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Shoombuatong W, Meewan I, Mookdarsanit L, Schaduangrat N. Stack-HDAC3i: A high-precision identification of HDAC3 inhibitors by exploiting a stacked ensemble-learning framework. Methods 2024; 230:S1046-2023(24)00184-1. [PMID: 39191338 DOI: 10.1016/j.ymeth.2024.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Revised: 08/07/2024] [Accepted: 08/17/2024] [Indexed: 08/29/2024] Open
Abstract
Epigenetics involves reversible modifications in gene expression without altering the genetic code itself. Among these modifications, histone deacetylases (HDACs) play a key role by removing acetyl groups from lysine residues on histones. Overexpression of HDACs is linked to the proliferation and survival of tumor cells. To combat this, HDAC inhibitors (HDACi) are commonly used in cancer treatments. However, pan-HDAC inhibition can lead to numerous side effects. Therefore, isoform-selective HDAC inhibitors, such as HDAC3i, could be advantageous for treating various medical conditions while minimizing off-target effects. To date, computational approaches that use only the SMILES notation without any experimental evidence have become increasingly popular and necessary for the initial discovery of novel potential therapeutic drugs. In this study, we develop an innovative and high-precision stacked-ensemble framework, called Stack-HDAC3i, which can directly identify HDAC3i using only the SMILES notation. Using an up-to-date benchmark dataset, we first employed both molecular descriptors and Mol2Vec embeddings to generate feature representations that cover multi-view information embedded in HDAC3i, such as structural and contextual information. Subsequently, these feature representations were used to train baseline models using nine popular ML algorithms. Finally, the probabilistic features derived from the selected baseline models were fused to construct the final stacked model. Both cross-validation and independent tests showed that Stack-HDAC3i is a high-accuracy prediction model with great generalization ability for identifying HDAC3i. Furthermore, in the independent test, Stack-HDAC3i achieved an accuracy of 0.926 and Matthew's correlation coefficient of 0.850, which are 0.44-6.11% and 0.83-11.90% higher than its constituent baseline models, respectively.
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Affiliation(s)
- Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
| | - Ittipat Meewan
- Center for Advanced Therapeutics, Institute of Molecular Biosciences, Mahidol University, Nakhon Pathom 73170, Thailand
| | - Lawankorn Mookdarsanit
- Business Information System, Faculty of Management Science, Chandrakasem Rajabhat University, Bangkok 10900, Thailand
| | - Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
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Jha T, Jana R, Banerjee S, Baidya SK, Amin SA, Gayen S, Ghosh B, Adhikari N. Exploring different classification-dependent QSAR modelling strategies for HDAC3 inhibitors in search of meaningful structural contributors. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2024; 35:367-389. [PMID: 38757181 DOI: 10.1080/1062936x.2024.2350504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Accepted: 04/28/2024] [Indexed: 05/18/2024]
Abstract
Histone deacetylase 3 (HDAC3), a Zn2+-dependent class I HDACs, contributes to numerous disorders such as neurodegenerative disorders, diabetes, cardiovascular disease, kidney disease and several types of cancers. Therefore, the development of novel and selective HDAC3 inhibitors might be promising to combat such diseases. Here, different classification-based molecular modelling studies such as Bayesian classification, recursive partitioning (RP), SARpy and linear discriminant analysis (LDA) were conducted on a set of HDAC3 inhibitors to pinpoint essential structural requirements contributing to HDAC3 inhibition followed by molecular docking study and molecular dynamics (MD) simulation analyses. The current study revealed the importance of hydroxamate function for Zn2+ chelation as well as hydrogen bonding interaction with Tyr298 residue. The importance of hydroxamate function for higher HDAC3 inhibition was noticed in the case of Bayesian classification, recursive partitioning and SARpy models. Also, the importance of substituted thiazole ring was revealed, whereas the presence of linear alkyl groups with carboxylic acid function, any type of ester function, benzodiazepine moiety and methoxy group in the molecular structure can be detrimental to HDAC3 inhibition. Therefore, this study can aid in the design and discovery of effective novel HDAC3 inhibitors in the future.
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Affiliation(s)
- T Jha
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - R Jana
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - S Banerjee
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - S K Baidya
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - S A Amin
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - S Gayen
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - B Ghosh
- Epigenetic Research Laboratory, Department of Pharmacy, Birla Institute of Technology and Science-Pilani, Hyderabad, India
| | - N Adhikari
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
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Makgoba TB, Kapp E, Egieyeh S, Joubert J. HDAC3 inhibitors: a patent review of their broad-spectrum applications as therapeutic agents. Expert Opin Ther Pat 2024; 34:273-295. [PMID: 38873766 DOI: 10.1080/13543776.2024.2363890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 05/31/2024] [Indexed: 06/15/2024]
Abstract
INTRODUCTION Histone deacetylases (HDACs) are a class of zinc-dependent enzymes. They maintain acetylation homeostasis, with numerous biological functions and are associated with many diseases. HDAC3 strictly requires multi-subunit complex formation for activity. It is associated with the progression of numerous non-communicable diseases. Its widespread involvement in diseases makes it an epigenetic drug target. Preexisting HDAC3 inhibitors have many uses, highlighting the need for continued research in the discovery of HDAC3-selective inhibitors. AREA COVERED This review provides an overview of 24 patents published from 2010 to 2023, focusing on compounds that inhibit the HDAC3 isoenzyme. EXPERT OPINION HDAC3-selective inhibitors - pivotal for pharmacological applications, as single or combination therapies - are gaining traction as a strategy to move away from complications laden pan-HDAC inhibitors. Moreover, there is an unmet need for HDAC3 inhibitors with alternative zinc-binding groups (ZBGs) because some preexisting ZBGs have limitations related to toxicity and side effects. Difficulties in achieving HDAC3 selectivity may be due to isoform selectivity. However, advancements in computer-aided drug design and experimental data of HDAC3 3D co-crystallized models could lead to the discovery of novel HDAC3-selective inhibitors, which bear alternative ZBGs with balanced selectivity for HDAC3 and potency.
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Affiliation(s)
- Thabo Brighton Makgoba
- Pharmaceutical Chemistry, School of Pharmacy, University of the Western Cape, Bellville, South Africa
| | - Erika Kapp
- Pharmaceutical Chemistry, School of Pharmacy, University of the Western Cape, Bellville, South Africa
| | - Samuel Egieyeh
- Pharmaceutical Chemistry, School of Pharmacy, University of the Western Cape, Bellville, South Africa
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Zhang L, Yang Y, Yang Y, Xiao Z. Discovery of Novel Metalloenzyme Inhibitors Based on Property Characterization: Strategy and Application for HDAC1 Inhibitors. Molecules 2024; 29:1096. [PMID: 38474606 DOI: 10.3390/molecules29051096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/17/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024] Open
Abstract
Metalloenzymes are ubiquitously present in the human body and are relevant to a variety of diseases. However, the development of metalloenzyme inhibitors is limited by low specificity and poor drug-likeness associated with metal-binding fragments (MBFs). A generalized drug discovery strategy was established, which is characterized by the property characterization of zinc-dependent metalloenzyme inhibitors (ZnMIs). Fifteen potential Zn2+-binding fragments (ZnBFs) were identified, and a customized pharmacophore feature was defined based on these ZnBFs. The customized feature was set as a required feature and applied to a search for novel inhibitors for histone deacetylase 1 (HDAC1). Ten potential HDAC1 inhibitors were recognized, and one of them (compound 9) was a known potent HDAC1 inhibitor. The results demonstrated the effectiveness of our strategy to identify novel inhibitors for zinc-dependent metalloenzymes.
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Affiliation(s)
- Lu Zhang
- Beijing Key Laboratory of Active Substance Discovery and Druggability Evaluation, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
- Department of Toxicology, Tianjin Centers for Disease Control and Prevention, Tianjin 300011, China
| | - Yajun Yang
- Beijing Key Laboratory of Active Substance Discovery and Druggability Evaluation, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Ying Yang
- Beijing Key Laboratory of Active Substance Discovery and Druggability Evaluation, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Zhiyan Xiao
- Beijing Key Laboratory of Active Substance Discovery and Druggability Evaluation, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
- State Key Laboratory of Digestive Health, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
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Patel P, Shrivastava SK, Sharma P, Kurmi BD, Shirbhate E, Rajak H. Hydroxamic acid derivatives as selective HDAC3 inhibitors: computer-aided drug design strategies. J Biomol Struct Dyn 2024; 42:362-383. [PMID: 36995068 DOI: 10.1080/07391102.2023.2192804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 03/13/2023] [Indexed: 03/31/2023]
Abstract
Histone deacetylases (HDACs) are critical epigenetic drug targets that have gained significant attention in the scientific community for the treatment of cancer. The currently marketed HDAC inhibitors lack selectivity for the various HDAC isoenzymes. Here, we describe our protocol for the discovery of novel potential hydroxamic acid based HDAC3 inhibitors through pharmacophore modeling, virtual screening, docking, molecular dynamics (MD) simulation and toxicity studies. The ten pharmacophore hypotheses were established, and their reliability was validated by different ROC (receiving operator curve) analysis. Among them, the best model (Hypothesis 9 or RRRA) was employed for searching SCHEMBL, ZINC and MolPort database to screen out hit molecules as selective HDAC3 inhibitors, followed by different docking stages. MD simulation (50 ns) and MMGBSA study were performed to study the stability of ligand binding modes and with the help of trajectory analysis, to calculate the ligand-receptor complex RMSD (root-mean-square deviation), RMSF (root-mean-square fluctuation) and H-bond distance, etc. Finally, in-silico toxicity studies were performed on top screened molecules and compared with reference drug SAHA and established structure-activity relationship (SAR). The results indicated that compound 31, with high inhibitory potency and less toxicity (probability value 0.418), is suitable for further experimental analysis.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Preeti Patel
- Medicinal Chemistry Research Laboratory, Department of Pharmacy, Guru Ghasidas University, Bilaspur, Chhattisgarh, India
- Department of Pharmaceutical Chemistry, ISF College of Pharmacy, Moga, Punjab, India
| | - Sushant Kumar Shrivastava
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | - Piyoosh Sharma
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | - Balak Das Kurmi
- Department of Pharmaceutics, ISF College of Pharmacy, Moga, Punjab, India
| | - Ekta Shirbhate
- Medicinal Chemistry Research Laboratory, Department of Pharmacy, Guru Ghasidas University, Bilaspur, Chhattisgarh, India
| | - Harish Rajak
- Medicinal Chemistry Research Laboratory, Department of Pharmacy, Guru Ghasidas University, Bilaspur, Chhattisgarh, India
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Lanka G, Begum D, Banerjee S, Adhikari N, P Y, Ghosh B. Pharmacophore-based virtual screening, 3D QSAR, Docking, ADMET, and MD simulation studies: An in silico perspective for the identification of new potential HDAC3 inhibitors. Comput Biol Med 2023; 166:107481. [PMID: 37741229 DOI: 10.1016/j.compbiomed.2023.107481] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 08/19/2023] [Accepted: 09/15/2023] [Indexed: 09/25/2023]
Abstract
Histone deacetylase 3 (HDAC3) is an epigenetic regulator that involves gene expression, apoptosis, and cell cycle progression, and the overexpression of HDAC3 is accountable for several cancers, neurodegeneracy, and many other diseases. Therefore, HDAC3 emerged as a promising drug target for the novel drug design. Here, we carried out the pharmacophore modeling using 50 benzamide-based HDAC3 selective inhibitors and utilized it for PHASE ligand screening to retrieve the hits with similar pharmacophore features. The dataset inhibitors of best hypotheses used to build the 3D QSAR model and the generated 3D QSAR model resulted in good PLS statistics with a regression coefficient (R2) of 0.89, predictive coefficient (Q2) of 0.88, and Pearson-R factor of 0.94 indicating its excellent predictive ability. The hits retrieved from pharmacophore-based virtual screening were subjected to docking against HDAC3 for the identification of potential inhibitors. A total of 10 hitsM1 to M10 were ranked using their scoring functions and further subject to lead optimization. The Prime MM/GBSA, AutoDock binding free energies, and ADMET studies were implemented for the selection of lead candidates. The four ligand molecules M1, M2, M3, and M4 were identified as potential leads against HDAC3 after lead optimization. The top two leads M1 and M2 were subjected to MD simulations for their stability evaluation with HDAC3. The newly designed leads M11 and M12 were identified as HDAC3 potential inhibitors from MD simulations studies. Therefore, the outcomes of the present study could provide insights into the discovery of new potential HDAC3 inhibitors with improved selectivity and activity against a variety of cancers and neurodegenerative diseases.
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Affiliation(s)
- Goverdhan Lanka
- Epigenetic Research Laboratory, Department of Pharmacy, Birla Institute of Technology and Science-Pilani, Hyderabad Campus, Shamirpet, Hyderabad, 500078, India
| | - Darakhshan Begum
- Epigenetic Research Laboratory, Department of Pharmacy, Birla Institute of Technology and Science-Pilani, Hyderabad Campus, Shamirpet, Hyderabad, 500078, India
| | - Suvankar Banerjee
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, P. O. Box 17020, Jadavpur University, Kolkata, 700032, West Bengal, India
| | - Nilanjan Adhikari
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, P. O. Box 17020, Jadavpur University, Kolkata, 700032, West Bengal, India
| | - Yogeeswari P
- Computer Aided Drug Design Laboratory, Department of Pharmacy, Birla Institute of Technology and Science-Pilani Hyderabad Campus, Shamirpet, Hyderabad, 500078, India
| | - Balaram Ghosh
- Epigenetic Research Laboratory, Department of Pharmacy, Birla Institute of Technology and Science-Pilani, Hyderabad Campus, Shamirpet, Hyderabad, 500078, India.
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Abdallah DI, de Araujo ED, Patel NH, Hasan LS, Moriggl R, Krämer OH, Gunning PT. Medicinal chemistry advances in targeting class I histone deacetylases. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:757-779. [PMID: 37711592 PMCID: PMC10497394 DOI: 10.37349/etat.2023.00166] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 06/22/2023] [Indexed: 09/16/2023] Open
Abstract
Histone deacetylases (HDACs) are a class of zinc (Zn)-dependent metalloenzymes that are responsible for epigenetic modifications. HDACs are largely associated with histone proteins that regulate gene expression at the DNA level. This tight regulation is controlled by acetylation [via histone acetyl transferases (HATs)] and deacetylation (via HDACs) of histone and non-histone proteins that alter the coiling state of DNA, thus impacting gene expression as a downstream effect. For the last two decades, HDACs have been studied extensively and indicated in a range of diseases where HDAC dysregulation has been strongly correlated with disease emergence and progression-most prominently, cancer, neurodegenerative diseases, HIV, and inflammatory diseases. The involvement of HDACs as regulators in these biochemical pathways established them as an attractive therapeutic target. This review summarizes the drug development efforts exerted to create HDAC inhibitors (HDACis), specifically class I HDACs, with a focus on the medicinal chemistry, structural design, and pharmacology aspects of these inhibitors.
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Affiliation(s)
- Diaaeldin I. Abdallah
- Department of Chemical & Physical Sciences, University of Toronto Mississauga, Mississauga, Ontario L5L 1C6, Canada
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 2E8, Canada
| | - Elvin D. de Araujo
- Department of Chemical & Physical Sciences, University of Toronto Mississauga, Mississauga, Ontario L5L 1C6, Canada
| | - Naman H. Patel
- Department of Chemical & Physical Sciences, University of Toronto Mississauga, Mississauga, Ontario L5L 1C6, Canada
| | - Lina S. Hasan
- Department of Chemical & Physical Sciences, University of Toronto Mississauga, Mississauga, Ontario L5L 1C6, Canada
| | - Richard Moriggl
- Institute of Animal Breeding and Genetics, University of Veterinary Medicine, 1210 Vienna, Austria
| | - Oliver H. Krämer
- Department of Toxicology, University of Mainz Medical Center, 55131 Mainz, Germany
| | - Patrick T. Gunning
- Department of Chemical & Physical Sciences, University of Toronto Mississauga, Mississauga, Ontario L5L 1C6, Canada
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 2E8, Canada
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Fakhimahmadi A, Hasanaj I, Hofstetter G, Pogner C, Gorfer M, Wiederstein M, Szepannek N, Bianchini R, Dvorak Z, Jensen SA, Berger M, Jensen-Jarolim E, Hufnagl K, Roth-Walter F. Nutritional Provision of Iron Complexes by the Major Allergen Alt a 1 to Human Immune Cells Decreases Its Presentation. Int J Mol Sci 2023; 24:11934. [PMID: 37569310 PMCID: PMC10418924 DOI: 10.3390/ijms241511934] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/14/2023] [Accepted: 07/20/2023] [Indexed: 08/13/2023] Open
Abstract
Alternaria alternata is a common fungus strongly related with severe allergic asthma, with 80% of affected individuals being sensitized solely to its major allergen Alt a 1. Here, we assessed the function of Alt a 1 as an innate defense protein binding to micronutrients, such as iron-quercetin complexes (FeQ2), and its impact on antigen presentation in vitro. Binding of Alt a 1 to FeQ2 was determined in docking calculations. Recombinant Alt a 1 was generated, and binding ability, as well as secondary and quaternary structure, assessed by UV-VIS, CD, and DLS spectroscopy. Proteolytic functions were determined by casein and gelatine zymography. Uptake of empty apo- or ligand-filled holoAlt a 1 were assessed in human monocytic THP1 cells under the presence of dynamin and clathrin-inhibitors, activation of the Arylhydrocarbon receptor (AhR) using the human reporter cellline AZ-AHR. Human PBMCs were stimulated and assessed for phenotypic changes in monocytes by flow cytometry. Alt a 1 bound strongly to FeQ2 as a tetramer with calculated Kd values reaching pico-molar levels and surpassing affinities to quercetin alone by a factor of 5000 for the tetramer. apoAlt a 1 but not holoAlta 1 showed low enzymatic activity against casein as a hexamer and gelatin as a trimer. Uptake of apo- and holo-Alt a 1 occurred partly clathrin-dependent, with apoAlt a 1 decreasing labile iron in THP1 cells and holoAlt a 1 facilitating quercetin-dependent AhR activation. In human PBMCs uptake of holoAlt a 1 but not apoAlt a 1 significantly decreased the surface expression of the costimulatory CD86, but also of HLADR, thereby reducing effective antigen presentation. We show here for the first time that the presence of nutritional iron complexes, such as FeQ2, significantly alters the function of Alt a 1 and dampens the human immune response, thereby supporting the notion that Alt a 1 only becomes immunogenic under nutritional deprivation.
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Affiliation(s)
- Aila Fakhimahmadi
- Comparative Medicine, The Interuniversity Messerli Research Institute, 1210 Vienna, Austria; (A.F.); (I.H.); (G.H.); (N.S.); (R.B.); (S.A.J.); (M.B.); (E.J.-J.); (K.H.)
- Institute of Pathophysiology and Allergy Research, Center of Pathophysiology, Infectiology and Immunology, Medical University of Vienna, 1090 Vienna, Austria
| | - Ilir Hasanaj
- Comparative Medicine, The Interuniversity Messerli Research Institute, 1210 Vienna, Austria; (A.F.); (I.H.); (G.H.); (N.S.); (R.B.); (S.A.J.); (M.B.); (E.J.-J.); (K.H.)
| | - Gerlinde Hofstetter
- Comparative Medicine, The Interuniversity Messerli Research Institute, 1210 Vienna, Austria; (A.F.); (I.H.); (G.H.); (N.S.); (R.B.); (S.A.J.); (M.B.); (E.J.-J.); (K.H.)
- Institute of Pathophysiology and Allergy Research, Center of Pathophysiology, Infectiology and Immunology, Medical University of Vienna, 1090 Vienna, Austria
| | - Clara Pogner
- Bioresources Unit, Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, 3430 Tulln, Austria; (C.P.); (M.G.)
| | - Markus Gorfer
- Bioresources Unit, Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, 3430 Tulln, Austria; (C.P.); (M.G.)
| | - Markus Wiederstein
- Department of Biosciences, University of Salzburg, 5020 Salzburg, Austria;
| | - Nathalie Szepannek
- Comparative Medicine, The Interuniversity Messerli Research Institute, 1210 Vienna, Austria; (A.F.); (I.H.); (G.H.); (N.S.); (R.B.); (S.A.J.); (M.B.); (E.J.-J.); (K.H.)
| | - Rodolfo Bianchini
- Comparative Medicine, The Interuniversity Messerli Research Institute, 1210 Vienna, Austria; (A.F.); (I.H.); (G.H.); (N.S.); (R.B.); (S.A.J.); (M.B.); (E.J.-J.); (K.H.)
| | - Zdenek Dvorak
- Department of Cell Biology and Genetics, Faculty of Science, Palacky University, 779 00 Olomouc, Czech Republic;
| | - Sebastian A. Jensen
- Comparative Medicine, The Interuniversity Messerli Research Institute, 1210 Vienna, Austria; (A.F.); (I.H.); (G.H.); (N.S.); (R.B.); (S.A.J.); (M.B.); (E.J.-J.); (K.H.)
| | - Markus Berger
- Comparative Medicine, The Interuniversity Messerli Research Institute, 1210 Vienna, Austria; (A.F.); (I.H.); (G.H.); (N.S.); (R.B.); (S.A.J.); (M.B.); (E.J.-J.); (K.H.)
| | - Erika Jensen-Jarolim
- Comparative Medicine, The Interuniversity Messerli Research Institute, 1210 Vienna, Austria; (A.F.); (I.H.); (G.H.); (N.S.); (R.B.); (S.A.J.); (M.B.); (E.J.-J.); (K.H.)
- Institute of Pathophysiology and Allergy Research, Center of Pathophysiology, Infectiology and Immunology, Medical University of Vienna, 1090 Vienna, Austria
| | - Karin Hufnagl
- Comparative Medicine, The Interuniversity Messerli Research Institute, 1210 Vienna, Austria; (A.F.); (I.H.); (G.H.); (N.S.); (R.B.); (S.A.J.); (M.B.); (E.J.-J.); (K.H.)
- Institute of Pathophysiology and Allergy Research, Center of Pathophysiology, Infectiology and Immunology, Medical University of Vienna, 1090 Vienna, Austria
| | - Franziska Roth-Walter
- Comparative Medicine, The Interuniversity Messerli Research Institute, 1210 Vienna, Austria; (A.F.); (I.H.); (G.H.); (N.S.); (R.B.); (S.A.J.); (M.B.); (E.J.-J.); (K.H.)
- Institute of Pathophysiology and Allergy Research, Center of Pathophysiology, Infectiology and Immunology, Medical University of Vienna, 1090 Vienna, Austria
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Kumbhar N, Nimal S, Barale S, Kamble S, Bavi R, Sonawane K, Gacche R. Identification of novel leads as potent inhibitors of HDAC3 using ligand-based pharmacophore modeling and MD simulation. Sci Rep 2022; 12:1712. [PMID: 35110603 PMCID: PMC8810932 DOI: 10.1038/s41598-022-05698-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 01/03/2022] [Indexed: 02/08/2023] Open
Abstract
In the landscape of epigenetic regulation, histone deacetylase 3 (HDAC3) has emerged as a prominent therapeutic target for the design and development of candidate drugs against various types of cancers and other human disorders. Herein, we have performed ligand-based pharmacophore modeling, virtual screening, molecular docking, and MD simulations to design potent and selective inhibitors against HDAC3. The predicted best pharmacophore model 'Hypo 1' showed excellent correlation (R2 = 0.994), lowest RMSD (0.373), lowest total cost value (102.519), and highest cost difference (124.08). Hypo 1 consists of four salient pharmacophore features viz. one hydrogen bond acceptor (HBA), one ring aromatic (RA), and two hydrophobic (HYP). Hypo 1 was validated by Fischer's randomization with a 95% of confidence level and the external test set of 60 compounds with a good correlation coefficient (R2 = 0.970). The virtual screening of chemical databases, drug-like properties calculations followed by molecular docking resulted in identifying 22 representative hit compounds. Performed 50 ns of MD simulations on top three hits were retained the salient π-stacking, Zn2+ coordination, hydrogen bonding, and hydrophobic interactions with catalytic residues from the active site pocket of HDAC3. Total binding energy calculated by MM-PBSA showed that the Hit 1 and Hit 2 formed stable complexes with HDAC3 as compared to reference TSA. Further, the PLIP analysis showed a close resemblance between the salient pharmacophore features of Hypo 1 and the presence of molecular interactions in co-crystallized FDA-approved drugs. We conclude that the screened hit compounds may act as potent inhibitors of HDAC3 and further preclinical and clinical studies may pave the way for developing them as effective therapeutic agents for the treatment of different cancers and neurodegenerative disorders.
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Affiliation(s)
- Navanath Kumbhar
- Department of Biotechnology, Savitribai Phule Pune University Pune, Pune, Maharashtra (MS), 411007, India
| | - Snehal Nimal
- Department of Biotechnology, Savitribai Phule Pune University Pune, Pune, Maharashtra (MS), 411007, India
| | - Sagar Barale
- Department of Microbiology, Shivaji University, Kolhapur, Maharashtra (MS), 416004, India
| | - Subodh Kamble
- Structural Bioinformatics Unit, Department of Biochemistry, Shivaji University, Kolhapur, Maharashtra (MS), 416004, India
| | - Rohit Bavi
- School of Chemical Science, Punyashlok Ahilyadevi Holkar Solapur University, Solapur, Maharashtra (MS), 413255, India
| | - Kailas Sonawane
- Department of Microbiology, Shivaji University, Kolhapur, Maharashtra (MS), 416004, India
- Structural Bioinformatics Unit, Department of Biochemistry, Shivaji University, Kolhapur, Maharashtra (MS), 416004, India
| | - Rajesh Gacche
- Department of Biotechnology, Savitribai Phule Pune University Pune, Pune, Maharashtra (MS), 411007, India.
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Yu C, Hu J, Luyten W, Sun D, Jiang T. Identification of novel topoisomerase II alpha inhibitors by virtual screening, molecular docking, and bioassay. Chem Biol Drug Des 2021; 99:92-102. [PMID: 34310071 DOI: 10.1111/cbdd.13927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 06/21/2021] [Accepted: 07/03/2021] [Indexed: 11/27/2022]
Abstract
Breast cancer is one of the most common tumors, and its treatment still leaves room for improvement. Topoisomerase II alpha is a potential target for the treatment of human diseases such as breast cancer. In this article, we attempted to discover a novel anticancer drug. We have used the topoisomerase II alpha protein-Homo sapiens (Human) to hierarchically screen the Maybridge database. Based on their docking score, the top hit compounds have been assayed for inhibition in a topoisomerase II pBR322 DNA relaxation assay in vitro. Candidate compound 6 (CP6) was found to have the best inhibitory effect for topoisomerase II among the 20 tested compounds. In addition, CP6 had potent cytotoxicity against eight tested tumor cell lines. At the same time, CP6 was shown to have potential anti-multidrug resistance capabilities. This study identifies CP6, which can contribute to the development of new topoisomerase II inhibitors as anticancer agents.
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Affiliation(s)
- Che Yu
- Department of Nephrology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Jiabao Hu
- School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Walter Luyten
- Animal Physiology and Neurobiology Section, Department of Biology, KU Leuven, Leuven, Belgium
| | - Dan Sun
- Animal Physiology and Neurobiology Section, Department of Biology, KU Leuven, Leuven, Belgium.,College of Life Sciences, Nankai University, Tianjin, China
| | - Tao Jiang
- Department of Anesthesiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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11
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Prospect of Anterior Gradient 2 homodimer inhibition via repurposing FDA-approved drugs using structure-based virtual screening. Mol Divers 2021; 26:1399-1409. [PMID: 34181147 DOI: 10.1007/s11030-021-10263-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 06/21/2021] [Indexed: 10/21/2022]
Abstract
Anterior Gradient 2 (AGR2) has recently been reported as a tumor biomarker in various cancers, i.e., breast, prostate and lung cancer. Predominantly, AGR2 exists as a homodimer via a dimerization domain (E60-K64); after it is self-dimerized, it helps FGF2 and VEGF to homo-dimerize and promotes the angiogenesis and the invasion of vascular endothelial cells and fibroblasts. Up till now, no small molecule has been discovered to inhibit the AGR2-AGR2 homodimer. Therefore, the present study was performed to prepare a validated 3D structure of AGR2 by homology modeling and discover a small molecule by screening the FDA-approved drugs library on AGR2 homodimer as a target protein. Thirteen different homology models of AGR2 were generated based on different templates which were narrowed down to 5 quality models sorted by their overall Z-scores. The top homology model based on PDB ID = 3PH9 was selected having the best Z-score and was further assessed by Verify-3D, ERRAT and RAMPAGE analysis. Structure-based virtual screening narrowed down the large library of FDA-approved drugs to ten potential AGR2-AGR2 homodimer inhibitors having FRED score lower than - 7.8 kcal/mol in which the top 5 drugs' binding stability was counter-validated by molecular dynamic simulation. To sum up, the present study prepared a validated 3D structure of AGR2 and, for the first time reported the discovery of 5 FDA-approved drugs to inhibit AGR2-AGR2 homodimer by using structure-based virtual screening. Moreover, the binding of the top 5 hits with AGR2 was also validated by molecular dynamic simulation. A validated 3D structure of Anterior Gradient 2 (AGR2) was prepared by homology modeling, which was used in virtual screening of FDA-approved drugs library for the discovery of prospective inhibitors of AGR2-AGR2 homodimer.
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12
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Qin T, Zhu Z, Wang XS, Xia J, Wu S. Computational representations of protein-ligand interfaces for structure-based virtual screening. Expert Opin Drug Discov 2021; 16:1175-1192. [PMID: 34011222 DOI: 10.1080/17460441.2021.1929921] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Introduction: Structure-based virtual screening (SBVS) is an essential strategy for hit identification. SBVS primarily uses molecular docking, which exploits the protein-ligand binding mode and associated affinity score for compound ranking. Previous studies have shown that computational representation of protein-ligand interfaces and the later establishment of machine learning models are efficacious in improving the accuracy of SBVS.Areas covered: The authors review the computational methods for representing protein-ligand interfaces, which include the traditional ones that use deliberately designed fingerprints and descriptors and the more recent methods that automatically extract features with deep learning. The effects of these methods on the performance of machine learning models are briefly discussed. Additionally, case studies that applied various computational representations to machine learning are cited with remarks.Expert opinion: It has become a trend to extract binding features automatically by deep learning, which uses a completely end-to-end representation. However, there is still plenty of scope for improvement . The interpretability of deep-learning models, the organization of data management, the quantity and quality of available data, and the optimization of hyperparameters could impact the accuracy of feature extraction. In addition, other important structural factors such as water molecules and protein flexibility should be considered.
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Affiliation(s)
- Tong Qin
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zihao Zhu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiang Simon Wang
- Artificial Intelligence and Drug Discovery Core Laboratory for District of Columbia Center for AIDS Research (DC CFAR), Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, U.S.A
| | - Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Song Wu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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13
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Song Y, Park SY, Wu Z, Liu KH, Seo YH. Hybrid inhibitors of DNA and HDACs remarkably enhance cytotoxicity in leukaemia cells. J Enzyme Inhib Med Chem 2021; 35:1069-1079. [PMID: 32314611 PMCID: PMC7191901 DOI: 10.1080/14756366.2020.1754812] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Chlorambucil is a nitrogen mustard-based DNA alkylating drug, which is widely used as a front-line treatment of chronic lymphocytic leukaemia (CLL). Despite its widespread application and success for the initial treatment of leukaemia, a majority of patients eventually develop acquired resistance to chlorambucil. In this regard, we have designed and synthesised a novel hybrid molecule, chloram-HDi that simultaneously impairs DNA and HDAC enzymes. Chloram-HDi efficiently inhibits the proliferation of HL-60 and U937 leukaemia cells with GI50 values of 1.24 µM and 1.75 µM, whereas chlorambucil exhibits GI50 values of 21.1 µM and 37.7 µM against HL-60 and U937 leukaemia cells, respectively. The mechanism behind its remarkably enhanced cytotoxicity is that chloram-HDi not only causes a significant DNA damage of leukaemia cells but also downregulates DNA repair protein, Rad52, resulting in the escalation of its DNA-damaging effect. Furthermore, chloram-HDi inhibits HDAC enzymes to induce the acetylation of α-tubulin and histone H3.
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Affiliation(s)
- Yoojin Song
- College of Pharmacy, Keimyung University, Daegu, Republic of Korea
| | - Sun You Park
- College of Pharmacy, Keimyung University, Daegu, Republic of Korea
| | - Zhexue Wu
- BK21 Plus KNU Multi-Omics based Creative Drug Research Team, College of Pharmacy and Research Institute of Pharmaceutical Sciences, Kyungpook National University, Daegu, South Korea
| | - Kwang-Hyeon Liu
- BK21 Plus KNU Multi-Omics based Creative Drug Research Team, College of Pharmacy and Research Institute of Pharmaceutical Sciences, Kyungpook National University, Daegu, South Korea
| | - Young Ho Seo
- College of Pharmacy, Keimyung University, Daegu, Republic of Korea
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14
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Li S, Ding Y, Chen M, Chen Y, Kirchmair J, Zhu Z, Wu S, Xia J. HDAC3i-Finder: A Machine Learning-based Computational Tool to Screen for HDAC3 Inhibitors. Mol Inform 2020; 40:e2000105. [PMID: 33067876 DOI: 10.1002/minf.202000105] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 10/16/2020] [Indexed: 11/10/2022]
Abstract
Histone deacetylase 3 (HDAC3) is a potential drug target for treatment of human diseases such as cancer, chronic inflammation, neurodegenerative diseases and diabetes. Machine learning (ML) as an essential cheminformatics approach has been widely used for QSAR modeling. However, none of them has been applied to HDAC3. To this end, we carefully compiled a set of 1098 compounds from the ChEMBL database that have been assayed against HDAC3 and calculated three different sets of molecular features for each compound, i. e. two-dimensional Mordred descriptors, MACCS keys (166 bits) and Morgan2 fingerprints (1024 bits). Five ML classifiers, i. e. k-Nearest Neighbour (KNN), Support Vector Machine (SVM), Random forest (RF), eXtreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) were trained on each feature set and optimized for classification. A total of 15 models were generated and carefully compared, among which the best-performing one was the XGBoost model based on the Morgan2 fingerprints, i. e. XGBoost_morgan2. Evaluated on a well-curated benchmarking set named MUBD-HDAC3, this model achieved a high early ROC enrichment (ROCE0.5 %: 41.02). A further retrospective screening of an annotated chemical library in PubChem demonstrated that the best model could identify 8 novel-scaffold HDAC3 inhibitors while assaying only 1 % of the compounds. To make this model accessible for the scientific community, we developed a python GUI application named HDAC3i-Finder to facilitate prospective screening for HDAC3 inhibitors. The source code of HDAC3i-Finder is available at https://github.com/jwxia2014/HDAC3i-Finder.
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Affiliation(s)
- Shan Li
- College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
| | - Yu Ding
- College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
| | - Miaomiao Chen
- College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
| | - Ya Chen
- Center for Bioinformatics (ZBH), Department of Computer Science, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, Hamburg, Germany
| | - Johannes Kirchmair
- Center for Bioinformatics (ZBH), Department of Computer Science, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, Hamburg, Germany.,Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Vienna, Austria
| | - Zihao Zhu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Song Wu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
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15
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Capoci IRG, Sakita KM, Faria DR, Rodrigues-Vendramini FAV, Arita GS, de Oliveira AG, Felipe MS, Maigret B, Bonfim-Mendonça PDS, Kioshima ES, Svidzinski TIE. Two New 1,3,4-Oxadiazoles With Effective Antifungal Activity Against Candida albicans. Front Microbiol 2019; 10:2130. [PMID: 31572335 PMCID: PMC6751290 DOI: 10.3389/fmicb.2019.02130] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 08/29/2019] [Indexed: 12/29/2022] Open
Abstract
Candida infections have become a serious public health problem with high mortality rates, especially in immunocompromised patients, since Candida albicans is the major opportunistic pathogen responsible for systemic or invasive candidiasis. Commercially available antifungal agents are restricted and fungal resistance to such drugs has increased; therefore, the development of a more specific antifungal agent is necessary. Using assays for antifungal activity, here we report that two new compounds of 1,3,4-oxadiazoles class (LMM5 and LMM11), which were discovered by in silico methodologies as possible thioredoxin reductase inhibitors, were effective against C. albicans. Both compounds had in vitro antifungal activity with MIC 32 μg/ml. Cytotoxicity in vitro demonstrated that LMM5 and LMM11 were non-toxic in the cell lines evaluated. The kinetic of the time-kill curve suggested a fungistatic profile and showed an inhibitory effect of LMM5 and LMM11 in 12 h that remained for 24 and 36 h, which is better than fluconazole. In the murine systemic candidiasis model by C. albicans, the two compounds significantly reduced the renal and spleen fungal burden. According to the SEM and TEM images, we hypothesize that the mechanism of action of LMM5 and LMM11 is directly related to the inhibition of the enzyme thioredoxin reductase and internally affect the fungal cell. In view of all in vitro and in vivo results, LMM5 and LMM11 are effective therapeutic candidates for the development of new antifungal drugs addressing the treatment of human infections caused by C. albicans.
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Affiliation(s)
| | - Karina Mayumi Sakita
- Department of Clinical Analysis and Biomedicine, The State University of Maringá, Maringá, Brazil
| | - Daniella Renata Faria
- Department of Clinical Analysis and Biomedicine, The State University of Maringá, Maringá, Brazil
| | | | - Glaucia Sayuri Arita
- Department of Clinical Analysis and Biomedicine, The State University of Maringá, Maringá, Brazil
| | | | - Maria Sueli Felipe
- Department of Cellular Biology, The University of Brasília, Brasília, Brazil
| | | | | | - Erika Seki Kioshima
- Department of Clinical Analysis and Biomedicine, The State University of Maringá, Maringá, Brazil
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Cheminformatics techniques in antimalarial drug discovery and development from natural products 1: basic concepts. PHYSICAL SCIENCES REVIEWS 2019. [DOI: 10.1515/psr-2018-0130] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Abstract
A large number of natural products, especially those used in ethnomedicine of malaria, have shown varying in vitro antiplasmodial activities. Facilitating antimalarial drug development from this wealth of natural products is an imperative and laudable mission to pursue. However, limited manpower, high research cost coupled with high failure rate during preclinical and clinical studies might militate against the pursuit of this mission. These limitations may be overcome with cheminformatic techniques. Cheminformatics involves the organization, integration, curation, standardization, simulation, mining and transformation of pharmacology data (compounds and bioactivity) into knowledge that can drive rational and viable drug development decisions. This chapter will review the application of cheminformatics techniques (including molecular diversity analysis, quantitative-structure activity/property relationships and Machine learning) to natural products with in vitro and in vivo antiplasmodial activities in order to facilitate their development into antimalarial drug candidates and design of new potential antimalarial compounds.
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17
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Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns. Int J Mol Sci 2019; 20:ijms20092060. [PMID: 31027337 PMCID: PMC6540224 DOI: 10.3390/ijms20092060] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 04/08/2019] [Accepted: 04/22/2019] [Indexed: 01/02/2023] Open
Abstract
The study proposes a novel consensus strategy based on linear combinations of different docking scores to be used in the evaluation of virtual screening campaigns. The consensus models are generated by applying the recently proposed Enrichment Factor Optimization (EFO) method, which develops the linear equations by exhaustively combining the available docking scores and by optimizing the resulting enrichment factors. The performances of such a consensus strategy were evaluated by simulating the entire Directory of Useful Decoys (DUD datasets). In detail, the poses were initially generated by the PLANTS docking program and then rescored by ReScore+ with and without the minimization of the complexes. The so calculated scores were then used to generate the mentioned consensus models including two or three different scoring functions. The reliability of the generated models was assessed by a per target validation as performed by default by the EFO approach. The encouraging performances of the here proposed consensus strategy are emphasized by the average increase of the 17% in the Top 1% enrichment factor (EF) values when comparing the single best score with the linear combination of three scores. Specifically, kinases offer a truly convincing demonstration of the efficacy of the here proposed consensus strategy since their Top 1% EF average ranges from 6.4 when using the single best performing primary score to 23.5 when linearly combining scoring functions. The beneficial effects of this consensus approach are clearly noticeable even when considering the entire DUD datasets as evidenced by the area under the curve (AUC) averages revealing a 14% increase when combining three scores. The reached AUC values compare very well with those reported in literature by an extended set of recent benchmarking studies and the three-variable models afford the highest AUC average.
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