ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches
Open accessAbstract
The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On ten-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93%) and Matthews’s correlation coefficient (0.84). The performances of all the three models were comparable (Matthews’s correlation coefficient=0.84 - 0.87) on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better (R2 =0.84) than the multi-linear regression and partial least squares regression models, whereas, the partial least square based regression model for the prediction of permeability (caco-2) performed better (R2= 0.68) in comparison to the random forest and multi-linear regression based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity, solubility, and permeability of small molecules.
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RF usually outperformed other machine learning models when molecular fingerprints based on simple molecular properties and 2D structures were combined with a variety of models for toxicity prediction.
“By comparing their performances, it was found that RF usually outperformed [91].”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [91]
Chemical properties can also be used for classification and prediction.
“Being determined by molecular structures, chemical properties (molecular weight, degradation rate, solubility coefficient in different solvents, molar index, permeability, etc.) can also be used for classification and prediction (e.g., Figure 1d) [90,91].”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [91]
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The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856 · see citing works
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594 · see citing works
Wold, S., Esbensen, K. H., & Geladi, P. L. M. (1987) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(87)80084-9 · Cited by 11,792 · see citing works
Gentleman R: R: A language for data analysis and graphics
Ihaka, R., & Gentleman, R. (1996) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1996.10474713 · Cited by 11,747 · see citing works
Partial least-squares regression: a tutorial
Geladi, P. L. M., & Kowalski, B. R. (1986) · Analytica Chimica Acta · doi:10.1016/0003-2670(86)80028-9 · Cited by 7,016 · see citing works
The Lifelong Effects of Early Childhood Adversity and Toxic Stress
Shonkoff, J. P., Garner, A. S., THE COMMITTEE ON PSYCHOSOCIAL ASPECTS OF CHILD AND FAMILY HEALTH, C. O. E. C., Siegel, B. S., Dobbins, M. I., Earls, M. F., Garner, A. S., McGuinn, L., Pascoe, J., & Wood, D. L. (2012) · Pediatrics · doi:10.1542/peds.2011-2663 · Cited by 5,164 · cited 1× · see citing works
“Several of these chemicals are known to cause adverse drug reactions, non-acute and subacute poisoning leading to allergic reactions, and sometimes disability or death due to their mutagenic, carcinogenic or toxic nature (Shonkoff et al., 2012).”
BackgroundThe Merck Index: An Encyclopedia of Chemicals, Drugs, and Biologicals
(1990) · Annals of Internal Medicine · doi:10.7326/0003-4819-113-6-487_2 · Cited by 4,508 · see citing works
Supervised Machine Learning: A Review of Classification Techniques
Kotsiantis, S. B. (2007) · Informatica · Cited by 4,192 · see citing works
DrugBank: a comprehensive resource for in silico drug discovery and exploration
Wishart, D. S. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj067 · Cited by 3,892 · 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
Li, T.-H. (2013) · Chapman & Hall/CRC eBooks · doi:10.1201/b15154-8 · Cited by 2,750 · see citing works
admetSAR: A Comprehensive Source and Free Tool for Assessment of Chemical ADMET Properties
Cheng, F., Li, W., Zhou, Y., Shen, J., Wu, Z., Liu, G., Lee, P. W., & Tang, Y. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300367a · Cited by 2,233 · see citing works
Particulate air pollution and acute health effects
Seaton, A., Godden, D., MacNee, W., & Donaldson, K. (1995) · The Lancet · doi:10.1016/s0140-6736(95)90173-6 · Cited by 2,138 · see citing works
Manuscripts Accepted for Publication
(2005) · Child Development · doi:10.1111/j.1467-8624.2005.00876.x · Cited by 2,051 · see citing works
Artursson, P., & Karlsson, J. E. (1991) · Biochemical and Biophysical Research Communications · doi:10.1016/0006-291x(91)91647-u · Cited by 1,899 · see citing works
Toxicity determined in vitro by morphological alterations and neutral red absorption
Borenfreund, E., & Puerner, J. A. (1985) · Toxicology Letters · doi:10.1016/0378-4274(85)90046-3 · Cited by 1,893 · see citing works
DeepTox: toxicity prediction using deep learning
Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016) · Frontiers in Environmental Science · doi:10.3389/fenvs.2015.00080 · Cited by 1,081 · see citing works
Casarett and Doull's toxicology: The basic science of poisons
Pickrell, J. (1996) · Toxicology Letters · doi:10.1016/s0378-4274(96)90054-5 · Cited by 1,062 · see citing works
BiGG Models: A platform for integrating, standardizing and sharing genome-scale models
King, Z. A., Lu, J., Dräger, A., Miller, P., Federowicz, S., Lerman, J. A., Ebrahim, A., Palsson, B. O., & Lewis, N. E. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1049 · Cited by 938 · see citing works
Caco-2 cell permeability assays to measure drug absorption
van Breemen, R. B., & Li, Y. (2005) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1517/17425255.1.2.175 · Cited by 602 · see citing works
Schneider, A., Hommel, G., & Blettner, M. (2010) · Deutsches Ärzteblatt international · doi:10.3238/arztebl.2010.0776 · Cited by 526 · see citing works
Hinderliter, P. M., Minard, K. R., Orr, G., Chrisler, W., Thrall, B. D., Pounds, J. G., & Teeguarden, J. (2010) · Particle and Fibre Toxicology · doi:10.1186/1743-8977-7-36 · Cited by 501 · see citing works
Methanol and Ethylene Glycol Poisonings
Jacobsen, D., & McMartin, K. E. (1986) · Medical Toxicology · doi:10.1007/bf03259846 · Cited by 491 · see citing works
ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation
Dong, J. N., Cao, D., Miao, H., Shao, L., Deng, B., Yun, Y., Wang, N., Lü, A., Zeng, W., & Chen, A. F. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0109-z · Cited by 407 · see citing works
ChemmineR: a compound mining framework for R
Cao, Y., Charisi, A., Cheng, L.-C., Jiang, T., & Girke, T. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn307 · Cited by 376 · see citing works
Random Forest Models To Predict Aqueous Solubility
Palmer, D. S., O’Boyle, N. M., Glen, R. C., & Mitchell, J. B. O. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci060164k · Cited by 366 · see citing works
Roberts, C. L., Keita, Å. V., Duncan, S. H., O’Kennedy, N., Söderholm, J. D., Rhodes, J. M., & Campbell, B. J. (2010) · Gut · doi:10.1136/gut.2009.195370 · Cited by 285 · see citing works
Toxicity of concentrated sodium hypochlorite used as an endodontic irrigant
Gernhardt, C. R., Eppendorf, K., Kozlowski, A., & Brandt, M. (2004) · International Endodontic Journal · doi:10.1111/j.0143-2885.2004.00804.x · Cited by 248 · see citing works
Wang, N., Dong, J. N., Deng, Y.-H., Zhu, M.-F., Wen, M., Yao, Z., Lü, A., Wang, J., & Cao, D. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00642 · Cited by 244 · see citing works
Xue, L., & Bajorath, J. (2000) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/1386207003331454 · Cited by 244 · see citing works
T3DB: a comprehensively annotated database of common toxins and their targets
Lim, E. L., Pon, A., Djoumbou-Feunang, Y., Knox, C., Shrivastava, S., Guo, A. C., Neveu, V., & Wishart, D. S. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp934 · Cited by 168 · see citing works
Recent advances in total least squares techniques and errors-in-variables modeling
Huffel, S. V., & Modeling, E.-I.-V. (1997) · Society for Industrial and Applied Mathematics eBooks · Cited by 163 · see citing works
In Vitro Techniques for the Assessment of Neurotoxicity
Harry, G. J., Billingsley, M. L., Bruinink, A., Campbell, I. L., Classen, W., Dorman, D. C., Galli, C. L., Ray, D., Smith, R. A., & Tilson, H. A. (1998) · Environmental Health Perspectives · doi:10.1289/ehp.98106s1131 · Cited by 158 · see citing works
LONGLEY, E. (1975) · The Medical Journal of Australia · doi:10.5694/j.1326-5377.1975.tb82090.x · Cited by 158 · see citing works
Reconsidering the effects of monosodium glutamate: A literature review
Freeman, M. (2006) · Journal of the American Academy of Nurse Practitioners · doi:10.1111/j.1745-7599.2006.00160.x · Cited by 150 · see citing works
Oncometabolites: tailoring our genes
Nowicki, S., & Gottlieb, E. (2015) · FEBS Journal · doi:10.1111/febs.13295 · Cited by 145 · see citing works
The Health Risks of Saccharin Revisited
Ellwein, L. B., & Cohen, S. M. (1990) · Critical Reviews in Toxicology · doi:10.3109/10408449009089867 · Cited by 119 · see citing works
Mesothelioma from Chrysotile Asbestos: Update
Kanarek, M. S. (2011) · Annals of Epidemiology · doi:10.1016/j.annepidem.2011.05.010 · Cited by 115 · see citing works
The log P Parameter as a Molecular Descriptor in the Computer-aided Drug Design – an Overview
Kujawski, J., Popielarska, H., Myka, A., Drabińska, B., & Bernard, M. K. (2012) · Computational Methods in Science and Technology · doi:10.12921/cmst.2012.18.02.81-88 · Cited by 109 · see citing works
Bergström, C. A. S. (2005) · Basic & Clinical Pharmacology & Toxicology · doi:10.1111/j.1742-7843.2005.pto960303.x · Cited by 71 · see citing works
Possible neurologic effects of aspartame, a widely used food additive.
Maher, T. J., & Wurtman, R. J. (1987) · Environmental Health Perspectives · doi:10.1289/ehp.877553 · Cited by 70 · see citing works
Clinical toxicology of commercial products
Belokon, S. V. (1957) · The Journal of Pediatrics · doi:10.1016/s0022-3476(57)80185-1 · Cited by 34 · see citing works
RELATIONSHIP OF HYDROCARBON SOLUBILITY TO TOXICITY IN ALGAE AND CELLULAR MEMBRANE EFFECTS
Hutchinson, T. C., Hellebust, J. A., Mackay, D., Tarn, D., & Kauss, P. B. (1979) · International Oil Spill Conference Proceedings · doi:10.7901/2169-3358-1979-1-541 · Cited by 33 · see citing works
ToxiPred: A Server for Prediction of Aqueous Toxicity of Small Chemical Molecules in T. Pyriformis
Mishra, N. K., Singla, D., Agarwal, S., & Raghava, G. P. S. (2013) · Journal of Translational Toxicology · doi:10.1166/jtt.2014.1005 · Cited by 13 · see citing works
Interleukin-2, killer cells and cancer therapy: an overview.
Parkinson, D., & Lotzová, E. (1990) · PubMed · Cited by 6 · see citing works
Fountain, J. S., & Reith, D. M. (2014) · PubMed · Cited by 2 · see citing works
Flagged first, then by citations
2 of the 49 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Budavari S. (ed.). (1989). The Merck Index-Encyclopedia of Chemicals, Drugs and Biologicals. Rahway, NJ: Merck and Co., Inc., 446.
Cited by
Sorted by citations
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
Wu, Y., & Wang, G. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19082358 · Cited by 193 · see citing works
Singh, A. K., Bilal, M., Iqbal, H. M. N., & Raj, A. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.144561 · Cited by 146 · 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
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
Doherty, A., Wall, A. M., Khaldi, N., & Kussmann, M. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.768979 · Cited by 67 · see citing works
Improving Enzyme Optimum Temperature Prediction with Resampling Strategies and Ensemble Learning
Gado, J. E., Beckham, G. T., & Payne, C. M. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00489 · Cited by 63 · 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
Chandrasekar, V., Mohammad, S., Aboumarzouk, O. M., Singh, A. V., & Dakua, S. P. (2025) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.137071 · Cited by 51 · see citing works
Zhang, S., Thompson, J. P., Xia, J., Bogetti, A. T., York, F., Skillman, A. G., Chong, L. T., & LeBard, D. N. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01540 · Cited by 45 · see citing works
ChAlPred: A web server for prediction of allergenicity of chemical compounds
Sharma, N., Patiyal, S., Dhall, A., Naorem, L. D., & Raghava, G. P. S. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104746 · Cited by 45 · see citing works
Srivastava, G. N., Malwe, A. S., Sharma, A. K., Shastri, V., Hibare, K., & Sharma, V. K. (2020) · Genomics · doi:10.1016/j.ygeno.2020.03.020 · Cited by 41 · see citing works
Self-citationIn 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
Meng, L., Zhou, B., Liu, H., Chen, Y., Yuan, R., Chen, Z., Luo, S., & Chen, H. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.174201 · Cited by 34 · see citing works
Thapa, S., Nargund, S. L., & Biradar, M. S. (2023) · Journal of Chemistry · doi:10.1155/2023/9307613 · Cited by 34 · see citing works
Yao, R., Heinrich, M., Wang, Z., & Weckerle, C. S. (2018) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2018.06.010 · Cited by 32 · 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
In silico approach for identifying natural lead molecules against SARS-COV-2
Gupta, S. S., Kumar, A., Shankar, R., & Sharma, U. K. (2021) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2021.107916 · Cited by 25 · 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
From molecules to data: the emerging impact of chemoinformatics in chemistry
Chetry, A. B., & Ohto, K. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-00978-6 · Cited by 22 · see citing works
Computational investigation of quinazoline derivatives as Keap1 inhibitors for Alzheimer's disease
Gote, S. A., Thapa, S., Dubey, S., Nargund, S. L., & Biradar, M. S. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101334 · Cited by 22 · see citing works
Elkaeed, E. B., Metwaly, A. M., Alesawy, M. S., Saleh, A. M., Alsfouk, A. A., & Eissa, I. H. (2022) · Life · doi:10.3390/life12091407 · Cited by 21 · see citing works
Malwe, A. S., & Sharma, V. K. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1254073 · Cited by 21 · see citing works
Self-citationData Science in Chemical Engineering: Applications to Molecular Science
Ashraf, C. M., Joshi, N., Beck, D. A. C., & Pfaendtner, J. (2021) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-101220-102232 · Cited by 20 · see citing works
Sadik, K., Byadi, S., Hachim, M. E., Hamdani, N. E., Podlipnik, Č., & Aboulmouhajir, A. (2021) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2021.130571 · Cited by 17 · see citing works
Gupta, V. K., & Rana, P. S. (2020) · The Computer Journal · doi:10.1093/comjnl/bxaa001 · Cited by 16 · 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
Health effects of pharmaceuticals and personal care products
Jamil, K. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816189-0.00005-6 · Cited by 13 · see citing works
Toward efficient generation, correction, and properties control of unique drug‐like structures
Druchok, M., Yarish, D., Gurbych, O., & Maksymenko, M. (2021) · Journal of Computational Chemistry · doi:10.1002/jcc.26494 · 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
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
Progress of machine learning in the application of small molecule druggability prediction
Li, J., Zhang, J., Guo, R. F., Dai, J., Niu, Z., Wang, Y., Wang, T., Jiang, X., & Hu, W. (2025) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2025.117269 · Cited by 9 · see citing works
Rovenchak, A. A., & Druchok, M. (2024) · Journal of Computational Chemistry · doi:10.1002/jcc.27292 · Cited by 9 · see citing works
Molecular Docking and Pharmacodynamic Study of Potential Inhibitors of Streptococcus mutans Biofilm
Soni, A. N., Varshney, M., & Tale, V. S. (2023) · Biomedical and Biotechnology Research Journal (BBRJ) · doi:10.4103/bbrj.bbrj_176_23 · Cited by 9 · see citing works
Prediction of adverse biological effects of chemicals using knowledge graph embeddings
Myklebust, E. B., Jiménez-Ruiz, E., Chen, J., Wolf, R. L., & Tollefsen, K. E. (2022) · Semantic Web · doi:10.3233/sw-222804 · Cited by 9 · see citing works
Darwish, S. M., Shendi, T. A., & Younes, A. (2019) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2019.103826 · Cited by 9 · see citing works
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
AI/ML-based computational models for toxicity prediction
Barua, S., Balaji, B., & Balaji, S. (2026) · Environmental Science and Pollution Research · doi:10.1007/s11356-025-37354-8 · Cited by 8 · see citing works
Artificial intelligence in antibiotic discovery: Applications, challenges, and future outlook
Xu, J. B., Li, C., Wang, X., Peleg, A. Y., LI, F., Song, J., & de la Fuente‐Núñez, C. (2026) · Cell Biomaterials · doi:10.1016/j.celbio.2026.100445 · Cited by 8 · see citing works
Toxicity Detection in Soap using Deep Learning
Ananthajothi, K., Prathima, S. Y., Ps, S., Varshini, V., & Tharun, M. (2023) · International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) · doi:10.1109/icses60034.2023.10465402 · Cited by 8 · see citing works
Pant, A. B. (2020) · Alternatives to Laboratory Animals · doi:10.1177/0261192920986811 · Cited by 8 · see citing works
Stevenson, 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
Application of Docking for Lead Optimization
Patra, J., Singh, D., Jain, S., & Mahindroo, N. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822312-3.00012-6 · Cited by 7 · see citing works
Mishra, S. K., Hamadou, M., Gurung, K., Subba, A., Mukhia, P., Chhetri, T., Roy, A., Rai, P., & Georrge, J. J. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10617-4 · Cited by 6 · see citing works
Self-citationVarela-Rodríguez, L., Calzada, F., Velázquez-Domínguez, J. A., Hernández‐Ramírez, V. I., Varela-Rodríguez, H., Bautista, E., Herrera-Martínez, M., Pichardo-Hernández, D. L., Castellanos-Mijangos, R. D., Chávez‐Munguía, B., et al. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms251910633 · Cited by 6 · see citing works
Rude, C. I., Smith, J. N., Scott, R. P., Schultz, K., Anderson, K. A., & Tanguay, R. L. (2025) · Toxicological Sciences · doi:10.1093/toxsci/kfaf039 · Cited by 6 · see citing works
ARULANANDAM, C. D., Babu, V., Soorni, Y., & Prathiviraj, R. (2024) · Toxicology Research · doi:10.1093/toxres/tfaf008 · Cited by 6 · see citing works
Darwish, S. M., Shendi, T. A., & Younes, A. (2019) · Expert Systems · doi:10.1111/exsy.12415 · Cited by 6 · see citing works
Singh, S., Kumar, K., Rao, A., & Prajapati, V. K. (2024) · Fungal biology · doi:10.1007/978-3-031-49112-2_9 · Cited by 5 · see citing works
Synthesis of New Glucose-containing 5-Arylisoxazoles and their Enzyme Inhibitory Activity
Hariri, R., Iraji, A., Mojtabavi, S., Saeedi, M., Faramarzi, M. A., Amini, M., & Akbarzadeh, T. (2024) · Letters in Organic Chemistry · doi:10.2174/0115701786283334231228104931 · Cited by 4 · see citing works
Pandiyan, S. M., Shanmugaraj, P., Manoharan, J. P., & Vidyalakshmi, S. (2021) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.1990130 · Cited by 4 · see citing works
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Tripathi, A., Alisha, A., Riya, R., Sriram, K., & Murugan, N. A. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4976-3_27 · Cited by 3 · see citing works
BFGTP: A BERT-Guided Two-Stage Molecular Representation Learning Framework for Toxicity Prediction
Hu, K., He, Y., Wei, J., Sun, C., Geng, J., Wei, L., & Su, R. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3556766 · Cited by 3 · see citing works
In silico tools and databases for designing cancer immunotherapy
Dhall, A., Jain, S., Sharma, N., Naorem, L. D., Kaur, D., Patiyal, S., & Raghava, G. P. S. (2022) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2021.11.008 · Cited by 3 · see citing works
Virtual Libraries for Docking Methods: Guidelines for the Selection and the Preparation
Sellami, A., Réau, M. F., Langenfeld, F., Lagarde, N., & Montès, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822312-3.00017-5 · Cited by 3 · see citing works
Machine Learning and Deep Learning Promote Computational Toxicology for Risk Assessment of Chemicals
Kusko, R. L., & Hong, H. (2023) · Computational methods in engineering & the sciences · doi:10.1007/978-3-031-20730-3_1 · 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
Computational Nanotoxicology and its Applications
Jabeen, S., Siddiqui, V. U., Bala, S., Khan, A. R., Raza, S., & Khan, T. (2023) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815196986123010012 · Cited by 2 · see citing works
Longwani, U., Sharma, A. K., Malwe, A. S., Jaiswal, S. K., & Sharma, V. K. (2025) · Gut Microbiome · doi:10.1017/gmb.2024.15 · Cited by 2 · see citing works
Self-citationInFlamPred: a machine learning framework for anti-inflammatory small molecule prediction
Selvam, S., Balaji, P. D., Uthra, R. A., Anupama, C. G., Sohn, H., & Madhavan, T. (2025) · Chemical Papers · doi:10.1007/s11696-025-04165-7 · Cited by 2 · see citing works
Bhavsar, D., Kutre, S., Shikhare, P., Kumar, S., Behera, S. K., & Chauthe, S. K. (2024) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2024.2330712 · Cited by 2 · see citing works
Data science-driven analyses of drugs inducing hypertension as an adverse effect
Sharma, R. (2020) · Molecular Diversity · doi:10.1007/s11030-020-10059-5 · Cited by 2 · see citing works
Towards the prediction of non-peptidic epitopes
Zierep, P. F., Vita, R., Blazeska, N., Moumbock, A. F. A., Greenbaum, J., Peters, B., & Günther, S. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009151 · Cited by 2 · see citing works
Naderian, S., & Samad‐Soltani, T. (2024) · International Journal of Drug Research in Clinics · doi:10.34172/ijdrc.2024.e13 · Cited by 2 · see citing works
In-silico Integration in Environmental Remediation
Achudhan, A. B., Masilamani, M., Kannan, P., & Saleena, L. M. (2023) · Springer eBooks · doi:10.1007/978-981-99-2564-3_13 · Cited by 1 · see citing works
Метод машинного навчання для створення нових лікарських речовин із заданими властивостями
Гурбич, О. (2022) · Науковий вісник Ужгородського університету Серія Математика і інформатика · doi:10.24144/2616-7700.2022.40(1).126-145 · Cited by 1 · see citing works
Computational approaches for bioengineering of cornea
Kumar, S., Uppal, S., Vipin, V. S., Tyagi, N., Kanwar, R. S., Wilfred, R., Singh, S., & Verma, Y. K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13222-3.00012-5 · Cited by 1 · see citing works
Human Stem Cells for Drug Toxicity Evaluation: A Comprehensive Review
Idris, M. M., & Nawaz, S. S. (2025) · Journal of Current Toxicology and Venomics · doi:10.2174/0129505704335182241218112943 · Cited by 1 · see citing works
From primes to paths: Enabling fast multi-relational graph analysis
Bougiatiotis, K., & Παλιούρας, Γ. (2026) · Data & Knowledge Engineering · doi:10.1016/j.datak.2026.102554 · Cited by 1 · see citing works
Machine Learning (ML) and Toxicity Studies
Ghosh, S., & Dasgupta, R. (2022) · Springer eBooks · doi:10.1007/978-981-16-8881-2_21 · Cited by 1 · see citing works
Nakashima, J., Takeuchi, M., Kawamoto, S., Monobe, K., Taira, J., & Aoki, S. (2022) · Journal of Applied Pharmaceutical Science · doi:10.7324/japs.2022.120805 · 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
Varela-Rodríguez, L., Velázquez-Domínguez, J. A., Hernández‐Ramírez, V. I., Varela-Rodríguez, H., Matus-Meza, A.-S., Calzada, F., Bautista, E., & Talamás‐Rohana, P. (2021) · doi:10.3390/ecms2021-10843
A Hands-On Perspective on Physico-chemical Versus AI/ML Methods Along the Genome to Drug Pathway
Jayaram, B. (2024) · Springer eBooks · doi:10.1007/978-3-031-75457-9_4
MultiTox: A sequence-based stacked ensemble model for multiclass protein toxin classification
Sharma, H., Thakur, M. S., Barala, A., Khan, M. S., Bhagat, S., & Bagler, G. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.147399
Ho, J., Marchenko, A., Arriola, E. R., & Majumdar, S. (2026) · ChemRxiv · doi:10.26434/chemrxiv.15000664/v1
CAPred: An effective prediction strategy for allergy through machine intelligence
Padmavathy, T., Thenmozhi, M., & J, S. K. (2026) · International Conference on Networking and Communications (ICNWC) · doi:10.1109/icnwc68145.2026.11518367
Language of Toxicity: An eXplainable Artificial Intelligence Approach
Amoroso, N., Pantaleo, E., Ciriaco, F., Cutropia, F., Gambacorta, N., Mastrolorito, F., Monaco, A., Orfino, A., Bellotti, R., & Nicolotti, O. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c03214
- Authors
- Sharma, A. K., Srivastava, G. N., Roy, A., & Sharma, V. K.
- Affiliations
- Indian Institute of Science Education and Research, Bhopal (IN)
- Corresponding author
- Sharma, Vineet K.
- Venue
- Frontiers in Pharmacology
- Publisher
- Frontiers Media SA
- Published
- 2017-11-30
- Volume
- 8
- Pages
- 880-880
- ISSN
- 1663-9812 (ISSN-L)
- PMC
- PMC5714866
- OpenAlex
- W2770630458
- Cited by
- 76
- References
- 49
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 11 · 2023: 11 · 2022: 11 · 2021: 15 · 2020: 6 · 2019: 4 · 2018: 2
- FWCI
- 2.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Pharmacology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministry of Education, India (CREST)
- Indian Institute of Science Education and Research Bhopal
- Indian Institute of Science Education and Research Mohali
- Core Research for Evolutional Science and Technology
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › History and advancements in chemistry
- Keywords
- chemical toxicity prediction, small molecules, water solubility, intestinal permeability, Caco-2 permeability, cheminformatics, molecular descriptors, molecular fingerprints, random forest regression, partial least squares regression
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