Toxicity prediction based on artificial intelligence: A multidisciplinary overview

Importance of pollinators in changing landscapes for world crops

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The Principles of Humane Experimental Technique

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ProTox-II: a webserver for the prediction of toxicity of chemicals

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The State of Food Insecurity in the World

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admetSAR: A Comprehensive Source and Free Tool for Assessment of Chemical ADMET Properties

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Patterns of widespread decline in North American bumble bees

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admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties

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Toxicity testing in the 21st century: A vision and a strategy

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The CompTox Chemistry Dashboard: a community data resource for environmental chemistry

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Photonic crystal nanocavity assisted rejection ratio tunable notch microwave photonic filter

Long, Y., Xia, J., Zhang, Y., Dong, J., & Wang, J. (2017) · Scientific Reports · doi:10.1038/srep40223 · Cited by 842 · see citing works

From machine learning to deep learning: progress in machine intelligence for rational drug discovery

Zhang, L., Tan, J., Han, D., & Zhu, H. (2017) · Drug Discovery Today · doi:10.1016/j.drudis.2017.08.010 · Cited by 760 · see citing works

In silico toxicology: computational methods for the prediction of chemical toxicity

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Systematic review and meta-analysis of the adverse health effects of ambient PM2.5 and PM10 pollution in the Chinese population

Lu, F., Xu, D., Cheng, Y., Dong, S., Guo, C., Jiang, X., & Zheng, X. (2014) · Environmental Research · doi:10.1016/j.envres.2014.06.029 · Cited by 721 · see citing works

Deep learning improves prediction of drug–drug and drug–food interactions

Ryu, J. Y., Kim, H. U., & Lee, S. Y. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1803294115 · Cited by 662 · see citing works

OPERA models for predicting physicochemical properties and environmental fate endpoints

Mansouri, K., Grulke, C., Judson, R., & Williams, A. J. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0263-1 · Cited by 635 · see citing works

Application of Deep Learning in Food: A Review

Zhou, L., Zhang, C., Liu, F., Qiu, Z., & He, Y. (2019) · Comprehensive Reviews in Food Science and Food Safety · doi:10.1111/1541-4337.12492 · Cited by 566 · see citing works

Predicting modes of toxic action from chemical structure: Acute toxicity in the fathead minnow ( Pimephales promelas )

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Toxicological screening

Parasuraman, S. (2011) · Journal of Pharmacology and Pharmacotherapeutics · doi:10.4103/0976-500x.81895 · Cited by 543 · see citing works

Pathway Based Analysis of Mutation Data Is Efficient for Scoring Target Cancer Drugs

Zolotovskaia, M., Sorokin, M., Emelianova, A. A., Borisov, N., Kuzmin, D., Borger, P., Garazha, A., & Buzdin, A. A. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.00001 · Cited by 531 · see citing works

Predicting drug metabolism: experiment and/or computation?

Kirchmair, J., Göller, A. H., Lang, D. H., Kunze, J., Testa, B., Wilson, I. D., Glen, R. C., & Schneider, G. (2015) · Nature Reviews Drug Discovery · doi:10.1038/nrd4581 · Cited by 507 · see citing works

Emergy Analysis and Sustainability Efficiency Analysis of Different Crop‐Based Biodiesel in Life Cycle Perspective

Ren, J., Manzardo, A., Mazzi, A., Fedele, A., & Scipioni, A. (2013) · The Scientific World JOURNAL · doi:10.1155/2013/918514 · Cited by 490 · see citing works

Big data and artificial intelligence modeling for drug discovery

Zhu, H. (2020) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010919-023324 · Cited by 455 · see citing works

Communications in Computer and Information Science

Fischer, T. (2017) · Cited by 415 · see citing works

Deep Learning for Drug-Induced Liver Injury

Xu, Y., Dai, Z., Chen, F., Gao, S., Pei, J., & Lai, L. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00238 · Cited by 385 · see citing works

Artificial Intelligence for Drug Toxicity and Safety

Basile, A. O., Yahi, A., & Tatonetti, N. P. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.07.005 · Cited by 385 · see citing works

Support vector machines in water quality management

Singh, K. P., Basant, N., & Gupta, S. (2011) · Analytica Chimica Acta · doi:10.1016/j.aca.2011.07.027 · Cited by 357 · see citing works

Deep learning in drug discovery: opportunities, challenges and future prospects

Lavecchia, A. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.07.006 · Cited by 353 · see citing works

A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials

Gayvert, K. M., Madhukar, N. S., & Elemento, O. (2016) · Cell chemical biology · doi:10.1016/j.chembiol.2016.07.023 · Cited by 310 · see citing works

Chemical regulators have overreached

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Spatiotemporal prediction of continuous daily PM2.5 concentrations across China using a spatially explicit machine learning algorithm

Zhan, Y., Luo, Y., Deng, X., Chen, H., Grieneisen, M. L., Shen, X., Zhu, L., & Zhang, M. (2017) · Atmospheric Environment · doi:10.1016/j.atmosenv.2017.02.023 · Cited by 258 · see citing works

CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods

Zhang, L., Ai, H., Chen, W., Yin, Z., Hu, H., Zhu, J., Zhao, J. H., Zhao, Q., & Liu, H. (2017) · Scientific Reports · doi:10.1038/s41598-017-02365-0 · Cited by 255 · see citing works

Managing the challenge of drug-induced liver injury: a roadmap for the development and deployment of preclinical predictive models

Weaver, R. J., Blomme, E. A. G., Chadwick, A. E., Copple, I. M., Gerets, H. H. J., Goldring, C. E., Guillouzo, A., Hewitt, P. G., Ingelman‐Sundberg, M., Jensen, K. G., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0048-x · Cited by 254 · see citing works

The Next Era: Deep Learning in Pharmaceutical Research

Ekins, S. (2016) · Pharmaceutical Research · doi:10.1007/s11095-016-2029-7 · Cited by 237 · see citing works

Hyperspectral technique combined with deep learning algorithm for detection of compound heavy metals in lettuce

Zhou, X. Y., Jun, S., Tian, Y., Lu, B., Hang, Y., & Chen, Q. (2020) · Food Chemistry · doi:10.1016/j.foodchem.2020.126503 · Cited by 235 · see citing works

Drug drug interaction extraction from biomedical literature using syntax convolutional neural network

Zhao, Z., Yang, Z., Luo, L., Lin, H., & Wang, J. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw486 · Cited by 223 · see citing works

Integrating tools for non-targeted analysis research and chemical safety evaluations at the US EPA

Sobus, J. R., Wambaugh, J. F., Isaacs, K., Williams, A. J., McEachran, A. D., Richard, A. M., Grulke, C., Ulrich, E. M., Rager, J. E., Strynar, M. J., et al. (2017) · Journal of Exposure Science & Environmental Epidemiology · doi:10.1038/s41370-017-0012-y · Cited by 214 · see citing works

Woody biomass phytoremediation of contaminated brownfield land

French, C. J., DICKINSON, N. M., & Putwain, P. D. (2005) · Environmental Pollution · doi:10.1016/j.envpol.2005.08.065 · Cited by 209 · see citing works

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

Tyzack, J. D., & Kirchmair, J. (2018) · Chemical Biology & Drug Design · doi:10.1111/cbdd.13445 · Cited by 209 · see citing works

Non-animal methods to predict skin sensitization (II): an assessment of defined approaches

Kleinstreuer, N., Hoffmann, S., Alépée, N., Allen, D., Ashikaga, T., Casey, W., Clouet, E., Cluzel, M., Desprez, B., Gellatly, N. L., et al. (2018) · Critical Reviews in Toxicology · doi:10.1080/10408444.2018.1429386 · Cited by 207 · see citing works

Drug-drug interaction extraction from biomedical texts using long short-term memory network

Sahu, S. K., & Anand, A. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.08.005 · Cited by 206 · see citing works

In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning

Zang, Q., Mansouri, K., Williams, A. J., Judson, R., Allen, D., Casey, W., & Kleinstreuer, N. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00625 · Cited by 199 · 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

Drug–drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths

Zhang, Y., Zheng, W., Lin, H., Wang, J., Yang, Z., & Dumontier, M. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx659 · Cited by 192 · see citing works

An Overview of Machine Learning and Big Data for Drug Toxicity Evaluation

Vo, A. H., Vleet, T. R. V., Gupta, R. R., Liguori, M. J., & Rao, M. S. (2019) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.9b00227 · Cited by 191 · see citing works

Differences in Birth Weight Associated with the 2008 Beijing Olympics Air Pollution Reduction: Results from a Natural Experiment

Rich, D. Q., Liu, K., Zhang, J., Thurston, S. W., Stevens, T. P., Pan, Y., Kane, C., Weinberger, B., Ohman-Strickland, P., Woodruff, T. J., et al. (2015) · Environmental Health Perspectives · doi:10.1289/ehp.1408795 · Cited by 174 · see citing works

Computational determination of hERG-related cardiotoxicity of drug candidates

Lee, H., Yu, M., Kazmi, S. R., Oh, S. Y., Rhee, K., Bae, M., Lee, B. H., Shin, D., Oh, K., Ceong, H.-T., et al. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2814-5 · Cited by 162 · see citing works

The prediction of food additives in the fruit juice based on electronic nose with chemometrics

Qiu, S., & Wang, J. (2017) · Food Chemistry · doi:10.1016/j.foodchem.2017.03.011 · Cited by 161 · see citing works

EPA’s DSSTox database: History of development of a curated chemistry resource supporting computational toxicology research

Grulke, C., Williams, A., Thillanadarajah, I., & Richard, A. (2019) · Computational Toxicology · doi:10.1016/j.comtox.2019.100096 · Cited by 161 · see citing works

New trends in the analytical determination of emerging contaminants and their transformation products in environmental waters

Agüera, A., Bueno, M. J. M., & Fernández‐Alba, A. R. (2013) · Environmental Science and Pollution Research · doi:10.1007/s11356-013-1586-0 · Cited by 158 · see citing works

Antimicrobial Activity of Necklace Orchids is Phylogenetically Clustered and can be Predicted With a Biological Response Method

Wati, R. K., Graaf, E. D., Bogarín, D., Heijungs, R., van Vugt, R., SMETS, E. F., & Gravendeel, B. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.586345 · Cited by 156 · see citing works

Detection of drug–drug interactions through data mining studies using clinical sources, scientific literature and social media

Vilar, S., Friedman, C., & Hripcsak, G. M. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx010 · Cited by 155 · see citing works

Comparison of the results of response surface methodology and artificial neural network for the biosorption of lead using black cumin

Bingöl, D., Hercan, M., Elevli, S., & Kılıç, E. (2012) · Bioresource Technology · doi:10.1016/j.biortech.2012.02.084 · Cited by 153 · see citing works

Fusion of machine vision technology and AlexNet-CNNs deep learning network for the detection of postharvest apple pesticide residues

Jiang, B., He, J., Yang, S., Fu, H., Li, T., Song, H., & He, D. (2019) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2019.02.001 · Cited by 152 · see citing works

Machine learning-guided discovery and design of non-hemolytic peptides

Plisson, F., Ramírez‐Sánchez, O., & Martínez-Hernández, C. (2020) · Scientific Reports · doi:10.1038/s41598-020-73644-6 · Cited by 147 · see citing works

Computational Tool for Risk Assessment of Nanomaterials: Novel QSTR-Perturbation Model for Simultaneous Prediction of Ecotoxicity and Cytotoxicity of Uncoated and Coated Nanoparticles under Multiple Experimental Conditions

Kleandrova, V. V., Luan, F., González‐Díaz, H., Ruso, J. M., Speck‐Planche, A., & Cordeiro, M. N. D. S. (2014) · Environmental Science & Technology · doi:10.1021/es503861x · Cited by 145 · see citing works

Computer-aided nanotoxicology: assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR-perturbation approach

Luan, F., Kleandrova, V. V., González‐Díaz, H., Ruso, J. M., Melo, A., Speck‐Planche, A., & Cordeiro, M. N. D. S. (2014) · Nanoscale · doi:10.1039/c4nr01285b · Cited by 145 · see citing works

ADMET Evaluation in Drug Discovery. 19. Reliable Prediction of Human Cytochrome P450 Inhibition Using Artificial Intelligence Approaches

Wu, Z., Lei, T., Shen, C., Wang, Z., Cao, D., & Hou, T. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00801 · Cited by 142 · see citing works

Drug-induced liver injury severity and toxicity (DILIst): binary classification of 1279 drugs by human hepatotoxicity

Thakkar, S., Li, T., Liu, Z., Wu, L., Roberts, R., & Tong, W. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2019.09.022 · Cited by 140 · see citing works

New public QSAR model for carcinogenicity

Fjodorova, N., Vračko, M., Novič, M., Roncaglioni, A., & Benfenati, E. (2010) · Chemistry Central Journal · doi:10.1186/1752-153x-4-s1-s3 · Cited by 137 · see citing works

Future pesticide risk assessment: narrowing the gap between intention and reality

Schäfer, R. B., Liess, M., Altenburger, R., Filser, J., Hollert, H., Roß‐Nickoll, M., Schäffer, A., & Scheringer, M. (2019) · Environmental Sciences Europe · doi:10.1186/s12302-019-0203-3 · Cited by 134 · see citing works

ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches

Wang, S., Sun, H., Liu, H., Li, D., Li, Y., & Hou, T. (2016) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.6b00471 · Cited by 133 · see citing works

Artificial neural network (ANN) approach for modeling Zn(II) adsorption from leachate using a new biosorbent

Turan, N. G., Mesci, B., & Özgönenel, O. (2011) · Chemical Engineering Journal · doi:10.1016/j.cej.2011.07.042 · Cited by 130 · see citing works

Prediction of Human Cytochrome P450 Inhibition Using a Multitask Deep Autoencoder Neural Network

Li, X., Xu, Y., Lai, L., & Pei, J. (2018) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.8b00110 · Cited by 130 · see citing works

Predicting Drug-Induced Liver Injury with Bayesian Machine Learning

Williams, D. P., Lazic, S. E., Foster, A. J., Semenova, E., & Morgan, P. (2020) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.9b00264 · Cited by 124 · see citing works

Probing the toxicity of nanoparticles: a unified in silico machine learning model based on perturbation theory

Concu, R., Kleandrova, V. V., Speck‐Planche, A., & Cordeiro, M. N. D. S. (2017) · Nanotoxicology · doi:10.1080/17435390.2017.1379567 · Cited by 123 · see citing works

Computational ecotoxicology: Simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions

Kleandrova, V. V., Luan, F., González‐Díaz, H., Ruso, J. M., Melo, A., Speck‐Planche, A., & Cordeiro, M. N. D. S. (2014) · Environment International · doi:10.1016/j.envint.2014.08.009 · Cited by 122 · see citing works

Combining spatial autocorrelation with machine learning increases prediction accuracy of soil heavy metals

Sergeev, A., Buevich, A., Михайловна, Б. Е., & Shichkin, A. V. (2018) · CATENA · doi:10.1016/j.catena.2018.11.037 · Cited by 121 · see citing works

Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere

Ma, D., & Zhang, Z. (2016) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2016.03.022 · Cited by 119 · see citing works

Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets

Clark, A. M., Dole, K., Coulon-Spektor, A., McNutt, A., Grass, G., Freundlich, J. S., Reynolds, R. C., & Ekins, S. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00143 · Cited by 114 · see citing works

Drug drug interaction extraction from the literature using a recursive neural network

Lim, S., Lee, K., & Kang, J. (2018) · PLoS ONE · doi:10.1371/journal.pone.0190926 · Cited by 114 · see citing works

Quantitative TLC-SERS detection of histamine in seafood with support vector machine analysis

Tan, A., Zhao, Y., Sivashanmugan, K., Squire, K. J., & Wang, A. X. (2019) · Food Control · doi:10.1016/j.foodcont.2019.03.032 · Cited by 113 · see citing works

Drug-Induced Liver Injury: Biomarkers, Requirements, Candidates, and Validation

Meunier, L., & Larrey, D. G. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.01482 · Cited by 106 · see citing works

Landscape predictors of pathogen prevalence and range contractions in US bumblebees

McArt, S. H., Urbanowicz, C. M., McCoshum, S. M., Irwin, R. E., & Adler, L. S. (2017) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2017.2181 · Cited by 104 · see citing works

Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI)

Minerali, E., Foil, D. H., Zorn, K. M., Lane, T. R., & Ekins, S. (2020) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.0c00326 · Cited by 102 · see citing works

Translating Clinical Findings into Knowledge in Drug Safety Evaluation - Drug Induced Liver Injury Prediction System (DILIps)

Liu, Z., Shi, Q., Ding, D., Kelly, R. J., Fang, H., & Tong, W. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002310 · Cited by 96 · see citing works

Pixel-level aflatoxin detecting based on deep learning and hyperspectral imaging

Han, Z., & Gao, J. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.104888 · Cited by 96 · see citing works

Computational Exposure Science: An Emerging Discipline to Support 21st-Century Risk Assessment

Egeghy, P. P., Sheldon, L. S., Isaacs, K., Özkaynak, H., Goldsmith, M., Wambaugh, J. F., Judson, R., & Buckley, T. J. (2015) · Environmental Health Perspectives · doi:10.1289/ehp.1509748 · Cited by 94 · see citing works

Enabling the Discovery and Virtual Screening of Potent and Safe Antimicrobial Peptides. Simultaneous Prediction of Antibacterial Activity and Cytotoxicity

Kleandrova, V. V., Ruso, J. M., Speck‐Planche, A., & Cordeiro, M. N. D. S. (2016) · ACS Combinatorial Science · doi:10.1021/acscombsci.6b00063 · Cited by 92 · see citing works

A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea

Yang, G., Lee�, H., & Lee, G. (2020) · Atmosphere · doi:10.3390/atmos11040348 · Cited by 92 · see citing works

Prediction of bioconcentration factors in fish and invertebrates using machine learning

Miller, T. H., Gallidabino, M. D., MacRae, J. I., Owen, S. F., Bury, N. R., & Barron, L. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.08.122 · Cited by 89 · see citing works

Machine learning models for predicting PAHs bioavailability in compost amended soils

Wu, G., Kechavarzi, C., Li, X., Wu, S., Pollard, S., Sui, H., & Coulon, F. (2013) · Chemical Engineering Journal · doi:10.1016/j.cej.2013.02.122 · Cited by 88 · see citing works

Predicting drug-induced liver injury: The importance of data curation

Kotsampasakou, E., Montanari, F., & Ecker, G. F. (2017) · Toxicology · doi:10.1016/j.tox.2017.06.003 · Cited by 88 · see citing works

Tox_(R)CNN: Deep learning-based nuclei profiling tool for drug toxicity screening

Jiménez‐Carretero, D., Abrishami, V., Fernández-de-Manúel, L., Palacios, I., Quílez-Álvarez, A., Díez-Sánchez, A., del Pozo, M. Á., & Montoya, M. C. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006238 · Cited by 84 · see citing works

Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches

Singh, K. P., Gupta, S. P., & Rai, P. (2013) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2013.05.017 · Cited by 79 · see citing works

Open Source Bayesian Models. 2. Mining a “Big Dataset” To Create and Validate Models with ChEMBL

Clark, A. M., & Ekins, S. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00144 · Cited by 77 · see citing works

Application of backpropagation artificial neural network prediction model for the PAH bioremediation of polluted soil

Olawoyin, R. (2016) · Chemosphere · doi:10.1016/j.chemosphere.2016.07.003 · Cited by 75 · see citing works

Novel naïve Bayes classification models for predicting the carcinogenicity of chemicals

Zhang, H., Cao, Z.-X., Li, M., Li, Y., & Peng, C. Y. (2016) · Food and Chemical Toxicology · doi:10.1016/j.fct.2016.09.005 · Cited by 71 · see citing works

Similarity-based machine learning support vector machine predictor of drug-drug interactions with improved accuracies

Song, D., Chen, Y., Min, Q., Sun, Q., Ye, K., Zhou, C., Yuan, S., Sun, Z., & Liao, J. (2018) · Journal of Clinical Pharmacy and Therapeutics · doi:10.1111/jcpt.12786 · Cited by 71 · see citing works

Optical detection of aflatoxins B in grained almonds using fluorescence spectroscopy and machine learning algorithms

Bertani, F. R., Businaro, L., Gambacorta, L., Mencattini, A., Brenda, D., Giuseppe, D. D., Ninno, A. D., Solfrizzo, M., Martinelli, E., & Gerardino, A. (2019) · Food Control · doi:10.1016/j.foodcont.2019.107073 · Cited by 71 · see citing works

Projecting Fine Particulate Matter-Related Mortality in East China

Madaniyazi, L., Nagashima, T., Guo, Y., Yu, W., & Tong, S. (2015) · Environmental Science & Technology · doi:10.1021/acs.est.5b01478 · Cited by 70 · see citing works

Atmospheric dispersion modeling using Artificial Neural Network based cellular automata

Lauret, P., Heymes, F., Aprin, L., & Johannet, A. (2016) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2016.08.001 · Cited by 68 · see citing works

Speeding up Early Drug Discovery in Antiviral Research: A Fragment-Based in Silico Approach for the Design of Virtual Anti-Hepatitis C Leads

Speck‐Planche, A., & Cordeiro, M. N. D. S. (2017) · ACS Combinatorial Science · doi:10.1021/acscombsci.7b00039 · Cited by 68 · see citing works

Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II

Ogura, K., Sato, T., Yuki, H., & Honma, T. (2019) · Scientific Reports · doi:10.1038/s41598-019-47536-3 · Cited by 66 · see citing works

Revealing cytotoxic substructures in molecules using deep learning

Webel, H. E., Kimber, T. B., Radetzki, S., Neuenschwander, M., Nazaré, M., & Volkamer, A. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00310-4 · Cited by 64 · see citing works

History of Chronic Toxicity and Animal Carcinogenicity Studies for Pharmaceuticals

Jacobs, A. C., & Hatfield, K. P. (2012) · Veterinary Pathology · doi:10.1177/0300985812450727 · Cited by 62 · see citing works

Transparent Data Mining for Big and Small Data

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Prediction of Heavy Metal Removal by Different Liner Materials from Landfill Leachate: Modeling of Experimental Results Using Artificial Intelligence Technique

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Validity of Tier 1 Modelling Tools and Impacts on Exposure Assessments within REACH Registrations—ETEAM Project, Validation Studies and Consequences

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Examining the in vivo pulmonary toxicity of engineered metal oxide nanomaterials using a genetic algorithm-based dose-response-recovery clustering model

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Exploration of the spatial-Composite Risk Index (CRI) for the characterization of toxicokinetics in petrochemical active areas

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Nanoparticle Characteristic Interaction Effects on Pulmonary Toxicity: A Random Forest Modeling Framework to Compare Risks of Nanomaterial Variants

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ALTERNATIVAS A LA EXPERIMENTACIÓN ANIMAL EN TOXICOLOGÍA: SITUACIÓN ACTUAL

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X-ray absorption spectrum combined with deep neural network for on-line detection of beverage preservatives

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OpenRiskNet, an open e-infrastructure to support data sharing, knowledge integration and in silico analysis and modelling in risk assessment

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A prediction model of occupational manganese exposure based on artificial neural network

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Developing the Function of ‘Magnitude-of-Effect’ (MoE) for Artificial Neural Networks to Demonstrate the Causal Effect of Exposure Variables on Outcome Variable

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Swiss 3R Competence Centre: Promoting the 3Rs implementation

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The role of AI in drug discovery: challenges, opportunities, and strategies

Blanco-González, A., Cabezón, A., Seco-González, A., Conde-Torres, D., Antelo-Riveiro, P., Piñeiro, Á., & Garcia-Fandino, R. (2023) · Pharmaceuticals · doi:10.3390/ph16060891 · Cited by 690 · see citing works

Pesticides: An alarming detrimental to health and environment

Kaur, R., Choudhary, D., Bali, S., Bandral, S. S., Singh, V., Ahmad, M. A., Rani, N., Singh, T. G., & Chandrasekaran, B. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.170113 · Cited by 398 · see citing works

ToxinPred2: an improved method for predicting toxicity of proteins

Sharma, N., Naorem, L. D., Jain, S., & Raghava, G. P. S. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac174 · Cited by 380 · see citing works

Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management

Popescu, S. M., Mansoor, S., Wani, O. A., Kumar, S. S., Sharma, V. K., Sharma, A., Arya, V. M., Kirkham, M. B., Hou, D., Bolan, N. S., et al. (2024) · Frontiers in Environmental Science · doi:10.3389/fenvs.2024.1336088 · Cited by 345 · see citing works

Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives

Tran, T. T. V., Wibowo, A. S., Tayara, H., & Chong, K. T. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00200 · Cited by 241 · see citing works

Alternative Plasticizers As Emerging Global Environmental and Health Threat: Another Regrettable Substitution?

Qadeer, A., Kirsten, K. L., Ajmal, Z., Jiang, X., & Zhao, X. (2022) · Environmental Science & Technology · doi:10.1021/acs.est.1c08365 · Cited by 224 · see citing works

Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools

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Advances in artificial intelligence for drug delivery and development: A comprehensive review

Gholap, A. D., Uddin, M. J., Faiyazuddin, M., Omri, A., Gowri, S., & Khalid, M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108702 · Cited by 195 · see citing works

Tribulations and future opportunities for artificial intelligence in precision medicine

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Artificial intelligence and machine learning disciplines with the potential to improve the nanotoxicology and nanomedicine fields: a comprehensive review

Singh, A. V., Varma, M., Laux, P., Choudhary, S., Datusalia, A. K., Gupta, N., Luch, A., Gandhi, A., Kulkarni, P., & Nath, B. (2023) · Archives of Toxicology · doi:10.1007/s00204-023-03471-x · Cited by 171 · see citing works

Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges

Tiwari, P. C., Pal, R., Chaudhary, M. J., & Nath, R. (2023) · Drug Development Research · doi:10.1002/ddr.22115 · Cited by 162 · see citing works

Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications

Jeong, J., & Choi, J. (2022) · Environmental Science & Technology · doi:10.1021/acs.est.1c07413 · Cited by 147 · see citing works

Role of artificial intelligence in revolutionizing drug discovery

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Advances in Artificial Intelligence (AI)-assisted approaches in drug screening

Singh, S., Gupta, H., Sharma, P., & Sahi, S. (2023) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2023.100039 · Cited by 144 · see citing works

Molecular mechanism of the anti-inflammatory effects of plant essential oils: A systematic review

Zhao, Q., Zhu, L.-Y., Wang, S., Gao, Y., & Jin, F. (2022) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2022.115829 · Cited by 137 · see citing works

Artificial intelligence (AI)—it’s the end of the tox as we know it (and I feel fine)*

Kleinstreuer, N., & Härtung, T. (2024) · Archives of Toxicology · doi:10.1007/s00204-023-03666-2 · Cited by 139 · see citing works

Advancing drug safety in drug development: bridging computational predictions for enhanced toxicity prediction

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The Role of AI in Drug Discovery

Abbas, M. K. G., Rassam, A., Karamshahi, F., Abunora, R., & Abouseada, M. (2024) · ChemBioChem · doi:10.1002/cbic.202300816 · Cited by 116 · see citing works

Probabilistic risk assessment – the keystone for the future of toxicology

Maertens, A. (2022) · ALTEX · doi:10.14573/altex.2201081 · Cited by 101 · see citing works

Integrating AI and advanced spectroscopic techniques for precision food safety and quality control

Ziani, I., Bouakline, H., Guerraf, A. E., Bachiri, A. E., Fauconnier, M., & Sher, F. (2024) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2024.104850 · Cited by 89 · see citing works

Next generation radiotheranostics promoting precision medicine

Pomykala, K. L., Hadaschik, B. A., Sartor, O. A., Gillessen, S., Sweeney, C. J., Maughan, T., Hofman, M. S., & Herrmann, K. (2023) · Annals of Oncology · doi:10.1016/j.annonc.2023.03.001 · Cited by 82 · see citing works

ToxAIcology - The evolving role of artificial intelligence in advancing toxicology and modernizing regulatory science

Härtung, T. (2023) · ALTEX · doi:10.14573/altex.2309191 · Cited by 88 · see citing works

Compound–protein interaction prediction by deep learning: Databases, descriptors and models

Du, B.-X., Qin, Y., Jiang, Y., Xu, Y., Yiu, S., Yu, H., & Shi, J. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.02.023 · Cited by 73 · see citing works

Artificial intelligence as the new frontier in chemical risk assessment

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Perspective on Quantitative Structure–Toxicity Relationship (QSTR) Models to Predict Hepatic Biotransformation of Xenobiotics

Rai, M., Paudel, N., Sakhrie, M., Gemmati, D., Khan, I. A., Tisato, V., Kanase, A., Schulz, A., & Singh, A. V. (2023) · Livers · doi:10.3390/livers3030032 · Cited by 57 · see citing works

An updated review of functional ingredients of Manuka honey and their value-added innovations

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User-friendly and industry-integrated AI for medicinal chemists and pharmaceuticals

Kapustina, O., Burmakina, P., Gubina, N., Serov, N., & Vinogradov, V. V. (2024) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2024.100072 · Cited by 56 · see citing works

Artificial Intelligence Applications in Smart Healthcare: A Survey

Gao, X., He, P., Zhou, Y. X., & Qin, X. (2024) · Future Internet · doi:10.3390/fi16090308 · Cited by 65 · see citing works

The review of food safety inspection system based on artificial intelligence, image processing, and robotic

Chen, T.-C., & Yu, S. (2021) · Food Science and Technology · doi:10.1590/fst.35421 · Cited by 55 · see citing works

Overview of Tissue Engineering and Drug Delivery Applications of Reactive Electrospinning and Crosslinking Techniques of Polymeric Nanofibers with Highlights on Their Biocompatibility Testing and Regulatory Aspects

Younes, H. M., Kadavil, H., Ismail, H. M., Adib, S. S. A., Zamani, S., Alany, R. G., & Al-Kinani, A. A. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics16010032 · Cited by 55 · see citing works

Systematic approaches to machine learning models for predicting pesticide toxicity

Anandhi, G., & Iyapparaja, M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e28752 · Cited by 44 · see citing works

Ethical principles and scientific advancements: In vitro, in silico, and non-vertebrate animal approaches for a green ecotoxicology

Pastorino, P., Prearo, M., & Barceló, ‪. (2024) · Green Analytical Chemistry · doi:10.1016/j.greeac.2024.100096 · Cited by 42 · see citing works

Overview of Computational Toxicology Methods Applied in Drug and Green Chemical Discovery

Bueso-Bordils, J. I., Antón-Fos, G. M., Martín-Algarra, R. V., & Aleman, P. (2024) · Journal of Xenobiotics · doi:10.3390/jox14040101 · Cited by 43 · see citing works

Chemotaxonomy, an Efficient Tool for Medicinal Plant Identification: Current Trends and Limitations

Amin, A., & Park, S. (2025) · Plants · doi:10.3390/plants14142234 · Cited by 38 · see citing works

The role of trust in the use of artificial intelligence for chemical risk assessment

Wassenaar, P. N. H., Minnema, J., Vriend, J., Peijnenburg, W. J. G. M., Pennings, J. L. A., & Kienhuis, A. S. (2024) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2024.105589 · Cited by 32 · see citing works

Southeast Asia’s environmental challenges: emergence of new contaminants and advancements in testing methods

Bhagat, J., Singh, N., & Shimada, Y. (2024) · Frontiers in Toxicology · doi:10.3389/ftox.2024.1322386 · Cited by 33 · see citing works

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

Agu, P. C., & Obulose, C. N. (2024) · Drug Development Research · doi:10.1002/ddr.22159 · Cited by 33 · see citing works

Machine learning-driven catalyst design, synthesis and performance prediction for CO2 hydrogenation

Asif, M., Yao, C., Zuo, Z., Bilal, M., Zeb, H., Lee, S., Wang, Z., & Kim, T. (2024) · Journal of Industrial and Engineering Chemistry · doi:10.1016/j.jiec.2024.09.035 · Cited by 31 · see citing works

Artificial intelligence and nanotechnology for cervical cancer treatment: Current status and future perspectives

Kour, S., Biswas, I., Sheoran, S., Arora, S., Sheela, P., Duppala, S. K., Murthy, D. K., Pawar, S. C., Singh, H., Kumar, D., et al. (2023) · Journal of Drug Delivery Science and Technology · doi:10.1016/j.jddst.2023.104392 · Cited by 32 · see citing works

Intelligent consensus predictions of bioconcentration factor of pharmaceuticals using 2D and fragment-based descriptors

Khan, K., Kumar, V., Colombo, E., Lombardo, A., Benfenati, E., & Roy, K. (2022) · Environment International · doi:10.1016/j.envint.2022.107625 · Cited by 27 · see citing works

Advances of computational methods enhance the development of multi-epitope vaccines

Wei, Y., Qiu, T., Ai, Y., Zhang, Y., Xie, J., Zhang, D., Luo, X., Sun, X., Wang, X., & Qiu, J. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf055 · Cited by 29 · see citing works

Multitask Deep Neural Networks for Ames Mutagenicity Prediction

Martínez, M. J., Sabando, M. V., Soto, A. J., Roca, C., Requena-Triguero, C., Campillo, N. E., Paez, J. A., & Ponzoni, I. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00532 · Cited by 28 · see citing works

Digital Technology Applications in the Management of Adverse Drug Reactions: Bibliometric Analysis

Litvinova, O., Yeung, A. W. K., Hammerle, F. P., Mickael, M., Matin, M., Kletečka-Pulker, M., Atanasov, A. G., & Willschke, H. (2024) · Pharmaceuticals · doi:10.3390/ph17030395 · Cited by 26 · see citing works

Bridging knowledge gaps in human chemical exposure via drinking water with non-target screening

Ciccarelli, D., Samanipour, S., Rapp-Wright, H., Bieber, S., Letzel, T., O'Brien, J. W., Marczylo, T. H., Gant, T. W., Vineis, P., & Barron, L. (2024) · Critical Reviews in Environmental Science and Technology · doi:10.1080/10643389.2024.2396690 · Cited by 23 · see citing works

Creating 3D Objects with Integrated Electronics via Multiphoton Fabrication In Vitro and In Vivo

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A Horizon Scan to Support Chemical Pollution–Related Policymaking for Sustainable and Climate-Resilient Economies

Green, C., Bilyanska, A., Bradley, M., Dinsdale, J., Hutt, L., Backhaus, T., Boons, F., Bott, D., Collins, C. D., Cornell, S., et al. (2023) · Environmental Toxicology and Chemistry · doi:10.1002/etc.5620 · Cited by 25 · see citing works

Machine Learning for Bridging the Gap between Density Functional Theory and Coupled Cluster Energies

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Deep Learning in Environmental Toxicology: Current Progress and Open Challenges

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TabNet and TabTransformer: Novel Deep Learning Models for Chemical Toxicity Prediction in Comparison With Machine Learning

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Assessing chemical hazard and unraveling binding affinity of priority pollutants to lignin modifying enzymes for environmental remediation

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A Review of the Applications, Benefits, and Challenges of Generative AI for Sustainable Toxicology

Alam, F., Alnazzawi, T. S. M., Mehmood, R., & Almaghthawi, A. (2025) · Current Research in Toxicology · doi:10.1016/j.crtox.2025.100232 · Cited by 23 · see citing works

ToxMVA: An end-to-end multi-view deep autoencoder method for protein toxicity prediction

Shi, H. W., Li, Y., Chen, Y., Qin, Y., Tang, Y., Zhou, X. C., Zhang, Y., & Wu, Y. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106322 · Cited by 19 · see citing works

Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study

Bhowmik, R., Kant, R., Manaithiya, A., Saluja, D., Vyas, B., Nath, R., Qureshi, K. A., Parkkila, S., & Aspatwar, A. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1265573 · Cited by 19 · see citing works

Assessment of sedative activity of Chrysin: Behavioral approach with pharmacokinetics, toxicological profile and molecular docking

Hasan, M. S. A., Bhuia, M. S., Sheikh, S., Bithi, S. A., Saim, M. A., Kamli, H. M., Ansari, S. A., Ahammed, N. T., & Islam, M. T. (2024) · Sleep Medicine · doi:10.1016/j.sleep.2024.12.007 · Cited by 18 · see citing works

Application of Artificial Intelligence and Machine Learning in Drug Discovery and Development

Parvathaneni, M., Awol, A. K., Kumari, M., Lan, K., & Lingam, M. (2023) · Journal of Drug Delivery and Therapeutics · doi:10.22270/jddt.v13i1.5867 · Cited by 18 · see citing works

Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors

Wilm, A., de Lomana, M. G., Stork, C., Mathai, N., Hirte, S., Norinder, U., Kühnl, J., & Kirchmair, J. (2021) · Pharmaceuticals · doi:10.3390/ph14080790 · Cited by 18 · see citing works

Artificial Intelligence: Future Aspects in the Pharmaceutical Industry an Overview

Bairagi, A., Singhai, A., & Jain, A. (2024) · Asian Journal of Pharmacy and Technology · doi:10.52711/2231-5713.2024.00039 · Cited by 5 · see citing works

In vitro Assessment of Anti-Microbial Activity of Aloe vera (Barbadensis miller) Supported through Computational Studies

Pandit, A., Shukla, A. K., DEEPIKA, D., Vaidya, D., Kumari, A., & Kumar, A. (2023) · Russian Journal of Bioorganic Chemistry · doi:10.1134/s1068162023020188 · Cited by 17 · see citing works

Applicability domains of neural networks for toxicity prediction

Pérez‐Santín, E., de‐la‐Fuente‐Valentín, L., García, M. G., Bravo, K. A. S., López, F., & Sánchez, J. I. L. (2023) · AIMS Mathematics · doi:10.3934/math.20231426 · Cited by 15 · see citing works

Artificial Intelligence in Bioremediation and Environmental Management

Singh, K., Sarkar, S., & Sharma, S. (2025) · Environmental Quality Management · doi:10.1002/tqem.70192 · Cited by 14 · see citing works

Design and Diversity Analysis of Chemical Libraries in Drug Discovery

Olmedo, D. A., Durant-Archibold, A. A., López‐Pérez, J. L., & Medina‐Franco, J. L. (2023) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/1386207326666230705150110 · Cited by 14 · see citing works

Multimodal deep learning for chemical toxicity prediction and management

Hong, J., & Kwon, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-95720-5 · Cited by 13 · see citing works

Current Status and Future Perspectives of Nuclear Medicine in Prostate Cancer from Imaging to Therapy: A Comprehensive Review

Lee, J.-H., & Kim, T. J. (2025) · Biomedicines · doi:10.3390/biomedicines13051132 · Cited by 13 · see citing works

Recent Advancements in Novel Bioactive Peptides and Protein Hydrolysates Isolated from Different Medicinal Plants Along with Their Applications in Food and Pharmaceutical Industries

Sharma, V., Gupta, P., & Sharma, P. (2023) · International Journal of Peptide Research and Therapeutics · doi:10.1007/s10989-023-10565-8 · Cited by 13 · see citing works

Functional integration of ACE inhibitory peptides into food matrices: Stability, bioavailability, consumer acceptance, regulatory and commercialization barriers

Bhoria, S., & Dhewa, T. (2025) · Journal of Food Composition and Analysis · doi:10.1016/j.jfca.2025.108029 · Cited by 12 · see citing works

Bioactive peptides as multipotent molecules bespoke and designed for Alzheimer's disease

Mesias, A., Borges, S., Pintado, M., & Baptista-Silva, S. (2025) · Neuropeptides · doi:10.1016/j.npep.2025.102515 · Cited by 11 · see citing works

Multi-Strategy Assessment of Different Uses of QSAR under REACH Analysis of Alternatives to Advance Information Transparency

Chinen, K., & Malloy, T. F. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19074338 · Cited by 13 · see citing works

Traditional Machine and Deep Learning for Predicting Toxicity Endpoints

Norinder, U. (2022) · Molecules · doi:10.3390/molecules28010217 · Cited by 11 · see citing works

Assessments of protodioscin’s antinociceptive and antidiarrheal properties: in vivo and in silico investigations on macromolecule binding affinity and modulatory effects

Ghosh, P. R., Hasan, M. S. A., Rouf, R., Chowdhury, R., Yadav, B., Mia, E., Islam, M. T., Hasan, M. R., Ansari, S. A., Ansari, I. A., et al. (2025) · Naunyn-Schmiedeberg s Archives of Pharmacology · doi:10.1007/s00210-025-03860-2 · Cited by 10 · see citing works

The Role of Artificial Intelligence in Drug Discovery and Pharmaceutical Development: A Paradigm Shift in the History of Pharmaceutical Industries

Vidiyala, N., Sunkishala, P., Parupathi, P. R., & Nyavanandi, D. (2025) · AAPS PharmSciTech · doi:10.1208/s12249-025-03134-3 · Cited by 9 · see citing works

AI in Clinical Trials and Drug Development: Challenges and Potential Advancements

Gupta, D., Wal, P., Wal, A., R, S., Kumar, M., Panda, K. C., & Sharma, M. C. (2024) · Current Drug Discovery Technologies · doi:10.2174/0115701638314252241016165345 · Cited by 8 · see citing works

Anticancer activity of Jasminum sambac and its bioactive phytochemicals against the PI3K-AKT-mTOR pathway: A literature-based in silico study

Hasan, M. S. A., Bhuia, M. S., Chowdhury, R., Shadin, M., Mia, E., Yana, N. T., Rakib, I. H., Ansari, S. A., Ansari, I. A., Coutinho, H. D. M., et al. (2025) · South African Journal of Botany · doi:10.1016/j.sajb.2025.03.004 · Cited by 8 · see citing works

Definition and strategy of intelligent foodomics for diagnosis and identification of hazards and pathogens in food-borne diseases

Shangguan, D., Wang, Y., Huang, Q., & Wang, Y. (2025) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2025.104994 · Cited by 8 · see citing works

Can Artificial Intelligence Enhance the Antimicrobial Effectiveness of Photodynamic Therapy? What We Know and What We Do Not Know?

Pourhajibagher, M., Bahrami, R., & Bahador, A. (2025) · Journal of lasers in medical sciences · doi:10.34172/jlms.2025.33 · Cited by 8 · see citing works

Application of supervised learning classification modeling for predicting benthic sediment toxicity in the southern California bight: A test of concept

Saleh, O., Otim, F. N., & Otim, O. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.165946 · Cited by 7 · see citing works

Beyond QSARs: Quantitative Knowledge–Activity Relationships (QKARs) for enhanced drug toxicity prediction

Li, T., Qu, Y., Chen, A., Thakkar, S., Li, D., & Tong, W. (2025) · Toxicological Sciences · doi:10.1093/toxsci/kfaf135 · Cited by 7 · see citing works

Leveraging In Silico Structure–Activity Models to Predict Acute Honey Bee (Apis mellifera) Toxicity for Agrochemicals

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Artificial Intelligence in Clinical Toxicology

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Build-a-Bio-Strip: An Online Platform for Rapid Toxicity Assessment in Chemical Synthesis

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Methodology to classify hazardous compounds via deep learning based on convolutional neural networks

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Active Learning-Optimized Dynamic Sampling Strategy for Enhanced Toxicity Prediction of Chemical Compounds

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Research trends of computational toxicology: a bibliometric analysis

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Role of Artificial Intelligence in the Toxicity Prediction of Drugs

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Application of artificial intelligence in pharmaceutical development

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Comparison of classical Machine Learning-based algorithms to predict Triplex Forming Oligonucleotides

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Designing transparent and accountable AI systems for healthcare

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Future Trends and Challenges in Pharmaceutical Microbiology with the Integration of Artificial Intelligence

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AI/ML-based computational models for toxicity prediction

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Use of artificial intelligence in animal experimentation: A review

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AI for In Silico Toxicity Prediction

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