eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates
Open accessAbstract
BACKGROUND: The efficiency of drug development defined as a number of successfully launched new pharmaceuticals normalized by financial investments has significantly declined. Nonetheless, recent advances in high-throughput experimental techniques and computational modeling promise reductions in the costs and development times required to bring new drugs to market. The prediction of toxicity of drug candidates is one of the important components of modern drug discovery. RESULTS: In this work, we describe eToxPred, a new approach to reliably estimate the toxicity and synthetic accessibility of small organic compounds. eToxPred employs machine learning algorithms trained on molecular fingerprints to evaluate drug candidates. The performance is assessed against multiple datasets containing known drugs, potentially hazardous chemicals, natural products, and synthetic bioactive compounds. Encouragingly, eToxPred predicts the synthetic accessibility with the mean square error of only 4% and the toxicity with the accuracy of as high as 72%. CONCLUSIONS: eToxPred can be incorporated into protocols to construct custom libraries for virtual screening in order to filter out those drug candidates that are potentially toxic or would be difficult to synthesize. It is freely available as a stand-alone software at https://github.com/pulimeng/etoxpred .
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AI-based methods have been able to predict the toxicity of drug candidates.
“AI-based methods have also been able to predict the toxicity of drug candidates [6].”
From The role of AI in drug discovery: challenges, opportunities, and strategies (2023) · cites this paper as [6]
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Chaudhari, R. R., Tan, Z., Huang, B., & Zhang, S. (2017) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2017.1280024 · Cited by 120 · cited 1× · see citing works
“Numerous machine learning-based techniques have been developed to reveal complex relations between chemical entities and their biological targets [35].”
BackgroundAssessing the Performance of 3D Pharmacophore Models in Virtual Screening: How Good are They?
Braga, R. C., & Andrade, C. H. (2013) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026611313090010 · Cited by 114 · cited 1× · see citing works
“Although the combination of pharmacophore modeling, QSAR, and VS techniques has been demonstrated to be valuable in the absence of the protein structure data [12, 13], the three-dimensional (3D) information on the target protein allows employing structure-based drug design (SBDD) [14] in CADD.”
MethodComputational Approaches for Drug Discovery
Hung, C., & Chen, C. (2014) · Drug Development Research · doi:10.1002/ddr.21222 · Cited by 105 · cited 1× · see citing works
“Computer-aided drug discovery (CADD) holds a significant promise to reduce the costs and speed up the development of lead candidates at the outset of drug discovery [3].”
BackgroundChevillard, F., & Kolb, P. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00203 · Cited by 98 · cited 1× · see citing works
“Although the capabilities of the traditional LBDD to discover new classes of leads may be limited, recent advances in generating targeted virtual chemical libraries by combinatorial chemistry methods considerably extend the application of LBDD methods [8 – 10].”
BackgroundFlexX‐Scan: Fast, structure‐based virtual screening
Schellhammer, I., & Rarey, M. (2004) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20217 · Cited by 96 · cited 1× · see citing works
“VS performed with high-performance computing machines renders docking programs such as AutoDock Vina [19], rDock [20], Glide [21], and FlexX [22] capable to search through millions of compounds in a matter of days or even hours.”
MethodSuperToxic: a comprehensive database of toxic compounds
Schmidt, U., Struck, S., Grüning, B. A., Hossbach, J., Jaeger, I. S., Parol, R., Lindequist, U., Teuscher, E., & Preißner, R. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn850 · Cited by 91 · cited 1× · see citing works
“Among 12,612 molecules with LD 50 data provided by the SuperToxic database [60], 7392 compounds are labeled as toxic with a LD 50 of 500 mg kg − 1 .”
MethodPharmacophore-based virtual screening: a review of recent applications
Kim, K., Kim, N. D., & Seong, B. L. (2010) · Expert Opinion on Drug Discovery · doi:10.1517/17460441003592072 · Cited by 90 · cited 1× · see citing works
“Although the combination of pharmacophore modeling, QSAR, and VS techniques has been demonstrated to be valuable in the absence of the protein structure data [12, 13], the three-dimensional (3D) information on the target protein allows employing structure-based drug design (SBDD) [14] in CADD.”
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Liu, T., Naderi, M., Alvin, C., Mukhopadhyay, S., & Bryliński, M. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00596 · Cited by 87 · cited 2× · see citing works
“…application, this technique can be incorporated into high-throughput pipelines constructing custom libraries for virtual screening, such as that based on e MolFrag [9] and e Synth [10], to eliminate from CADD those drug candidates that are potentially toxic or would be difficult to synthesize.”
MethodSelf-citationInterpreting linear support vector machine models with heat map molecule coloring
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“DeepTox was reported to outperform deep neural networks (DNNs) [31], support vector machines (SVMs) [32], random forests (RF) [33], and elastic nets [34].”
BackgroundMechanism of Paroxetine (Paxil) Inhibition of the Serotonin Transporter
Davis, B. A., Nagarajan, A., Forrest, L. R., & Singh, S. K. (2016) · Scientific Reports · doi:10.1038/srep23789 · Cited by 56 · cited 1× · see citing works
“Further, fluorination helps stabilize the binding of a drug to a protein pocket by creating additional favorable interactions, as it was suggested for the fluorophe-nyl ring of paroxetine (DrugBank-ID: DB00715) [83], a selective serotonin reuptake inhibitor shown in Fig.”
BackgroundHyohdoh, I., Furuichi, N., Aoki, T., Itezono, Y., Shirai, H., Ozawa, S., Watanabe, F., Matsushita, M., Sakaitani, M., Ho, P., et al. (2013) · ACS Medicinal Chemistry Letters · doi:10.1021/ml4002419 · Cited by 40 · cited 1× · see citing works
“1) fluorine substitution [80].”
A graph-based approach to construct target-focused libraries for virtual screening
Naderi, M., Alvin, C., Ding, Y., Mukhopadhyay, S., & Bryliński, M. (2016) · Journal of Cheminformatics · doi:10.1186/s13321-016-0126-6 · Cited by 40 · cited 2× · see citing works
“…application, this technique can be incorporated into high-throughput pipelines constructing custom libraries for virtual screening, such as that based on e MolFrag [9] and e Synth [10], to eliminate from CADD those drug candidates that are potentially toxic or would be difficult to synthesize.”
BackgroundMethodSelf-citationTypical toxic components in traditional Chinese medicine
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3-O-Methyldopa, L-Dopa, and Trihexyphenidyl in the Treatment of Parkinson’s Disease
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“9A, trihexyphenidyl (DrugBank-ID: DB00376), a muscarinic antagonist to treat Parkinson ’ s disease [73], donepezil (Drug-Bank-ID: DB00843), a reversible acetyl cholinesterase inhibitor to treat Alzheimer ’ s disease [74], an opioid analgesic drug remifentanil (DrugBank-ID: DB00899) [75], and…”
BackgroundPrasugrel: A Critical Comparison with Clopidogrel
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“Finally, replacing the chlorine atom with a fluorine improves safety profile and pharmacokinetic properties of prasugrel (DrugBank-ID: DB06209) compared to other thienopyridine antiplatelet drugs, ticlopidine and clopidogrel [87].”
BackgroundFast Molecular Docking Methods
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Targeted Fluorination of a Nonsteroidal Anti‐inflammatory Drug to Prolong Metabolic Half‐Life
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“Indeed, a targeted fluorination of a nonsteroidal anti-inflammatory drug flurbiprofen (DrugBank-ID: DB00712) helped prolong its metabolic half-life [85].”
BackgroundThe Interactions Between hERG Potassium Channel and Blockers
Du, L., Li, M., & You, Q. (2009) · Current Topics in Medicinal Chemistry · doi:10.2174/156802609788317829 · Cited by 20 · cited 1× · see citing works
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MethodClasses of Piperidine-Based Drugs
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Artificial intelligence in drug discovery and development
Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.10.010 · Cited by 1,695 · see citing works
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473 · see citing works
“…prediction software and web-based tools such as Tox21 (https:// ntp. niehs. nih. gov/ whatw estudy/ tox21/ index. html) [327], SEA (http:// sea. bkslab. org/) [328], eToxPred (https:// www. bryli nski. org/ etoxp red-0) [329], and TargeTox (https:// github. com/ artem-lysen ko/ Targe Tox) [330].”
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
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 machine learning in drug discovery and development
Patel, V. J., & Shah, M. (2021) · Intelligent Medicine · doi:10.1016/j.imed.2021.10.001 · Cited by 234 · see citing works
Machine Learning Toxicity Prediction: Latest Advances by Toxicity End Point
Cavasotto, C. N., & Scardino, V. (2022) · ACS Omega · doi:10.1021/acsomega.2c05693 · Cited by 226 · see citing works
Machine learning for synergistic network pharmacology: a comprehensive overview
Noor, F., Asif, M., Ashfaq, U. A., Qasim, M., & Qamar, M. T. U. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad120 · Cited by 219 · see citing works
Machine Learning and Artificial Intelligence in Toxicological Sciences
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An Overview of Machine Learning and Big Data for Drug Toxicity Evaluation
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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
“By animal models and clinical trials (Basile et al., 2019; Pérez Santín et al., 2021; Pu et al., 2019).”
BackgroundEvolving scenario of big data and Artificial Intelligence (AI) in drug discovery
Tripathi, M. K., Nath, A., Singh, T. P., Ethayathulla, A. S., & Kaur, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10256-w · Cited by 146 · see citing works
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
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
“…of AI techniques (e.g., Luechtefeld and Hartung 2017; Idakwo et al. 2018; Tang et al. 2018; Baskin 2018; Luechtefeld et al. 2018a, Bhhatarai et al. 2019; Pu et al. 2019; Mansouri et al. 2021; Lin and Chou 2022; Jeong et al. 2022; Sedykh et al. 2022; Tuyet et al. 2023, Hartung 2023b, 2023c).”
MethodAI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges
Schauperl, M., & Denny, R. A. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00026 · Cited by 122 · see citing works
Wang, M. W. H., Goodman, J. M., & Allen, T. E. H. (2020) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.0c00316 · Cited by 125 · see citing works
Revolutionizing Drug Discovery: A Comprehensive Review of AI Applications
Dhudum, R., Ganeshpurkar, A., & Pawar, A. (2024) · Drugs and Drug Candidates · doi:10.3390/ddc3010009 · Cited by 127 · see citing works
“However, these techniques can be expensive and cumbersome and may often produce inaccurate results [28].”
BackgroundReimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine
Iqbal, J., Jaimes, D. C. C., Makineni, P., Subramani, S., Hemaida, S., Thugu, T. R., Butt, A. N., Sikto, J. T., Kaur, P., Lak, M. A., et al. (2023) · Cureus · doi:10.7759/cureus.44658 · Cited by 109 · see citing works
“How AI may generate and synthesize novel molecules has been studied, opening the door to using AI to create new medicines [49].”
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“1 billion dollars is for the drug development phases prior to human testing [7].”
Backgroundda Costa, K. S., do Nascimento, L. D., Lima, A. H., Damasceno, V., Nahum, C., Braga, R. C., & Lameira, J. (2021) · Frontiers in Chemistry · doi:10.3389/fchem.2021.662688 · Cited by 88 · see citing works
Winkler, D. A., Hughes, A. E., Özkan, C., Mol, J. M. C., Würger, T., Feiler, C., Zhang, D., & Lamaka, S. V. (2024) · Progress in Materials Science · doi:10.1016/j.pmatsci.2024.101392 · Cited by 70 · see citing works
Bess, A., Berglind, F., Mukhopadhyay, S., Bryliński, M., Griggs, N. W., Cho, T., Galliano, C., & Wasan, K. M. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.10.022 · Cited by 63 · see citing works
“54 The resulting weights with their uncertainty estimates can be used to prioritize drugs and fi lter the top drug candidates by taking their respective toxicities and synthetic accessibilities 24 into consideration using a multicriterion optimization algorithm.”
Self-citationApplications and prospects of cryo-EM in drug discovery
Zhu, K., Yuan, C., Du, Y., Sun, K., Zhang, X., Vogel, H., Jia, X., Gao, Y., Zhang, Q., Wang, D., et al. (2023) · Military Medical Research · doi:10.1186/s40779-023-00446-y · Cited by 60 · see citing works
Artificial intelligence in the pharmaceutical sector: current scene and future prospect
Kalyane, D., Sanap, G., Paul, D., Shenoy, S., Anup, N., Polaka, S., Tambe, V., & Tekade, R. K. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-814455-8.00003-7 · Cited by 61 · see citing works
Multimodal molecular imaging in drug discovery and development
Vermeulen, I., Isin, E. M., Barton, P., Cillero‐Pastor, B., & Heeren, R. M. A. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.04.009 · Cited by 56 · see citing works
Artificial Intelligence in Drug Formulation and Development: Applications and Future Prospects
Noorain, Srivastava, V., Parveen, B., & Parveen, R. (2023) · Current Drug Metabolism · doi:10.2174/0113892002265786230921062205 · Cited by 55 · see citing works
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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
Ambreen, S., Umar, M., Noor, A., Jain, H., & Ali, R. (2024) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2024.117164 · Cited by 50 · see citing works
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Green, A. J., Mohlenkamp, M. J., Das, J., Chaudhari, M., Truong, L., Tanguay, R. L., & Reif, D. M. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009135 · Cited by 48 · see citing works
Artificial intelligence as a tool in drug discovery and development
Kokudeva, M., Vichev, M., Naseva, E., Miteva, D., & Velikova, T. V. (2024) · World Journal of Experimental Medicine · doi:10.5493/wjem.v14.i3.96042 · Cited by 58 · see citing works
Advancing health care via artificial intelligence: From concept to clinic
Sandeep Ganesh, G., Kolusu, A. S., Prasad, K., Samudrala, P. K., & Nemmani, K. V. S. (2022) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2022.175320 · Cited by 43 · see citing works
Updates on Drug Designing Approach Through Computational Strategies: a Review
Azad, I., Khan, T., Ahmad, N. S., Khan, A. R., & Akhter, Y. (2023) · Future Science OA · doi:10.2144/fsoa-2022-0085 · Cited by 43 · see citing works
Keeping pace with the explosive growth of chemical libraries with structure‐based virtual screening
Kuan, J., Radaeva, M., Avenido, A., Cherkasov, A. R., & Gentile, F. (2023) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1678 · Cited by 43 · see citing works
“ML is rapidly becoming one of the central tools for academic and industrial drug discovery, with applications in de novo design, 62 toxicity prediction, 63 and off-target interaction identification 64 among many others.”
Arab, I., Egghe, K., Laukens, K., Chen, K., Barakat, K., & Bittremieux, W. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01301 · Cited by 37 · see citing works
Machine learning to empower electrohydrodynamic processing
Wang, F., Elbadawi, M., Tsilova, S. L., Gaisford, S., Basit, A. W., & Parhizkar, M. (2021) · Materials Science and Engineering C · doi:10.1016/j.msec.2021.112553 · Cited by 36 · see citing works
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The pursuit of accurate predictive models of the bioactivity of small molecules
Martínez‐Mayorga, K., Rosas-Jiménez, J. G., Gonzalez-Ponce, K., López‐López, E., Neme, A., & Medina‐Franco, J. L. (2024) · Chemical Science · doi:10.1039/d3sc05534e · Cited by 38 · see citing works
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In silico methods for the prediction of drug toxicity
Rajpoot, K., Desai, N., Koppisetti, H., Tekade, M., Sharma, M. C., Behera, S. K., & Tekade, R. K. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-98367-9.00012-3 · Cited by 35 · see citing works
Trends in Deep Learning for Property-driven Drug Design
Born, J., & Manica, M. (2021) · Current Medicinal Chemistry · doi:10.2174/0929867328666210729115728 · Cited by 35 · see citing works
Liu, G., Singha, M., Pu, L., Neupane, P., Feinstein, J., Wu, H., Ramanujam, J., & Bryliński, M. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00540-0 · Cited by 32 · see citing works
Self-citationPiquing 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
“However, these techniques can be time ‐ consuming, expensive, and frequently produce inaccurate results (Pu et al., 2019).”
BackgroundMoshawih, S., Goh, H. P., Kifli, N., Idris, A. C., Yassin, H., Kotra, V., Goh, K. W., Liew, K. B., & Ming, L. C. (2022) · Chemical Biology & Drug Design · doi:10.1111/cbdd.14062 · Cited by 31 · see citing works
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Application of Negative Design To Design a More Desirable Virtual Screening Library
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A deep learning based multi-model approach for predicting drug-like chemical compound’s toxicity
Saravanan, K. M., Wan, J., Dai, L., Zhang, J., Zhang, J. Z. H., & Zhang, H. (2024) · Methods · doi:10.1016/j.ymeth.2024.04.020 · Cited by 25 · see citing works
Revolutionizing adjuvant development: harnessing AI for next-generation cancer vaccines
Zhang, W., Zheng, X., Coghi, P. S., Chen, J.-H., Dong, B.-J., & Fan, X. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1438030 · Cited by 27 · see citing works
“Furthermore, eToxPred is a software capable of evaluating candidate compound toxicity based on machine learning methods (140).”
MethodMultitask CapsNet: An Imbalanced Data Deep Learning Method for Predicting Toxicants
Wang, Y., Wang, B., Jiang, J., Guo, J., Lai, J., Lian, X., & Wu, J. (2021) · ACS Omega · doi:10.1021/acsomega.1c03842 · Cited by 24 · see citing works
Toxicity prediction of small drug molecules of androgen receptor using multilevel ensemble model
Gupta, V. K., & Rana, P. S. (2019) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720019500331 · Cited by 24 · see citing works
Aarón, R.-H., Cortazar-Moya, S., González-Pérez, J. E., Jiménez-González, O., López‐Malo, A., & Ismael, M.-C. J. (2025) · Discover Chemistry. · doi:10.1007/s44371-025-00201-3 · Cited by 23 · see citing works
“• Molecular fingerprints are used to predict, in silico, the toxicity and accessibility properties of natural compounds in pharmaceutical drug development [18].”
Supervised chemical graph mining improves drug-induced liver injury prediction
Lim, S., Kim, Y., Gu, J., Lee, S., Shin, W., & Kim, S. (2022) · iScience · doi:10.1016/j.isci.2022.105677 · Cited by 22 · see citing works
Mothra: Multiobjective de novo Molecular Generation Using Monte Carlo Tree Search
Suzuki, T., Ma, D., Yasuo, N., & Sekijima, M. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00759 · Cited by 21 · 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
Kim, M., & Hong, S. (2024) · Technologies · doi:10.3390/technologies12070095 · Cited by 22 · see citing works
“, outperforms its competitors in toxicity prediction [121,122].”
BackgroundMethodBioinformatics and Cheminformatics Tools in Early Drug Discovery
Parikh, P. K., Savjani, J., Gajjar, A. K., & Chhabria, M. T. (2023) · Wiley eBooks · doi:10.1002/9781119865728.ch8 · Cited by 18 · see citing works
Bharadwaj, S., Deepika, K., Kumar, A., Jaiswal, S., Miglani, S., Singh, D., Fartyal, P., Kumar, R., Singh, S., Singh, M. P., et al. (2024) · Chemical Biology & Drug Design · doi:10.1111/cbdd.14639 · Cited by 19 · see citing works
“The DeepDrug group comprises several AI-based tools in its purse, such as eSynth (Naderi et al. 2016), eToxPred (Pu et al. 2019), eDrugRes, eMolFrag (Liu et al. 2017), eVir, eComb, which can automatically synthe-size molecules via beam research techniques along with along filtration as per chemical…”
Srivastava, M., Nandan, S., Zaidi, A., Samani, A. S., Shukla, V., Aslam, H., Srivastava, A., Maurya, P., Khan, M. A., Khan, M. F., et al. (2025) · ChemistrySelect · doi:10.1002/slct.202404446 · Cited by 25 · see citing works
Mottaghi-Dastjerdi, N., & Soltany-Rezaee-Rad, M. (2024) · Iranian journal of pharmaceutical research · doi:10.5812/ijpr-150510 · Cited by 17 · see citing works
“Artificial intelligence significantly aids in this area by providing accurate predictions, essential for assessing new compounds' drug-likeness and potential success (21).”
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
Chikhale, R. V., Eldesoky, G. E., Kolpe, M. S., Suryawanshi, V. S., Patil, P. C., & Bhowmick, S. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e26802 · Cited by 17 · see citing works
“Molecules found after the screening of SelleckChem against Mtb protein were taken for toxicity and synthetic accessibility calculation using eToxPred tool [34].”
MethodPrior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity
Shavalieva, G., Papadokonstantakis, S., & Peters, G. M. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01079 · Cited by 15 · see citing works
Chao, C., Tsai, Y.-T., Lee, W.-T., Yeh, H., & Chiang, C. (2022) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2022/4378413 · Cited by 15 · see citing works
“According to experiences from synthetic biology, especially structure-based predictions based on machine learning techniques similar to ours, synthetic bioactive compounds may not necessarily exhibit the same biologic e ff ects (therapeutic or toxicity ones) as their parent compounds [59].”
Applying knowledge-driven mechanistic inference to toxicogenomics
Tripodi, I. J., Callahan, T. J., Westfall, J., Meitzer, N. S., Dowell, R. D., & Hunter, L. (2020) · Toxicology in Vitro · doi:10.1016/j.tiv.2020.104877 · Cited by 15 · see citing works
Bess, A., Berglind, F., Mukhopadhyay, S., Bryliński, M., Alvin, C., Fattah, F., & Wasan, K. M. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1297924 · Cited by 14 · see citing works
Self-citationArtificial Intelligence: Comprehensive Overview and its Pharma Application
Patel, S. S., & Shah, S. A. (2022) · Asian Journal of Pharmacy and Technology · doi:10.52711/2231-5713.2022.00054 · Cited by 13 · see citing works
FusionPDB: a knowledgebase of human fusion proteins
Kumar, H., Tang, L.-Y., Yang, C., & Kim, P. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad920 · Cited by 13 · see citing works
“-end of the protein feature region.”
AI's role in pharmaceuticals: Assisting drug design from protein interactions to drug development
Bechelli, S., & Delhommelle, J. (2023) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2023.100038 · Cited by 14 · see citing works
ToxinPredictor: Computational models to predict the toxicity of molecules
Goel, M., Amawate, A., Singh, A., & Bagler, G. (2024) · Chemosphere · doi:10.1016/j.chemosphere.2024.143900 · Cited by 13 · see citing works
Jalal, K., Khan, K., Hayat, A., Ahmad, D., Alotaibi, G., Uddin, R., Mashraqi, M. M., Alzamami, A., Aurongzeb, M., & Basharat, Z. (2022) · Molecular Diversity · doi:10.1007/s11030-022-10455-z · Cited by 13 · see citing works
In Silico Prediction of Oral Acute Rodent Toxicity Using Consensus Machine Learning
Schieferdecker, S., Rottach, F., & Vock, E. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00056 · Cited by 12 · see citing works
Computational anti-COVID-19 drug design: progress and challenges
Wang, J., Zhang, Y., Nie, W., Luo, Y., & Deng, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab484 · Cited by 12 · see citing works
Hao, N., Sun, P., Zhao, W., & Li, X. (2023) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2023.114806 · Cited by 11 · see citing works
Kosonocky, C. W., Feller, A. L., Wilke, C. O., & Ellington, A. D. (2023) · Patterns · doi:10.1016/j.patter.2023.100865 · Cited by 13 · see citing works
“9–16 In particular, the application of languagemodelstothisspacehasledtosurprisingsuccessinpre-dicting biochemical features such as drug-likeness and protein-ligand interactions.”
BackgroundDrug discovery in the context of precision medicine and artificial intelligence
Hasanzad, M., Nosrati, M., Khatami, F., Rahmani, P., Sarhangi, N., Nikfar, S., & Abdollahi, M. (2024) · Expert Review of Precision Medicine and Drug Development · doi:10.1080/23808993.2024.2393089 · Cited by 11 · see citing works
“Additionally, AI-based approaches can predict the potential adverse effects of drug candidates [29].”
Zhang, X., Wang, H., Du, Z., Zhuo, L., Fu, X., Cao, D., Xie, B., & Li, K. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3510297 · Cited by 11 · see citing works
Zhang, H., Luo, Q.-Q., Hu, M., Wang, N., Qi, H.-Z., Zhang, H.-R., & Ding, L. (2022) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2022.106340 · Cited by 11 · see citing works
Khanduja, A., Kumar, M., & Mohanty, D. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad101 · Cited by 11 · see citing works
Remodelling structure-based drug design using machine learning
Dutta, S., & Bose, K. (2021) · Emerging Topics in Life Sciences · doi:10.1042/etls20200253 · Cited by 11 · see citing works
Ghosh, A., Choudhary, G. P., & Medhi, B. (2024) · Indian Journal of Pharmacology · doi:10.4103/ijp.ijp_81_24 · Cited by 9 · see citing works
Gosavi, A. P. V., Nandgude, T. D., Mishra, R. K., & Puri, D. B. (2024) · AAPS PharmSciTech · doi:10.1208/s12249-024-02816-8 · Cited by 10 · see citing works
“…can learn from data patterns and create predictions based on the data, which can aid in optimizing the formulation process and prediction of outcomes [41–43]. c) Reduced Variability: AI approaches can reduce variability in the formulation process by detecting and correcting errors in real time.”
Arab, I., Laukens, K., & Bittremieux, W. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01102 · Cited by 10 · see citing works
Pandey, S. K., & Roy, K. (2024) · Toxicology Reports · doi:10.1016/j.toxrep.2024.101822 · Cited by 10 · see citing works
Impact of AI on drug delivery and pharmacokinetics: The present scenario and future prospects
Prajapati, J. B., Paliwal, H., Saikia, S., Prajapati, B. G., Prajapati, D. N., Philip, A. K., & Faiyazuddin, M. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-89925-3.00016-2 · Cited by 11 · see citing works
Khan, K., Basharat, Z., Jalal, K., Mashraqi, M. M., Alzamami, A., Alshamrani, S., & Uddin, R. (2022) · Antibiotics · doi:10.3390/antibiotics11050680 · Cited by 10 · see citing works
“Early knowledge of medication toxicity and side effects is crucial for drug induction in the development pipeline [74,75].”
BackgroundHasan, R., Bhuia, M. S., Chowdhury, R., Saha, S., Khan, M. A., Afroz, M., Ansari, S. A., Ansari, I. A., Coutinho, H. D. M., & Islam, M. T. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109298 · Cited by 9 · see citing works
Artificial Intelligence in Head and Neck Cancer: Towards Precision Medicine
Hagen, J., Hornung, L. J., Barham, W. T., Mukhopadhyay, S., Bess, A., Contrera, K. J., Basu, D., Sandulache, V. C., Spielmann, G., & Kansara, S. (2025) · Cancers · doi:10.3390/cancers17183023 · Cited by 9 · see citing works
Self-citationMao, J. W., Luo, Q.-Q., Zhang, H., Zheng, X.-H., Shen, C., Qi, H.-Z., Hu, M., & Zhang, H. (2021) · Chemico-Biological Interactions · doi:10.1016/j.cbi.2021.109784 · Cited by 9 · see citing works
Artificial Intelligence and Machine Learning in Healthcare Sector
Chavda, V. P., Patel, K., Patel, S. H., & Apostolopoulos, V. (2023) · Wiley eBooks · doi:10.1002/9781119865728.ch13 · Cited by 10 · see citing works
Employing Supervised Algorithms for the Prediction of Nanomaterial’s Antioxidant Efficiency
Mirzaei, M., Furxhi, I., Murphy, F., & Mullins, M. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032792 · Cited by 8 · see citing works
“This approach is data intensive [31] and has been applied in fields of medicine including disease prediction [32], the discovery of new medicine [33], the prediction of drug responses [34], medicine’s toxicity [35], nanotoxicity [36], antimicrobial resistance [37], antibiotic resistance [38] and the antibacterial capa…”
Computer-aided technologies in drug discovery and toxicity prediction
Pawar, B., Behera, S. K., Tekade, M., Al‐Shar’i, N. A., & Tekade, R. K. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15840-7.00004-x · Cited by 8 · see citing works
Ikhtiar, F., Jamal, A., & Bokhari, S. M. S. M. (2025) · Discover Oncology · doi:10.1007/s12672-025-03535-7 · Cited by 8 · see citing works
Zhang, C. N., Xie, L., Lü, X., Mao, R., Xu, L., & Xu, X. (2024) · Molecules · doi:10.3390/molecules29071499 · Cited by 8 · see citing works
“Therefore, we applied eToxPred [32] to compute the toxicity of the generated molecules.”
MethodPrediction of toxicity of secondary metabolites
Hernández-Alvarado, R. B., Madariaga‐Mazón, A., & Martínez‐Mayorga, K. (2019) · Physical Sciences Reviews · doi:10.1515/psr-2018-0107 · Cited by 8 · see citing works
“For example, eToxPred allows the estimation of toxicity of drug candidates based on Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN) and Extremely Randomized Trees, or Extra Trees (ET) algorithm [8].”
MethodChoi, K., & Kim, J. Y. (2024) · Food Science and Biotechnology · doi:10.1007/s10068-024-01701-1 · Cited by 9 · see citing works
“Based on HSDB, Pu et al. estimated the toxicity of drug candidates by employing SVM algorithm (Pu et al., 2019).”
Malani, M., Hiremath, M. S., Sharma, S., Jhunjhunwala, M., Gayen, S., Hota, C., & Nirmal, J. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2226717 · Cited by 7 · see citing works
“…such as machine learning and computer simulations can aid in better understanding drug-transporter interactions and drug toxicity with high reproducibility and reliability (Jain & Ecker, 2019; Jensen et al., 2021; Khuri & Deshmukh, 2018; Liu et al., 2016; Pu et al., 2019; Vamathevan et al., 2019).”
BackgroundStevenson, G. A., Kirshner, D., Bennion, B. J., Yang, Y., Zhang, X., Zemla, A., Torres, M. W., Epstein, A., Jones, D., Kim, H., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00722 · Cited by 7 · see citing works
Huang, L., Yuan, Z. P., Yan, H., Sheng, R., Liu, L., Wang, F., Xie, W., Chen, N., Huang, F., Huang, S., et al. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3387402 · Cited by 7 · see citing works
“…measurement to evaluate how the generated molecule fits into the protein pocket of interest; 5) Toxicity score is the value outputted by eToxPred [52] that indicate the toxicity probability; 6) SA (synthetic accessibility) measures the molecule synthetic accessibility; 7) QED estimates the…”
BackgroundDeveloping liver cancer drug response prediction system using late fusion of reduced deep features
Hassan, M., Ali, S., Alquhayz, H., Kim, J. Y., & Sanaullah, M. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2022.07.024 · Cited by 7 · see citing works
Artificial Intelligence for Assessing Side Effects
Panchbhai, A. (2022) · Wiley eBooks · doi:10.1002/9781394167258.ch12 · Cited by 7 · see citing works
- Authors
- Pu, L., Naderi, M., Liu, T., Wu, H.-C., Mukhopadhyay, S., & Brylinski, M.
- Affiliations
- Louisiana State University (US)
- Venue
- BMC Pharmacology and Toxicology
- Published
- 2019-01-08
- Volume
- 20
- Issue
- 1
- Pages
- 2-2
- ISSN
- 2050-6511 (ISSN-L)
- PMC
- PMC6325674
- OpenAlex
- W2918544128
- Cited by
- 212
- References
- 90
- Citations by year
- 2026: 21 · 2025: 33 · 2024: 50 · 2023: 44 · 2022: 29 · 2021: 20 · 2020: 10 · 2019: 5
- FWCI
- 10.88
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Pharmacology and Toxicology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Civil War Book Review (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute of General Medical Sciences (R35GM119524)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Keywords
- toxicity prediction, drug candidates, synthetic accessibility, molecular fingerprints, machine learning, virtual screening, small organic molecules, drug-likeness, chemical toxicity
- MeSH terms
- Machine Learning, Algorithms, Animals, Humans, Drug Discovery, Drug-Related Side Effects and Adverse Reactions
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