In silico identification of potential HDAC3 inhibitors through machine learning, molecular docking, and molecular dynamics simulations for drug repurposing
Background: Histone Deacetylase 3 (HDAC3) is an epigenetic enzyme that controls cell cycle progression, apoptosis, and gene expression. Overexpression of HDAC3 has been shown to be a potential contributing factor to the development and spread of breast cancer, and it has recently been identified as...
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Elsevier
2025-12-01
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| Series: | Aspects of Molecular Medicine |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949688825000309 |
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| author | Damilare P. Dosunmu Rachael Oluwakamiye Abolade Mujeebat Bashiru Adedoyin John-Joy Owolade Muyiwa Kolawole Samuel Ebunoluwa Omorilewa Boluwatife Ayomadewa Mercy Olatunya Precious O. Aribisala Samuel Aduramurewa Osunnaya Micheal Abimbola Oladosu Ebenezer Ayomide Oni Damilola Samuel Bodun |
| author_facet | Damilare P. Dosunmu Rachael Oluwakamiye Abolade Mujeebat Bashiru Adedoyin John-Joy Owolade Muyiwa Kolawole Samuel Ebunoluwa Omorilewa Boluwatife Ayomadewa Mercy Olatunya Precious O. Aribisala Samuel Aduramurewa Osunnaya Micheal Abimbola Oladosu Ebenezer Ayomide Oni Damilola Samuel Bodun |
| author_sort | Damilare P. Dosunmu |
| collection | DOAJ |
| description | Background: Histone Deacetylase 3 (HDAC3) is an epigenetic enzyme that controls cell cycle progression, apoptosis, and gene expression. Overexpression of HDAC3 has been shown to be a potential contributing factor to the development and spread of breast cancer, and it has recently been identified as a promising target in breast cancer. As a result, repurposing currently approved drugs as novel HDAC3 inhibitors may reduce the labor-intensive and time-consuming process of developing new molecules. Materials and methods: We sourced 4288 compounds from the ZINC15-approved drugs. We then employed both virtual and structure-based screening to identify and repurpose current drugs as selective inhibitors against the HDAC3 target protein. MD simulation was performed to assess the dynamic behavior and stability of the top ligand complexes for 100 ns. Results: This computational screening obtained the top five compounds with docking scores of − 10.96, − 10.32, − 9.83, − 9.83, and − 8.81 kcal/mol, respectively, in comparison with the reference ligand, BG45 (−4.18 kcal/mol), suggesting they may be more potent HDAC3 inhibitors. The MD simulation study of the top hit ligand-protein complex (HDAC3-ZINC000095618609 complex) revealed stable conformational changes. The results of pharmacokinetic and drug-likeness properties of the top-performing compounds reveal their potential to be considered viable HDAC3 inhibitors. Conclusion: This study highlights the potential of drug repurposing as a cost-effective and faster approach to cancer treatment. here we have identified drugs have the potential to be repurposed as HDAC3 inhibitors; however, additional in vitro and in vivo studies are needed to confirm their efficacy. |
| format | Article |
| id | doaj-art-922d3484ed664efabc0f48cd1431c4da |
| institution | Kabale University |
| issn | 2949-6888 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Aspects of Molecular Medicine |
| spelling | doaj-art-922d3484ed664efabc0f48cd1431c4da2025-08-20T03:27:05ZengElsevierAspects of Molecular Medicine2949-68882025-12-01610009210.1016/j.amolm.2025.100092In silico identification of potential HDAC3 inhibitors through machine learning, molecular docking, and molecular dynamics simulations for drug repurposingDamilare P. Dosunmu0Rachael Oluwakamiye Abolade1Mujeebat Bashiru2Adedoyin John-Joy Owolade3Muyiwa Kolawole Samuel4Ebunoluwa Omorilewa Boluwatife5Ayomadewa Mercy Olatunya6Precious O. Aribisala7Samuel Aduramurewa Osunnaya8Micheal Abimbola Oladosu9Ebenezer Ayomide Oni10Damilola Samuel Bodun11ChemoInformatics Academy, Nigeria; Department of Pharmacology and Therapeutics, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria; Corresponding author. Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria.ChemoInformatics Academy, Nigeria; Department of Information Science, University of Arkansas at Little Rock, USA; Department of Pharmaceutical Sciences, University of Arkansas for Medical Sciences, USAChemoInformatics Academy, Nigeria; Department of Information Science, University of Arkansas at Little Rock, USA; Department of Chemistry, University of Arkansas at Little Rock, Little Rock, AR, 72204, USAChemoInformatics Academy, Nigeria; Faculty of Pharmacy, Obafemi Awolowo University, NigeriaChemoInformatics Academy, Nigeria; Department of Biochemistry, College of Medicine, University of Lagos, NigeriaChemoInformatics Academy, Nigeria; Department of Molecular Biology and Biotechnology, Nigerian Institute of Medical Research, Yaba, Lagos, NigeriaDepartment of Chemistry, Ekiti State University, Ado-Ekiti, NigeriaEureka Laboratory, Babcock University, NigeriaChemoInformatics Academy, Nigeria; Department of Molecular Biology and Biotechnology, Nigerian Institute of Medical Research, Yaba, Lagos, NigeriaChemoInformatics Academy, Nigeria; Department of Biochemistry, College of Medicine, University of Lagos, NigeriaDepartment of Biochemistry, Adekunle Ajasin University, Akungba-Akoko, NigeriaChemoInformatics Academy, Nigeria; Covenant University Bioinformatics Research (CUBRe), Covenant University, NigeriaBackground: Histone Deacetylase 3 (HDAC3) is an epigenetic enzyme that controls cell cycle progression, apoptosis, and gene expression. Overexpression of HDAC3 has been shown to be a potential contributing factor to the development and spread of breast cancer, and it has recently been identified as a promising target in breast cancer. As a result, repurposing currently approved drugs as novel HDAC3 inhibitors may reduce the labor-intensive and time-consuming process of developing new molecules. Materials and methods: We sourced 4288 compounds from the ZINC15-approved drugs. We then employed both virtual and structure-based screening to identify and repurpose current drugs as selective inhibitors against the HDAC3 target protein. MD simulation was performed to assess the dynamic behavior and stability of the top ligand complexes for 100 ns. Results: This computational screening obtained the top five compounds with docking scores of − 10.96, − 10.32, − 9.83, − 9.83, and − 8.81 kcal/mol, respectively, in comparison with the reference ligand, BG45 (−4.18 kcal/mol), suggesting they may be more potent HDAC3 inhibitors. The MD simulation study of the top hit ligand-protein complex (HDAC3-ZINC000095618609 complex) revealed stable conformational changes. The results of pharmacokinetic and drug-likeness properties of the top-performing compounds reveal their potential to be considered viable HDAC3 inhibitors. Conclusion: This study highlights the potential of drug repurposing as a cost-effective and faster approach to cancer treatment. here we have identified drugs have the potential to be repurposed as HDAC3 inhibitors; however, additional in vitro and in vivo studies are needed to confirm their efficacy.http://www.sciencedirect.com/science/article/pii/S2949688825000309HDAC3 inhibitorsBreast cancerDockingDrug repurposingMD simulation |
| spellingShingle | Damilare P. Dosunmu Rachael Oluwakamiye Abolade Mujeebat Bashiru Adedoyin John-Joy Owolade Muyiwa Kolawole Samuel Ebunoluwa Omorilewa Boluwatife Ayomadewa Mercy Olatunya Precious O. Aribisala Samuel Aduramurewa Osunnaya Micheal Abimbola Oladosu Ebenezer Ayomide Oni Damilola Samuel Bodun In silico identification of potential HDAC3 inhibitors through machine learning, molecular docking, and molecular dynamics simulations for drug repurposing Aspects of Molecular Medicine HDAC3 inhibitors Breast cancer Docking Drug repurposing MD simulation |
| title | In silico identification of potential HDAC3 inhibitors through machine learning, molecular docking, and molecular dynamics simulations for drug repurposing |
| title_full | In silico identification of potential HDAC3 inhibitors through machine learning, molecular docking, and molecular dynamics simulations for drug repurposing |
| title_fullStr | In silico identification of potential HDAC3 inhibitors through machine learning, molecular docking, and molecular dynamics simulations for drug repurposing |
| title_full_unstemmed | In silico identification of potential HDAC3 inhibitors through machine learning, molecular docking, and molecular dynamics simulations for drug repurposing |
| title_short | In silico identification of potential HDAC3 inhibitors through machine learning, molecular docking, and molecular dynamics simulations for drug repurposing |
| title_sort | in silico identification of potential hdac3 inhibitors through machine learning molecular docking and molecular dynamics simulations for drug repurposing |
| topic | HDAC3 inhibitors Breast cancer Docking Drug repurposing MD simulation |
| url | http://www.sciencedirect.com/science/article/pii/S2949688825000309 |
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