algorithm for predicting oral sub-chronic toxicity in the rat

Gadaleta, D. (2014) · ALTEX · doi:10.14573/altex.1405091 · Cited by 26 · see citing works

True prediction of lowest observed adverse effect levels

García‐Domenech, R., de Julián‐Ortiz, J. V., & Besalú, E. (2006) · Molecular Diversity · doi:10.1007/s11030-005-9007-z · Cited by 25 · see citing works

Reducing, Refining and Replacing the Use of Animals in Toxicity Testing

(2013) · Royal Society of Chemistry eBooks · doi:10.1039/9781849737920 · Cited by 24 · see citing works

Predictability and prediction of lowest observed adverse effect levels in a structurally heterogeneous set of chemicals

de Julián‐Ortiz, J. V., García‐Domenech, R., Gálvez, J., & Pogliani, L. (2005) · SAR and QSAR in environmental research · doi:10.1080/10659360500036927 · Cited by 22 · see citing works

A k-NN algorithm for predicting oral sub-chronic toxicity in the rat - Supplementary Data

Gadaleta, D. (2014) · ALTEX · doi:10.14573/altex.1405091s · Cited by 13 · see citing works

Untitled work

Rhodes (1997) · doi:10.1002/(sici)1097-4636(19971215)37:4

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,045 · see citing works

Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis

Wu, Y., & Wang, G. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19082358 · Cited by 193 · see citing works

Estimate ecotoxicity characterization factors for chemicals in life cycle assessment using machine learning models

Hou, P., Jolliet, O., Zhu, J., & Xu, M. (2019) · Environment International · doi:10.1016/j.envint.2019.105393 · Cited by 139 · see citing works

Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations

Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J., Dordick, J. S., & Das, P. (2023) · Scientific Reports · doi:10.1038/s41598-023-31169-8 · Cited by 122 · see citing works

The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review

Suriyaamporn, P., Pamornpathomkul, B., Patrojanasophon, P., Ngawhirunpat, T., Rojanarata, T., & Opanasopit, P. (2024) · AAPS PharmSciTech · doi:10.1208/s12249-024-02901-y · Cited by 77 · see citing works

GGL-Tox: Geometric Graph Learning for Toxicity Prediction

Jiang, J., Wang, R., & Wei, G. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01294 · Cited by 74 · see citing works

Efficient Toxicity Prediction via Simple Features Using Shallow Neural Networks and Decision Trees

Karim, A., Mishra, A., Newton, M. A. H., & Sattar, A. (2019) · ACS Omega · doi:10.1021/acsomega.8b03173 · Cited by 72 · see citing works

Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure–activity relationship (q-RASAR) with the application of machine learning

Banerjee, A., Kar, S., Roy, K., Patlewicz, G. Y., Charest, N., Benfenati, E., & Cronin, M. (2024) · Critical Reviews in Toxicology · doi:10.1080/10408444.2024.2386260 · Cited by 61 · see citing works

Leveraging machine learning models in evaluating ADMET properties for drug discovery and development

Magesh, V., Rao, G. C., Karthik, M. J., & Maddi, S. (2025) · ADMET & DMPK · doi:10.5599/admet.2772 · Cited by 66 · see citing works

MolToxPred: small molecule toxicity prediction using machine learning approach

Anjali, S., Jani, V., Sonavane, U., & Joshi, R. (2024) · RSC Advances · doi:10.1039/d3ra07322j · Cited by 54 · see citing works

Identification of structural alerts for liver and kidney toxicity using repeated dose toxicity data

Pizzo, F., Gadaleta, D., Lombardo, A., Nicolotti, O., & Benfenati, E. (2015) · Chemistry Central Journal · doi:10.1186/s13065-015-0139-7 · Cited by 46 · see citing works

A k-nearest neighbor classification of hERG K+ channel blockers

Chavan, S., Abdelaziz, A., Wiklander, J. G., & Nicholls, I. A. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9898-z · Cited by 37 · see citing works

The prediction of acute toxicity (LD50) for organophosphorus-based chemical warfare agents (V-series) using toxicology in silico methods

Noga, M., Michalska, A., & Jurowski, K. (2023) · Archives of Toxicology · doi:10.1007/s00204-023-03632-y · Cited by 29 · see citing works

Predicting Dose-Range Chemical Toxicity using Novel Hybrid Deep Machine-Learning Method

Limbu, S., Zakka, C., & Dakshanamurthy, S. (2022) · Toxics · doi:10.3390/toxics10110706 · Cited by 28 · see citing works

Integrated In Silico Models for the Prediction of No-Observed-(Adverse)-Effect Levels and Lowest-Observed-(Adverse)-Effect Levels in Rats for Sub-chronic Repeated-Dose Toxicity

Gadaleta, D., Marzo, M., Toropov, A. A., Toropova, A. P., Lavado, G. J., Escher, S. E., Dorne, J. L. C. M., & Benfenati, E. (2020) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.0c00176 · Cited by 25 · see citing works

The estimation of acute oral toxicity (LD50) of G-series organophosphorus-based chemical warfare agents using quantitative and qualitative toxicology in silico methods

Noga, M., Michalska, A., & Jurowski, K. (2024) · Archives of Toxicology · doi:10.1007/s00204-024-03714-5 · Cited by 14 · see citing works

Computational Toxicology

Schmidt, F. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.11534-x · Cited by 13 · see citing works

The acute toxicity of Novichok's degradation products using quantitative and qualitative toxicology in silico methods

Noga, M., Michalska, A., & Jurowski, K. (2024) · Archives of Toxicology · doi:10.1007/s00204-024-03695-5 · Cited by 12 · see citing works

Toxicity of Bromo-DragonFLY as a New Psychoactive Substance: Application of In Silico Methods for the Prediction of Key Toxicological Parameters Important to Clinical and Forensic Toxicology

Noga, M., & Jurowski, K. (2024) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.4c00105 · Cited by 11 · see citing works

Computational Models That Use a Quantitative Structure–Activity Relationship Approach Based on Deep Learning

Matsuzaka, Y., & Uesawa, Y. (2023) · Processes · doi:10.3390/pr11041296 · Cited by 11 · see citing works

Prediction and Interpretation Microglia Cytotoxicity by Machine Learning

Liu, Q., He, D., Fan, M., Wang, J., Cui, Z., Wang, H., Mi, Y., Li, N., Meng, Q., & Hou, Y. W. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00366 · Cited by 10 · see citing works

Co‐model for chemical toxicity prediction based on multi‐task deep learning

Li, Y. Y., Chen, L., Pu, C., Zang, C., Yan, Y., Chen, Y., Zhang, Y., & Liu, H. (2023) · Molecular Informatics · doi:10.1002/minf.202200257 · Cited by 11 · see citing works

Molecular designing of potential environmentally friendly PFAS based on deep learning and generative models

Yang, Y., Yang, Z., Pang, X., Cao, H., Sun, Y., Wang, L., Zhou, Z., Wang, P., Liang, Y., & Wang, Y. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.176095 · Cited by 10 · see citing works

In Silico Models for Repeated-Dose Toxicity (RDT): Prediction of the No Observed Adverse Effect Level (NOAEL) and Lowest Observed Adverse Effect Level (LOAEL) for Drugs

Pizzo, F., Gadaleta, D., & Benfenati, E. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-1960-5_11 · Cited by 9 · see citing works

Toxicity of the New Psychoactive Substance (NPS) Clephedrone (4-Chloromethcathinone, 4-CMC): Prediction of Toxicity Using In Silico Methods for Clinical and Forensic Purposes

Jurowski, K., & Niżnik, Ł. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25115867 · Cited by 8 · see citing works

Developing a Novel Read-Across Concept for Ecotoxicological Risk Assessment of Phosphate Chemicals: A Case Study

Lee, S., Ok, S.-Y., Moon, H., Seo, S., & Ra, J. (2024) · Toxics · doi:10.3390/toxics12010096 · Cited by 7 · see citing works

Combinatorial MD/QM studies to develop novel ionic liquid-based anticancer drug delivery systems with aminium derived from carbohydrates as cationic components

Kalhor, S., & Fattahi, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-74250-6 · Cited by 7 · see citing works

Explaining Chemical Toxicity using Missing FeaturesPreprint

Lim, K. W., Sharma, B., Das, P., Chenthamarakshan, V., & Dordick, J. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.12199 · Cited by 8 · see citing works

Reexamining the acute toxicity of chloropicrin: Comprehensive estimation using in silico methods

Noga, M., & Jurowski, K. (2025) · Toxicology in Vitro · doi:10.1016/j.tiv.2025.106033 · Cited by 5 · see citing works

Application of in silico methods to predict the acute toxicity of bicyclic organophosphorus compounds as potential chemical weapon

Noga, M., & Jurowski, K. (2025) · Archives of Toxicology · doi:10.1007/s00204-025-04000-8 · Cited by 5 · see citing works

Toxicity assessment of the novel psychoactive substance HU-210 (Hebrew University 210; CAS: 112830–95-2): First insight into toxicophores and critical toxicity parameters (acute toxicity, health effects, genotoxicity, skin and eye irritation, cardiotoxicity and endocrine disruption) using in silico methods for applications in clinical and forensic toxicology

Jurowski, K., & Kobylarz, D. (2025) · Toxicology Letters · doi:10.1016/j.toxlet.2025.05.012 · Cited by 4 · see citing works

Un enfoque basado en el aprendizaje automático para el diagnóstico urgente de envenenamiento en situaciones de emergencia

Ghanimi, R., Chouikri, K., Ghanimi, I., Ghanimi, F., & Soulaymani, A. (2023) · Data & Metadata · doi:10.56294/dm2023110 · Cited by 3 · see citing works

Predicting Environmental Chemical Toxicity using a New Hybrid Deep Machine Learning MethodPreprint

Limbu, S., Zakka, C., & Dakshanamurthy, S. (2021) · ChemRxiv · doi:10.26434/chemrxiv.13726258 · Cited by 3 · see citing works

Toxicity of New Psychoactive Substance (NPS): Threo-4-methylmethylphenidate (4-Mmph) – Prediction of toxicity using in silico methods

Niżnik, Ł., Jabłońska, K., Orczyk, M., Orzechowska, M., Toporowska-Kaźmierak, J., Sowińska, M., Jasińska, J., & Jurowski, K. (2024) · Toxicology in Vitro · doi:10.1016/j.tiv.2024.105891 · Cited by 2 · see citing works

An artificial intelligence-based approach for an urgent detection of the pesticide responsible of intoxication

Ghanimi, R., Ghanimi, F., Ghanimi, I., & Soulaymani, A. (2023) · Data & Metadata · doi:10.56294/dm2023114 · Cited by 2 · see citing works

Plant-derived compounds and artificial intelligence: Outsmarting microbial pathogens and antimicrobial resistance

Kumar, S., Kumar, S., Bithel, N., Sharma, A., & Pandey, A. (2026) · Phytomedicine · doi:10.1016/j.phymed.2026.158512 · Cited by 1 · see citing works

Detection of Pesticide Responsible of Intoxication: An Artificial Intelligence Based Method

Ghanimi, R., Ghanimi, F., Ghanimi, I., & Soulaymani, A. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-48465-0_12 · Cited by 1 · see citing works

Predicting Environmental Chemical Toxicity using a New Hybrid Deep Machine Learning MethodPreprint

Limbu, S., Zakka, C., & Dakshanamurthy, S. (2021) · ChemRxiv · doi:10.26434/chemrxiv.13726258.v1 · Cited by 1 · see citing works

Predicting the Toxicity of Biomolecules Using Graph Kernel

Manimegalai, R., Susmeta, A., Umayal, V. R., & Mohan, V. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-88297-5_9

Artificial intelligence in the discovery of new antibiotics

Saddari, A., Dalli, M., Lahmer, M., Ezrari, S., & Maleb, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-34059-8.00018-x

Read-Across Structural Analysis of PFAS Acute Oral Toxicity in Rats Powered by the Isalos Analytics Platform’s Automated Machine Learning

Theodori, A., Papavasileiou, K. D., Tsoumanis, A., Melagraki, G., & Afantitis, A. (2026) · Toxics · doi:10.3390/toxics14020152

Optimizing skin sensitization prediction across activity cliffs: a comparative analysis of K-nearest neighbours vs random Forest

Ukaegbu, D. C., Kopańska, K., Ranslow, P. B. D., & Maertens, A. (2026) · Frontiers in Toxicology · doi:10.3389/ftox.2026.1739746

Applicability of NAMs to support a read-across based hazard assessment workflow for systemic toxicity: a conazole case study

Islam, B., Irwan, J., Wijaya, L., ter Braak, B., Jamalpoor, A., Meijer, T., Budin, C., de Boer, T., Liu, S., Wedler, M., et al. (2026) · Archives of Toxicology · doi:10.1007/s00204-026-04554-1

A k-NN algorithm for predicting oral sub-chronic toxicity in the rat

Gadaleta, D. (2014) · ALTEX · doi:10.14573/altex.1405091 · Cited by 26 · see citing works

A k-NN algorithm for predicting oral sub-chronic toxicity in the rat - Supplementary Data

Gadaleta, D. (2014) · ALTEX · doi:10.14573/altex.1405091s · Cited by 13 · see citing works

Integrated In Silico Models for the Prediction of No-Observed-(Adverse)-Effect Levels and Lowest-Observed-(Adverse)-Effect Levels in Rats for Sub-chronic Repeated-Dose Toxicity

Gadaleta, D., Marzo, M., Toropov, A. A., Toropova, A. P., Lavado, G. J., Escher, S. E., Dorne, J. L. C. M., & Benfenati, E. (2020) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.0c00176 · Cited by 25 · see citing works

Development of classification models for predicting chronic toxicity of chemicals to Daphnia magna and Pseudokirchneriella subcapitata

Ding, F. H., Wang, Z., Yang, X., Shi, L., Liu, J., & Chen, G. (2018) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2018.1545694 · Cited by 33 · see citing works

Towards new computational tools for predicting toxicity

Chavan, S. (2016) · KTH Publication Database DiVA (KTH Royal Institute of Technology)