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Research Article

A quantitative structural analysis of AR-42 derivatives as HDAC1 inhibitors for the identification of promising structural contributors

, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 861-883 | Received 13 Sep 2022, Accepted 02 Nov 2022, Published online: 22 Nov 2022

References

  • P. Patel, S.K. Wahan, S. Vishakha, B.D. Kurmi, G.D. Gupta, H. Rajak, and V. Asati, Recent progress in histone deacetylase (HDAC) 1 inhibitors as anticancer agent, preprint (2022). Available at https://www.eurekaselect.com/article/124766.
  • Globacan: Global cancer observatory, https://gco.iarc.fr/today/fact-sheets-cancers. accessed August 30th 2022.
  • R. Hai, D. Yang, F. Zheng, W. Wang, X. Han, A.M. Bode, and X. Luo, The emerging roles of HDACs and their therapeutic implications in cancer, Eur. J. Pharmacol. 931 (2022), pp. 175216. doi:10.1016/j.ejphar.2022.175216.
  • N. Adhikari, T. Jha, and B. Ghosh, Dissecting histone deacetylase 3 in multiple disease conditions: Selective inhibition as a promising therapeutic strategy, J. Med. Chem. 64 (2021), pp. 8827–8869. doi:10.1021/acs.jmedchem.0c01676.
  • E. Seto and M. Yoshida, Erasers of histone acetylation: The histone deacetylase enzymes, Cold Spring Harb. Perspect. Biol. 6 (2014), pp. a018713. doi:10.1101/cshperspect.a018713.
  • S. Banerjee, N. Adhikari, S.A. Amin, and T. Jha, Histone deacetylase 8 (HDAC8) and its inhibitors with selectivity to other isoforms: An overview, Eur. J. Med. Chem. 164 (2019), pp. 214–240. doi:10.1016/j.ejmech.2018.12.039.
  • C.-H.J. Chen, H. Jiang, O. Martin, and A.R. Wilson-Smith, Procedural and clinical outcomes of transcatheter aortic valve replacement in bicuspid aortic valve patients: A systematic review and meta-analysis, Ann. Cardiothorac. Surg. 11 (2022), pp. 351–362. doi:10.21037/acs-2022-bav-22.
  • -L.-L. Cao 1, X. Song, L. Pei, L. Liu, H. Wang, and M. Jia, Histone deacetylase HDAC1 expression correlates with the progression and prognosis of lung cancer: A meta-analysis, Medicine (Baltimore) 96 (2017), pp. e7663. doi:10.1097/MD.0000000000007663.
  • -L.-L. Zhang, Q. Li, D.-S. Zhong, W.-J. Zhang, X.-J. Sun, and Y. Zhu, MCM5 aggravates the HDAC1-mediated malignant progression of lung cancer, Front. Cell. Dev. Biol. 9 (2021), pp. 669132. doi:10.3389/fcell.2021.669132.
  • N. Zhang, H. Zhang, Y. Liu, P. Su, J. Zhang, X. Wang, M. Sun, B. Chen, W. Zhao, L. Wang, H. Wang, M.S. Moran, B.G. Haffty, and Q. Yang, SREBP1, targeted by miR-18a-5p, modulates epithelial-mesenchymal transition in breast cancer via forming a co-repressor complex with Snail and HDAC1/2, Cell Death Differ. 26 (2019), pp. 843–859. doi:10.1038/s41418-018-0158-8.
  • S. Nagarajan, S.V. Rao, J. Sutton, D. Cheeseman, S. Dunn, E.K. Papachristou, J.-E. Gonzalez Prada, D.-L. Couturier, S. Kumar, K. Kishore, C.S. Reddy Chilamakuri, S.-E. Glont, E.A. Goode, C. Brodie, N. Guppy, R. Natrajan, A. Bruna, C. Caldas, A. Russell, R. Siersbæk, K. Yusa, I. Chernukhin, and J.S. Carroll, ARID1A influences HDAC1/BRD4 activity, intrinsic proliferative capacity and breast cancer treatment response, Nat. Genet. 52 (2020), pp. 187–197. doi:10.1038/s41588-019-0541-5.
  • N. Injinari, Z. Amini-Farsani, M. Yadollahi-Farsani, and H. Teimori, Apoptotic effects of valproic acid on miR-34a, miR-520h and HDAC1 gene in breast cancer, Life Sci. 269 (2021), pp. 119027. doi:10.1016/j.lfs.2021.119027.
  • Y. Zhang, D.A. Nalawansha, K.E. Herath, R. Andrade, and M.K.H. Pflum, Differential profiles of HDAC1 substrates and associated proteins in breast cancer cells revealed by trapping, Mol. Omics. 17 (2021), pp. 544–553. doi:10.1039/d0mo00047g.
  • X. Liu, Y. Yu, J. Zhang, C. Lu, L. Wang, P. Liu, and H. Song, HDAC1 silencing in ovarian cancer enhances the chemotherapy response, Cell Physiol. Biochem. 48 (2018), pp. 1505–1518. doi:10.1159/000492260.
  • T. Lv, K. Song, L. Zhang, W. Li, Y. Chen, Y. Diao, Q. Yao, and P. Liu, miRNA-34a decreases ovarian cancer cell proliferation and chemoresistance by targeting HDAC1, Biochem. Cell Biol. 96 (2018), pp. 663–671. doi:10.1139/bcb-2018-0031.
  • W. Xiong, S. Yang, W. Zhang, Y. Chen, and F. Wang, MiR-761 inhibits colorectal cancer cell proliferation and invasion through targeting HDAC1, Pharmazie 74 (2019), pp. 111–114. doi:10.1691/ph.2019.8756.
  • M.W. Shin, S.L. Kim, H.C. Yang, S.K. Yim, S.Y. Seo, S.T. Lee, H.-K. Kim, and S.-W. Kim, The HDAC1 inhibitor CBUD-1001 enhances TRAIL-induced apoptosis in colorectal cancer cells, Anticancer Res. 41 (2021), pp. 4353–4364. doi:10.21873/anticanres.15240.
  • C. Chen, M. Wei, C. Wang, D. Sun, P. Liu, X. Zhong, Q. He, and W. Yu, The histone deacetylase HDAC1 activates HIF1α/VEGFA signal pathway in colorectal cancer, Gene 754 (2020), pp. 144851. doi:10.1016/j.gene.2020.144851.
  • Z. Yu, J. Zeng, H. Liu, T. Wang, Z. Yu, and J. Chen, Role of HDAC1 in the progression of gastric cancer and the correlation with lncRNAs, Oncol. Let. 17 (2019), pp. 3296–3304. doi:10.3892/ol.2019.9962.
  • J. Sun, J. Piao, N. Li, Y. Yang, K.-Y. Kim, and Z. Lin, Valproic acid targets HDAC1/2 and HDAC1/PTEN/Akt signalling to inhibit cell proliferation via the induction of autophagy in gastric cancer, FEBS J. 287 (2020), pp. 2118–2133. doi:10.1111/febs.15122.
  • H.J. Xie, J.H. Noh, J.K. Kim, K.H. Jung, J.W. Eun, H.J. Bae, M.G. Kim, Y.G. Chang, J.Y. Lee, H. Park, and S.W. Nam, HDAC1 inactivation induces mitotic defect and caspase-independent autophagic cell death in liver cancer, PLoS One 7 (2012), pp. e34265. doi:10.1371/journal.pone.0034265.
  • W.-B. Yang, -C.-C. Hsu, T.-I. Hsu, J.-P. Liou, K.-Y. Chang, P.-Y. Chen, -J.-J. Liu, S.-T. Yang, J.-Y. Wang, S.-H. Yeh, R.-M. Chen, W.-C. Chang, and J.-Y. Chuang, Increased activation of HDAC1/2/6 and Sp1 underlies therapeutic resistance and tumor growth in glioblastoma, Neuro. Oncol. 22 (2020), pp. 1439–1451. doi:10.1093/neuonc/noaa103.
  • Y. Huang, J. Chen, C. Lu, J. Han, G. Wang, C. Song, S. Zhu, C. Wang, G. Li, J. Kang, and J. Wang, HDAC1 and Klf4 interplay critically regulates human myeloid leukemia cell proliferation, Cell Death Dis. 5 (2014), pp. e1491. doi:10.1038/cddis.2014.433.
  • P.-C. Pao, D. Patnaik, L.A. Watson, F. Gao, L. Pan, J. Wang, C.N. Adaikkan, J. Penney, H.P. Cam, W.-C. Huang, L. Pantano, A. Lee, A. Nott, T.X. Phan, E. Gjoneska, S. Elmsaouri, S.J. Haggarty, and L.-H. Tsai, HDAC1 modulates OGG1-initiated oxidative DNA damage repair in the aging brain and Alzheimer’s disease, Nat. Commun. 11 (2020), pp. 2484. doi:10.1038/s41467-020-16361-y.
  • X. Xu, X. He, Z. Zhang, Y. Chen, J. Li, S. Ma, Q. Huang, and M. Li, CREB inactivation by HDAC1/PP1γ contributes to dopaminergic neurodegeneration in Parkinson’s disease, J. Neurosci. 42 (2022), pp. 4594–4604. doi:10.1523/JNEUROSCI.1419-21.2022.
  • L. Göschl, T. Preglej, N. Boucheron, V. Saferding, L. Müller, A. Platzer, K. Hirahara, H.-Y. Shih, J. Backlund, P. Matthias, B. Niederreiter, A. Hladik, M. Kugler, G.A. Gualdoni, C. Scheinecker, S. Knapp, C. Seiser, R. Holmdahl, K. Tillmann, R. Plasenzotti, B. Podesser, D. Aletaha, J.S. Smolen, T. Karonitsch, G. Steiner, W. Ellmeier, and M. Bonelli, Histone deacetylase 1 (HDAC1): A key player of T cell-mediated arthritis, J. Autoimmun. 108 (2020), pp. 102379. doi:10.1016/j.jaut.2019.102379.
  • J. Yang, M.-Y. Zhang, Y.-M. Du, X.-L. Ji, and Y.-Q. Qu, Identification and validation of CDKN1A and HDAC1 as senescence-related hub genes in chronic obstructive pulmonary disease, Int. J. Chron. Obstruct. Pulmon. Dis. 17 (2022), pp. 1811–1825. doi:10.2147/COPD.S374684.
  • X.-Q. Wang, H.-M. Bai, S.-T. Li, H. Sun, L.-Z. Min, -B.-B. Tao, J. Zhong, and B. Li, Knockdown of HDAC1 expression suppresses invasion and induces apoptosis in glioma cells, Oncotarget 8 (2017), pp. 48027–48040. doi:10.18632/oncotarget.18227.
  • J. Chen, L. Peng, Z. Zhao, Q. Yang, F. Yin, M. Liu, X. Luo, C. He, and Y. He, HDAC1 potentiates CD4+ T cell activation by inhibiting miR-124 and promoting IRF1 in systemic lupus erythematosus, Cell Immunol. 362 (2021), pp. 104284. doi:10.1016/j.cellimm.2021.104284.
  • S. Yoon and G.H. Eom, HDAC and HDAC inhibitor: From cancer to cardiovascular diseases, Chonnam. Med. J. 52 (2016), pp. 1–11. doi:10.4068/cmj.2016.52.1.1.
  • J.-S. Chen, H.-K. Wang, C.-Y. Hsu, Y.-T. Su, J.-S. Chen, C.-L. Liang, P.C.-H. Hsieh, -C.-C. Wu, and A.-L. Kwan, HDAC1 deregulation promotes neuronal loss and deficit of motor function in stroke pathogenesis, Sci. Rep. 11 (2021), pp. 16354. doi:10.1038/s41598-021-95837-3.
  • J. Verma, V.M. Khedkar, and E.C. Coutinho, 3D-QSAR in drug design - A review, Curr. Top. Med. Chem. 10 (2010), pp. 95–115. doi:10.2174/156802610790232260.
  • G.-F. Yang and X. Huang, Development of quantitative structure-activity relationships and its application in rational drug design, Curr. Pharm. Des. 12 (2006), pp. 4601–4611. doi:10.2174/138161206779010431.
  • S.J.Y. Macalino, V. Gosu, S. Hong, and S. Choi, Role of computer-aided drug design in modern drug discovery, Arch. Pharm. Res. 38 (2015), pp. 1686–1701. doi:10.1007/s12272-015-0640-5.
  • D. Wei, T. Lu, D. Ma, K. Yu, T. Zhang, J. Xiong, W. Wang, Z. Zhang, Q. Fang, and J. Wang, Synergistic activity of imatinib and AR-42 against chronic myeloid leukemia cells mainly through HDAC1 inhibition, Life Sci. 211 (2018), pp. 224–237. doi:10.1016/j.lfs.2018.09.040.
  • Y.-J. Chen, W.-H. Wang, W.-Y. Wu, -C.-C. Hsu, L.-R. Wei, S.-F. Wang, Y.-W. Hsu, -C.-C. Liaw, and W.-C. Tsai, Novel histone deacetylase inhibitor AR-42 exhibits antitumor activity in pancreatic cancer cells by affecting multiple biochemical pathways, PLoS One 12 (2017), pp. e0183368. doi:10.1371/journal.pone.0183368.
  • T.-Y. Lin, J. Fenger, S. Murahari, M.D. Bear, S.K. Kulp, D. Wang, C.-S. Chen, W.C. Kisseberth, and C.A. London, AR-42, a novel HDAC inhibitor, exhibits biologic activity against malignant mast cell lines via down-regulation of constitutively activated Kit, Blood 115 (2010), pp. 4217–4225. doi:10.1182/blood-2009-07-231985.
  • Y. Zhu, T. Yuan, Y. Zhang, J. Shi, L. Bai, X. Duan, R. Tong, and L. Zhong, AR-42: A pan-HDAC inhibitor with antitumor and antiangiogenic activities in esophageal squamous cell carcinoma, Drug Des. Devel. Ther. 13 (2019), pp. 4321–4330. doi:10.2147/DDDT.S211665.
  • D.R. Li, H. Zhang, E. Peek, S. Wang, L. Du, G. Li, and A.I. Chin, Synergy of histone-deacetylase inhibitor AR-42 with cisplatin in bladder cancer, J. Urol. 194 (2015), pp. 547–555. doi:10.1016/j.juro.2015.02.2918.
  • R. Zhou, J. Wu, X. Tang, X. Wei, C. Ju, F. Zhang, J. Sun, D. Shuai, Z. Zhang, Q. Liu, and X.-B. Lv, Histone deacetylase inhibitor AR-42 inhibits breast cancer cell growth and demonstrates a synergistic effect in combination with 5-FU, Oncol. Lett. 16 (2018), pp. 1967–1974. doi:10.3892/ol.2018.8854.
  • A. Mims 1, A.R. Walker, X. Huang, J. Sun, H. Wang, R. Santhanam, A.M. Dorrance, C. Walker, P. Hoellerbauer, S.S. Tarighat, K.K. Chan, R.B. Klisovic, D. Perrotti, M.A. Caligiuri, J.C. Byrd, C.-S. Chen, L.J. Lee, S. Jacob, K. Mrózek, C.D. Bloomfield, W. Blum, R. Garzon, S. Schwind, and G. Marcucci, Increased anti-leukemic activity of decitabine via AR-42-induced upregulation of miR-29b: A novel epigenetic-targeting approach in acute myeloid leukemia, Leukemia 27 (2013), pp. 871–878. doi:10.1038/leu.2012.342.
  • https://clinicaltrials.gov/ accessed October 15th 2022.
  • S.G. Liva, C.C. Coss, J. Wang, W. Blum, R. Klisovic, B. Bhatnagar, K. Walsh, S. Geyer, Q. Zhao, R. Garzon, G. Marcucci, M.A. Phelps, and A.R. Walker, Phase I study of AR-42 and decitabine in acute myeloid leukemia, Leuk. Lymph. 61 (2020), pp. 1484–1492. doi:10.1080/10428194.2020.1719095.
  • D.M. Lucas, L. Alinari, D.A. West, M.E. Davis, R.B. Edwards, A.J. Johnson, K.A. Blum, C.C. Hofmeister, M.A. Freitas, M.R. Parthun, D. Wang, A. Lehman, X. Zhang, D. Jarjoura, S.K. Kulp, C.M. Croce, M.R. Grever, C.-S. Chen, R.A. Baiocchi, and J.C. Byrd, The novel deacetylase inhibitor AR-42 demonstrates pre-clinical activity in B-cell malignancies in vitro and in vivo, PLoS One 5 (2010), pp. e10941. doi:10.1371/journal.pone.0010941.
  • S. Zhang, A. Suvannasankha, C.D. Crean, V.L. White, C.-S. Chen, and S.S. Farag, The novel histone deacetylase inhibitor, AR-42, inhibits gp130/Stat3 pathway and induces apoptosis and cell cycle arrest in multiple myeloma cells, Int. J. Cancer 129 (2011), pp. 204–213. doi:10.1002/ijc.25660.
  • D.W. Sborov, A. Canella, E.M. Hade, X. Mo, S. Khountham, J. Wang, W. Ni, M. Poi, C. Coss, Z. Liu, M.A. Phelps, A. Mortazavi, L. Andritsos, R.A. Baiocchi, B.A. Christian, D.M. Benson, J. Flynn, P. Porcu, J.C. Byrd, F. Pichiorri, and C.C. Hofmeister, A phase 1 trial of the HDAC inhibitor AR-42 in patients with multiple myeloma and T- and B-cell lymphomas, Leuk. Lymph. 58 (2017), pp. 2310–2318. doi:10.1080/10428194.2017.1298751.
  • J. Tng, J. Lim, K.-C. Wu, A.J. Lucke, W. Xu, R.C. Reid, and D.P. Fairlie, Achiral derivatives of hydroxamate AR-42 potently inhibit class I HDAC enzymes and cancer cell proliferation, J. Med. Chem. 63 (2020), pp. 5956–5971. doi:10.1021/acs.jmedchem.0c00230.
  • M.J. Chua, J. Tng, E. Hesping, G.M. Fisher, C.D. Goodman, T. Skinner-Adams, D. Do, A.J. Lucke, R.C. Reid, D.P. Fairlie, and K.T. Andrews, Histone deacetylase inhibitor AR-42 and achiral analogues kill malaria parasites in vitro and in mice, Int. J. Parasitol. Drugs. Drug Resist. 17 (2021), pp. 118–127. doi:10.1016/j.ijpddr.2021.08.006.
  • ChemDraw Ultra 5.0, Cambridge Soft Corporation, USA, 2010; Available at http://www.cambridgesoft.com.
  • Discovery Studio 3.0, Accelrys Inc., San Diego, USA, 2011; Available at http://www.accelrys.com.
  • C.W. Yap, PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints, J. Comput. Chem. 32 (2011), pp. 1466–1474. doi:10.1002/jcc.21707.
  • V. Yadav, S. Banerjee, S.K. Baidya, N. Adhikari, and T. Jha, Applying comparative molecular modelling techniques on diverse hydroxamate-based HDAC2 inhibitors: An attempt to identify promising structural features for potent HDAC2 inhibition, SAR QSAR Environ. Res. 32 (2021), pp. 835–861. doi:10.1080/1062936X.2021.1976831.
  • K. Roy, S. Kar, and R.N. Das, A Primer on QSAR/QSPR Modeling: Fundamental Con- Cepts, Springer, New York, 2015.
  • The simple, user-friendly, and reliable online standalone tools; Available at http://dtclab.webs.com/software-tools.
  • D.A. Freedman, Statistical Models: Theory and Practice. A simple regression equation has on the right-hand side an intercept and an explanatory variable with a slope coefficient. A multiple regression e right-hand side, each with its own slope coefficient, Cambridge University Press, Cambridge, UK, 2009.
  • S. Katoch, S.S. Chauhan, and V. Kumar, A review on genetic algorithm: Past, present, and future, Multimed. Tools Appl. 80 (2021), pp. 8091–8126. doi:10.1007/s11042-020-10139-6.
  • V. Kumar and K. Roy, Development of a simple, interpretable and easily transferable QSAR model for quick screening antiviral databases in search of novel 3C-like protease (3CLpro) enzyme inhibitors against SARS-CoV diseases, SAR QSAR Environ. Res. 31 (2020), pp. 511–526. doi:10.1080/1062936X.2020.1776388.
  • N. Adhikari, S. Banerjee, S.K. Baidya, B. Ghosh, and T. Jha, Ligand-based quantitative structural assessments of SARS-CoV-2 3CL pro inhibitors: An analysis in light of structure-based multi-molecular modeling evidences, J. Mol. Struct. 1251 (2022), pp. 132041. doi:10.1016/j.molstruc.2021.132041.
  • N. Adhikari, S.A. Amin, A. Saha, and T. Jha, Structural exploration for the refinement of anticancer matrix metalloproteinase-2 inhibitor designing approaches through robust validated multi-QSARs, J. Mol. Struct. 1156 (2018), pp. 501–515. doi:10.1016/j.molstruc.2017.12.005.
  • S.K. Baidya, S.A. Amin, S. Banerjee, N. Adhikari, and T. Jha, Structural exploration of arylsulfonamide-based ADAM17 inhibitors through validated comparative multi-QSAR modelling studies, J. Mol. Struct. 1185 (2019), pp. 128–142. doi:10.1016/j.molstruc.2019.02.081.
  • N. Adhikari, S.A. Amin, A. Saha, and T. Jha, Understanding chemico-biological interactions of glutamate MMP-2 inhibitors through rigorous alignment-dependent 3D-QSAR analyses, ChemistrySelect 2 (2017), pp. 7888–7898. doi:10.1002/slct.201701330.
  • S. Banerjee, S.A. Amin, S.K. Baidya, N. Adhikari, and T. Jha, Exploring the structural aspects of ureido-amino acid-based APN inhibitors: A validated comparative multi-QSAR modelling study, SAR QSAR Environ. Res. 31 (2020), pp. 325–345. doi:10.1080/1062936X.2020.1734080.
  • S. Guti, S.K. Baidya, S. Banerjee, N. Adhikari, and T. Jha, A robust classification-dependent multi-molecular modelling study on some biphenyl sulphonamide based MMP-8 inhibitors, SAR QSAR Environ. Res. 32 (2021), pp. 835–861. doi:10.1080/1062936X.2021.1976831.
  • S.A. Amin, N. Adhikari, S. Gayen, and T. Jha, First report on the structural exploration and prediction of new BPTES analogs as glutaminase inhibitors, J. Mol. Struct. 1143 (2017), pp. 49–64. doi:10.1016/j.molstruc.2017.04.020.
  • Golbraikh and A. Tropsha, Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection, Mol. Div. 5 (2000), pp. 231–243. doi:10.1023/A:1021372108686.
  • https://ccsb.scripps.edu/mgltools/ Accessed October 18th 2022.
  • O. Trott and A.J. Olson, AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading, J. Comp. Chem. 31 (2010), pp. 455–461.
  • J. Eberhardt, D. Santos-Martins, A.F. Tillack, and S. Forli, AutoDock vina 1.2.0: New docking methods, expanded force field, and python bindings, J. Chem. Inf. Model. 61 (2021), pp. 3891–3898. doi:10.1021/acs.jcim.1c00203.
  • https://www.rcsb.org/ accessed October 18th 2022.
  • H. Tang, X.S. Wang, X.-P. Huang, B.L. Roth, K.V. Butler, A.P. Kozikowski, M. Jung, and A. Tropsha, Novel inhibitors of human histone deacetylase (HDAC) identified by QSAR modeling of known inhibitors, virtual screening, and experimental validation, J. Chem. Inf. Model. 49 (2009), pp. 461–476. doi:10.1021/ci800366f.
  • J. Shi, G. Zhao, and Y. We, Computational QSAR model combined molecular descriptors and fingerprints to predict HDAC1 inhibitors, Médecine/Sciences 34 (2018), pp. 52–58. doi:10.1051/medsci/201834f110.
  • H. Sirous, G. Campiani, V. Calderone, and S. Brogi, Discovery of novel hit compounds as potential HDAC1 inhibitors: The case of ligand- and structure-based virtual screening, Comp. Biol. Med. 137 (2021), pp. 104808. doi:10.1016/j.compbiomed.2021.104808.
  • T. Abdizadeh, R. Ghodsi, and F. Hadizadeh, 3D-QSAR (CoMFA, CoMSIA) and molecular docking studies on histone deacetylase 1 selective inhibitors, Recent Pat. Anticancer Drug Discov. 12 (2017), pp. 365–383. doi:10.2174/1574892812666170508125927.
  • S. Krishna, A.D. Lakra, N. Shukla, S. Khan, D.P. Mishra, S. Ahmed, and M.I. Siddiqi, Identification of potential histone deacetylase1 (HDAC1) inhibitors using multistep virtual screening approach including SVM model, pharmacophore modeling, molecular docking and biological evaluation, J. Biomol. Struct. Dyn. 38 (2020), pp. 3280–3295. doi:10.1080/07391102.2019.1654925.

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