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For: Segall MD, Barber C. Addressing toxicity risk when designing and selecting compounds in early drug discovery. Drug Discovery Today 2014;19:688-93. [DOI: 10.1016/j.drudis.2014.01.006] [Cited by in Crossref: 64] [Cited by in F6Publishing: 56] [Article Influence: 8.0] [Reference Citation Analysis]
Number Citing Articles
1 Munson M, Lieberman H, Tserlin E, Rocnik J, Ge J, Fitzgerald M, Patel V, Garcia-Echeverria C. Lead optimization attrition analysis (LOAA): a novel and general methodology for medicinal chemistry. Drug Discov Today 2015;20:978-87. [PMID: 25814036 DOI: 10.1016/j.drudis.2015.03.010] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 1.6] [Reference Citation Analysis]
2 Zhang H, Ma J, Liu C, Ren J, Ding L. Development and evaluation of in silico prediction model for drug-induced respiratory toxicity by using naïve Bayes classifier method. Food and Chemical Toxicology 2018;121:593-603. [DOI: 10.1016/j.fct.2018.09.051] [Cited by in Crossref: 14] [Cited by in F6Publishing: 9] [Article Influence: 3.5] [Reference Citation Analysis]
3 Konteatis Z. What makes a good fragment in fragment-based drug discovery? Expert Opin Drug Discov 2021;16:723-6. [PMID: 33769176 DOI: 10.1080/17460441.2021.1905629] [Reference Citation Analysis]
4 Zhang H, Cao ZX, Li M, Li YZ, Peng C. Novel naïve Bayes classification models for predicting the carcinogenicity of chemicals. Food Chem Toxicol 2016;97:141-9. [PMID: 27597133 DOI: 10.1016/j.fct.2016.09.005] [Cited by in Crossref: 32] [Cited by in F6Publishing: 16] [Article Influence: 5.3] [Reference Citation Analysis]
5 Brayden DJ, Cryan SA, Dawson KA, O'Brien PJ, Simpson JC. High-content analysis for drug delivery and nanoparticle applications. Drug Discov Today 2015;20:942-57. [PMID: 25908578 DOI: 10.1016/j.drudis.2015.04.001] [Cited by in Crossref: 28] [Cited by in F6Publishing: 25] [Article Influence: 4.0] [Reference Citation Analysis]
6 Shaker B, Ahmad S, Lee J, Jung C, Na D. In silico methods and tools for drug discovery. Comput Biol Med 2021;137:104851. [PMID: 34520990 DOI: 10.1016/j.compbiomed.2021.104851] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Perryman AL, Patel JS, Russo R, Singleton E, Connell N, Ekins S, Freundlich JS. Naïve Bayesian Models for Vero Cell Cytotoxicity. Pharm Res 2018;35:170. [PMID: 29959603 DOI: 10.1007/s11095-018-2439-9] [Cited by in Crossref: 13] [Cited by in F6Publishing: 9] [Article Influence: 3.3] [Reference Citation Analysis]
8 Manivannan J, Silambarasan T, Kadarkarairaj R, Raja B. Systems pharmacology and molecular docking strategies prioritize natural molecules as cardioprotective agents. RSC Adv 2015;5:77042-55. [DOI: 10.1039/c5ra10761j] [Cited by in Crossref: 7] [Article Influence: 1.0] [Reference Citation Analysis]
9 Wang Y, Wang B, Jiang J, Guo J, Lai J, Lian XY, Wu J. Multitask CapsNet: An Imbalanced Data Deep Learning Method for Predicting Toxicants. ACS Omega 2021;6:26545-55. [PMID: 34661009 DOI: 10.1021/acsomega.1c03842] [Reference Citation Analysis]
10 Agarwal S, Verma E, Kumar V, Lall N, Sau S, Iyer AK, Kashaw SK. An integrated computational approach of molecular dynamics simulations, receptor binding studies and pharmacophore mapping analysis in search of potent inhibitors against tuberculosis. J Mol Graph Model 2018;83:17-32. [PMID: 29753941 DOI: 10.1016/j.jmgm.2018.04.019] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 0.8] [Reference Citation Analysis]
11 Pokhrel S, Bouback TA, Samad A, Nur SM, Alam R, Abdullah-Al-Mamun M, Nain Z, Imon RR, Talukder MEK, Tareq MMI, Hossen MS, Karpiński TM, Ahammad F, Qadri I, Rahman MS. Spike protein recognizer receptor ACE2 targeted identification of potential natural antiviral drug candidates against SARS-CoV-2. Int J Biol Macromol 2021;191:1114-25. [PMID: 34592225 DOI: 10.1016/j.ijbiomac.2021.09.146] [Reference Citation Analysis]
12 Pandit S, Singh P, Sinha M, Parthasarathi R. Integrated QSAR and Adverse Outcome Pathway Analysis of Chemicals Released on 3D Printing Using Acrylonitrile Butadiene Styrene. Chem Res Toxicol 2021;34:355-64. [PMID: 33416328 DOI: 10.1021/acs.chemrestox.0c00274] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Li Q, Li JF, Mao XR. Application of artificial intelligence in liver diseases: From diagnosis to treatment. Artif Intell Gastroenterol 2021; 2(5): 133-140 [DOI: 10.35712/aig.v2.i5.133] [Reference Citation Analysis]
14 Basile AO, Yahi A, Tatonetti NP. Artificial Intelligence for Drug Toxicity and Safety. Trends Pharmacol Sci 2019;40:624-35. [PMID: 31383376 DOI: 10.1016/j.tips.2019.07.005] [Cited by in Crossref: 37] [Cited by in F6Publishing: 17] [Article Influence: 12.3] [Reference Citation Analysis]
15 Wang MWH, Goodman JM, Allen TEH. Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models. Chem Res Toxicol 2021;34:217-39. [PMID: 33356168 DOI: 10.1021/acs.chemrestox.0c00316] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
16 Bácskay I, Nemes D, Fenyvesi F, Váradi J, Vasvári G, Fehér P, Vecsernyés M, Ujhelyi Z. Role of Cytotoxicity Experiments in Pharmaceutical Development. In: Çelik TA, editor. Cytotoxicity. InTech; 2018. [DOI: 10.5772/intechopen.72539] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
17 Hsu CW, Hewes KP, Stavitskaya L, Kruhlak NL. Construction and application of (Q)SAR models to predict chemical-induced in vitro chromosome aberrations. Regul Toxicol Pharmacol 2018;99:274-88. [PMID: 30278198 DOI: 10.1016/j.yrtph.2018.09.026] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 1.5] [Reference Citation Analysis]
18 Loiodice S, Nogueira da Costa A, Atienzar F. Current trends in in silico , in vitro toxicology, and safety biomarkers in early drug development. Drug and Chemical Toxicology 2019;42:113-21. [DOI: 10.1080/01480545.2017.1400044] [Cited by in Crossref: 13] [Cited by in F6Publishing: 9] [Article Influence: 2.6] [Reference Citation Analysis]
19 O'Brien PJ. High-content analysis in toxicology: screening substances for human toxicity potential, elucidating subcellular mechanisms and in vivo use as translational safety biomarkers. Basic Clin Pharmacol Toxicol 2014;115:4-17. [PMID: 24641563 DOI: 10.1111/bcpt.12227] [Cited by in Crossref: 64] [Cited by in F6Publishing: 51] [Article Influence: 8.0] [Reference Citation Analysis]
20 Xing Y, Wang Z, Li X, Hou C, Chai J, Li X, Su J, Gao J, Xu H. A new method for predicting the acute toxicity of carbamate pesticides based on the perspective of binding information with carrier protein. Spectrochim Acta A Mol Biomol Spectrosc 2021;264:120188. [PMID: 34358782 DOI: 10.1016/j.saa.2021.120188] [Reference Citation Analysis]
21 Paschoal JDF, Lopes IA, Borges MA, Feijó AL, Simões LD, Segatto NV, Campos VF, Seixas F, Casaril AM, Savegnago L, Lenardão EJ, Collares T. Toxicological evaluation of 3-(4-Chlorophenylselanyl)-1-methyl-1H-indole through the bovine oocyte in vitro maturation model. Toxicol In Vitro 2020;62:104678. [PMID: 31629896 DOI: 10.1016/j.tiv.2019.104678] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
22 Jayasekara PS, Skanchy SK, Kim MT, Kumaran G, Mugabe BE, Woodard LE, Yang J, Zych AJ, Kruhlak NL. Assessing the impact of expert knowledge on ICH M7 (Q)SAR predictions. Is expert review still needed? Regul Toxicol Pharmacol 2021;125:105006. [PMID: 34273441 DOI: 10.1016/j.yrtph.2021.105006] [Reference Citation Analysis]
23 Wu Z, Jiang D, Wang J, Hsieh CY, Cao D, Hou T. Mining Toxicity Information from Large Amounts of Toxicity Data. J Med Chem 2021;64:6924-36. [PMID: 33961429 DOI: 10.1021/acs.jmedchem.1c00421] [Reference Citation Analysis]
24 Yang Z, He J, Lu A, Hou T, Cao D. Application of Negative Design To Design a More Desirable Virtual Screening Library. J Med Chem 2020;63:4411-29. [DOI: 10.1021/acs.jmedchem.9b01476] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 5.5] [Reference Citation Analysis]
25 Shih HP, Zhang X, Aronov AM. Drug discovery effectiveness from the standpoint of therapeutic mechanisms and indications. Nat Rev Drug Discov 2018;17:19-33. [PMID: 29075002 DOI: 10.1038/nrd.2017.194] [Cited by in Crossref: 63] [Cited by in F6Publishing: 49] [Article Influence: 12.6] [Reference Citation Analysis]
26 Li S, Zhang L, Feng H, Meng J, Xie D, Yi L, Arkin IT, Liu H. MutagenPred-GCNNs: A Graph Convolutional Neural Network-Based Classification Model for Mutagenicity Prediction with Data-Driven Molecular Fingerprints. Interdiscip Sci 2021;13:25-33. [PMID: 33506363 DOI: 10.1007/s12539-020-00407-2] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
27 Sadeghi M, Miroliaei M, Fateminasab F, Moradi M. Screening cyclooxygenase-2 inhibitors from Allium sativum L. compounds: in silico approach. J Mol Model 2021;28:24. [PMID: 34970708 DOI: 10.1007/s00894-021-05016-4] [Reference Citation Analysis]
28 Rim KT. In silico prediction of toxicity and its applications for chemicals at work. Toxicol Environ Health Sci 2020;:1-12. [PMID: 32421081 DOI: 10.1007/s13530-020-00056-4] [Cited by in Crossref: 10] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
29 Gordo C, Núñez-Córdoba JM, Mateo R. Root causes of adverse drug events in hospitals and artificial intelligence capabilities for prevention. J Adv Nurs 2021;77:3168-75. [PMID: 33624324 DOI: 10.1111/jan.14779] [Reference Citation Analysis]
30 Bijral RK, Singh I, Manhas J, Sharma V. Exploring Artificial Intelligence in Drug Discovery: A Comprehensive Review. Arch Computat Methods Eng. [DOI: 10.1007/s11831-021-09661-z] [Reference Citation Analysis]
31 Zhang H, Shen C, Liu RZ, Mao J, Liu CT, Mu B. Developing novel in silico prediction models for assessing chemical reproductive toxicity using the naïve Bayes classifier method. J Appl Toxicol 2020;40:1198-209. [PMID: 32207182 DOI: 10.1002/jat.3975] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
32 Madariaga-mazón A, Hernández-alvarado RB, Noriega-colima KO, Osnaya-hernández A, Martinez-mayorga K. Toxicity of secondary metabolites. Physical Sciences Reviews 2019;4. [DOI: 10.1515/psr-2018-0116] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 1.7] [Reference Citation Analysis]
33 Tipping WJ, Lee M, Serrels A, Brunton VG, Hulme AN. Stimulated Raman scattering microscopy: an emerging tool for drug discovery. Chem Soc Rev 2016;45:2075-89. [PMID: 26839248 DOI: 10.1039/c5cs00693g] [Cited by in Crossref: 124] [Cited by in F6Publishing: 31] [Article Influence: 20.7] [Reference Citation Analysis]
34 Kaserer T, Höferl M, Müller K, Elmer S, Ganzera M, Jäger W, Schuster D. In Silico Predictions of Drug - Drug Interactions Caused by CYP1A2, 2C9 and 3A4 Inhibition - a Comparative Study of Virtual Screening Performance. Mol Inf 2015;34:431-57. [DOI: 10.1002/minf.201400192] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 1.4] [Reference Citation Analysis]
35 Liu Y, Tong Z, Shi J, Jia Y, Deng T, Wang Z. Reversion of antibiotic resistance in multidrug-resistant pathogens using non-antibiotic pharmaceutical benzydamine. Commun Biol 2021;4:1328. [PMID: 34824393 DOI: 10.1038/s42003-021-02854-z] [Reference Citation Analysis]
36 Nicolotti O, Benfenati E, Carotti A, Gadaleta D, Gissi A, Mangiatordi GF, Novellino E. REACH and in silico methods: an attractive opportunity for medicinal chemists. Drug Discov Today 2014;19:1757-68. [PMID: 24998783 DOI: 10.1016/j.drudis.2014.06.027] [Cited by in Crossref: 47] [Cited by in F6Publishing: 38] [Article Influence: 5.9] [Reference Citation Analysis]
37 Ai H, Chen W, Zhang L, Huang L, Yin Z, Hu H, Zhao Q, Zhao J, Liu H. Predicting Drug-Induced Liver Injury Using Ensemble Learning Methods and Molecular Fingerprints. Toxicol Sci 2018;165:100-7. [PMID: 29788510 DOI: 10.1093/toxsci/kfy121] [Cited by in Crossref: 26] [Cited by in F6Publishing: 24] [Article Influence: 8.7] [Reference Citation Analysis]
38 Yang ZY, Yang ZJ, Zhao Y, Yin MZ, Lu AP, Chen X, Liu S, Hou TJ, Cao DS. PySmash: Python package and individual executable program for representative substructure generation and application. Brief Bioinform 2021:bbab017. [PMID: 33709154 DOI: 10.1093/bib/bbab017] [Reference Citation Analysis]
39 Asai T, Adachi N, Moriya T, Oki H, Maru T, Kawasaki M, Suzuki K, Chen S, Ishii R, Yonemori K, Igaki S, Yasuda S, Ogasawara S, Senda T, Murata T. Cryo-EM Structure of K+-Bound hERG Channel Complexed with the Blocker Astemizole. Structure 2021;29:203-212.e4. [PMID: 33450182 DOI: 10.1016/j.str.2020.12.007] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
40 O’brien PJ, Edvardsson A. Validation of a Multiparametric, High-Content-Screening Assay for Predictive/Investigative Cytotoxicity: Evidence from Technology Transfer Studies and Literature Review. Chem Res Toxicol 2017;30:804-29. [DOI: 10.1021/acs.chemrestox.6b00403] [Cited by in Crossref: 13] [Cited by in F6Publishing: 11] [Article Influence: 2.6] [Reference Citation Analysis]
41 Landry C, Kim MT, Kruhlak NL, Cross KP, Saiakhov R, Chakravarti S, Stavitskaya L. Transitioning to composite bacterial mutagenicity models in ICH M7 (Q)SAR analyses. Regul Toxicol Pharmacol 2019;109:104488. [PMID: 31586682 DOI: 10.1016/j.yrtph.2019.104488] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 3.3] [Reference Citation Analysis]
42 Chacko S, Samanta S. A novel approach towards design, synthesis and evaluation of some Schiff base analogues of 2-aminopyridine and 2-aminobezothiazole against hepatocellular carcinoma. Biomedicine & Pharmacotherapy 2017;89:162-76. [DOI: 10.1016/j.biopha.2017.01.108] [Cited by in Crossref: 16] [Cited by in F6Publishing: 8] [Article Influence: 3.2] [Reference Citation Analysis]
43 Drwal MN, Banerjee P, Dunkel M, Wettig MR, Preissner R. ProTox: a web server for the in silico prediction of rodent oral toxicity. Nucleic Acids Res 2014;42:W53-8. [PMID: 24838562 DOI: 10.1093/nar/gku401] [Cited by in Crossref: 185] [Cited by in F6Publishing: 152] [Article Influence: 23.1] [Reference Citation Analysis]
44 Jaganathan K, Tayara H, Chong KT. Prediction of Drug-Induced Liver Toxicity Using SVM and Optimal Descriptor Sets. Int J Mol Sci 2021;22:8073. [PMID: 34360838 DOI: 10.3390/ijms22158073] [Reference Citation Analysis]
45 Dal Molin M, Selchow P, Schäfle D, Tschumi A, Ryckmans T, Laage-Witt S, Sander P. Identification of novel scaffolds targeting Mycobacterium tuberculosis. J Mol Med (Berl) 2019;97:1601-13. [PMID: 31728550 DOI: 10.1007/s00109-019-01840-7] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 2.7] [Reference Citation Analysis]
46 Babii O, Afonin S, Schober T, Garmanchuk LV, Ostapchenko LI, Yurchenko V, Zozulya S, Tarasov O, Pishel I, Ulrich AS, Komarov IV. Peptide drugs for photopharmacology: how much of a safety advantage can be gained by photocontrol? Future Drug Discovery 2020;2:FDD28. [DOI: 10.4155/fdd-2019-0033] [Cited by in Crossref: 9] [Cited by in F6Publishing: 3] [Article Influence: 4.5] [Reference Citation Analysis]
47 Guan L, Yang H, Cai Y, Sun L, Di P, Li W, Liu G, Tang Y. ADMET-score - a comprehensive scoring function for evaluation of chemical drug-likeness. Medchemcomm 2019;10:148-57. [PMID: 30774861 DOI: 10.1039/c8md00472b] [Cited by in Crossref: 61] [Cited by in F6Publishing: 25] [Article Influence: 15.3] [Reference Citation Analysis]
48 Zink D, Chuah JKC, Ying JY. Assessing Toxicity with Human Cell-Based In Vitro Methods. Trends Mol Med 2020;26:570-82. [PMID: 32470384 DOI: 10.1016/j.molmed.2020.01.008] [Cited by in Crossref: 13] [Cited by in F6Publishing: 12] [Article Influence: 6.5] [Reference Citation Analysis]
49 Yang H, Sun L, Li W, Liu G, Tang Y. In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts. Front Chem 2018;6:30. [PMID: 29515993 DOI: 10.3389/fchem.2018.00030] [Cited by in Crossref: 53] [Cited by in F6Publishing: 47] [Article Influence: 13.3] [Reference Citation Analysis]
50 Hevener KE. Computational Toxicology Methods in Chemical Library Design and High-Throughput Screening Hit Validation. Methods Mol Biol 2018;1800:275-85. [PMID: 29934898 DOI: 10.1007/978-1-4939-7899-1_13] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
51 Zhang L, Ai H, Chen W, Yin Z, Hu H, Zhu J, Zhao J, Zhao Q, Liu H. CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods. Sci Rep 2017;7:2118. [PMID: 28522849 DOI: 10.1038/s41598-017-02365-0] [Cited by in Crossref: 69] [Cited by in F6Publishing: 61] [Article Influence: 13.8] [Reference Citation Analysis]
52 Robson-tull J. Biophysical screening in fragment-based drug design: a brief overview. Bioscience Horizons: The International Journal of Student Research 2018;11. [DOI: 10.1093/biohorizons/hzy015] [Cited by in Crossref: 9] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
53 Lahiani A, Haham-Geula D, Lankri D, Cornell-Kennon S, Schaefer EM, Tsvelikhovsky D, Lazarovici P. Neurotropic activity and safety of methylene-cycloalkylacetate (MCA) derivative 3-(3-allyl-2-methylenecyclohexyl) propanoic acid. ACS Chem Neurosci 2020;11:2577-89. [PMID: 32667774 DOI: 10.1021/acschemneuro.0c00255] [Reference Citation Analysis]
54 Mangiatordi GF, Alberga D, Altomare CD, Carotti A, Catto M, Cellamare S, Gadaleta D, Lattanzi G, Leonetti F, Pisani L, Stefanachi A, Trisciuzzi D, Nicolotti O. Mind the Gap! A Journey towards Computational Toxicology. Mol Inform 2016;35:294-308. [PMID: 27546034 DOI: 10.1002/minf.201501017] [Cited by in Crossref: 17] [Cited by in F6Publishing: 12] [Article Influence: 2.8] [Reference Citation Analysis]
55 Ahmad F, Mahmood A, Muhmood T. Machine learning-integrated omics for the risk and safety assessment of nanomaterials. Biomater Sci 2021;9:1598-608. [PMID: 33443512 DOI: 10.1039/d0bm01672a] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
56 Felicetti T, Mangiaterra G, Cannalire R, Cedraro N, Pietrella D, Astolfi A, Massari S, Tabarrini O, Manfroni G, Barreca ML, Cecchetti V, Biavasco F, Sabatini S. C-2 phenyl replacements to obtain potent quinoline-based Staphylococcus aureus NorA inhibitors. J Enzyme Inhib Med Chem 2020;35:584-97. [PMID: 31992093 DOI: 10.1080/14756366.2020.1719083] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
57 Lysenko A, Sharma A, Boroevich KA, Tsunoda T. An integrative machine learning approach for prediction of toxicity-related drug safety. Life Sci Alliance 2018;1:e201800098. [PMID: 30515477 DOI: 10.26508/lsa.201800098] [Cited by in Crossref: 15] [Cited by in F6Publishing: 9] [Article Influence: 3.8] [Reference Citation Analysis]
58 Oliveira EA, Brito IA, Lima ML, Romanelli M, Moreira-Filho JT, Neves BJ, Andrade CH, Sartorelli P, Tempone AG, Costa-Silva TA, Lago JHG. Antitrypanosomal Activity of Acetogenins Isolated from the Seeds of Porcelia macrocarpa Is Associated with Alterations in Both Plasma Membrane Electric Potential and Mitochondrial Membrane Potential. J Nat Prod 2019;82:1177-82. [PMID: 31046273 DOI: 10.1021/acs.jnatprod.8b00890] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
59 Yang ZY, Yang ZJ, Lu AP, Hou TJ, Cao DS. Scopy: an integrated negative design python library for desirable HTS/VS database design. Brief Bioinform 2021;22:bbaa194. [PMID: 32892221 DOI: 10.1093/bib/bbaa194] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
60 Giordani CFA, Campanharo S, Wingert NR, Bueno LM, Manoel JW, Costa B, Cattani S, Arbo MD, Garcia SC, Garcia CV, Volpato NM, Schapoval EES, Steppe M. In vitro toxic evaluation of two gliptins and their main impurities of synthesis. BMC Pharmacol Toxicol 2019;20:82. [PMID: 31852534 DOI: 10.1186/s40360-019-0354-2] [Reference Citation Analysis]
61 Wu Q, Cai C, Guo P, Chen M, Wu X, Zhou J, Luo Y, Zou Y, Liu AL, Wang Q, Kuang Z, Fang J. In silico Identification and Mechanism Exploration of Hepatotoxic Ingredients in Traditional Chinese Medicine. Front Pharmacol 2019;10:458. [PMID: 31130860 DOI: 10.3389/fphar.2019.00458] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]