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Parvatikar PP, Patil S, Khaparkhuntikar K, Patil S, Singh PK, Sahana R, Kulkarni RV, Raghu AV. Artificial intelligence: Machine learning approach for screening large database and drug discovery. Antiviral Res 2023; 220:105740. [PMID: 37935248 DOI: 10.1016/j.antiviral.2023.105740] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 10/17/2023] [Accepted: 10/26/2023] [Indexed: 11/09/2023]
Abstract
Recent research in drug discovery dealing with many faces difficulties, including development of new drugs during disease outbreak and drug resistance due to rapidly accumulating mutations. Virtual screening is the most widely used method in computer aided drug discovery. It has a prominent ability in screening drug targets from large molecular databases. Recently, a number of web servers have developed for quickly screening publicly accessible chemical databases. In a nutshell, deep learning algorithms and artificial neural networks have modernised the field. Several drug discovery processes have used machine learning and deep learning algorithms, including peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modelling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Although there are presently a wide variety of data-driven AI/ML tools available, the majority of these tools have, up to this point, been developed in the context of non-communicable diseases like cancer, and a number of obstacles have prevented the translation of these tools to the discovery of treatments against infectious diseases. In this review various aspects of AI and ML in virtual screening of large databases were discussed. Here, with an emphasis on antivirals as well as other disease, offers a perspective on the advantages, drawbacks, and hazards of AI/ML techniques in the search for innovative treatments.
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Affiliation(s)
- Prachi P Parvatikar
- Department of Biotechnology, Allied Health Science, BLDE (Deemed-to-be University), Vijayapur 586103, Karnataka, India.
| | - Sudha Patil
- Department of Pharmaceutics, BLDEA's SSM College of Pharmacy and Research Centre, Vijayapur 586 103, Karnataka, India
| | - Kedar Khaparkhuntikar
- Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Telangana, 500037, India
| | - Shruti Patil
- Department of Biotechnology, Allied Health Science, BLDE (Deemed-to-be University), Vijayapur 586103, Karnataka, India
| | - Pankaj K Singh
- Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Telangana, 500037, India
| | - R Sahana
- Department of Computer Science and Engineering, RV Institute of Technology and Management, 560076, Bengaluru, India
| | - Raghavendra V Kulkarni
- Department of Biotechnology, Allied Health Science, BLDE (Deemed-to-be University), Vijayapur 586103, Karnataka, India; Department of Pharmaceutics, BLDEA's SSM College of Pharmacy and Research Centre, Vijayapur 586 103, Karnataka, India
| | - Anjanapura V Raghu
- Department of Science and Technology, BLDE (Deemed-to-be University), Vijayapur 586103, Karnataka, India.
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Gu S, Liu H, Liu L, Hou T, Kang Y. Artificial intelligence methods in kinase target profiling: Advances and challenges. Drug Discov Today 2023; 28:103796. [PMID: 37805065 DOI: 10.1016/j.drudis.2023.103796] [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: 07/12/2023] [Revised: 09/29/2023] [Accepted: 10/03/2023] [Indexed: 10/09/2023]
Abstract
Kinases have a crucial role in regulating almost the full range of cellular processes, making them essential targets for therapeutic interventions against various diseases. Accurate kinase-profiling prediction is vital for addressing the selectivity/specificity challenges in kinase drug discovery, which is closely related to lead optimization, drug repurposing, and the understanding of potential drug side effects. In this review, we provide an overview of the latest advancements in machine learning (ML)-based and deep learning (DL)-based quantitative structure-activity relationship (QSAR) models for kinase profiling. We highlight current trends in this rapidly evolving field and discuss the existing challenges and future directions regarding experimental data set construction and model architecture design. Our aim is to offer practical insights and guidance for the development and utilization of these approaches.
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Affiliation(s)
- Shukai Gu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Huanxiang Liu
- Faculty of Applied Science, Macao Polytechnic University, Macao 999078
| | - Liwei Liu
- Advanced Computing and Storage Laboratory, Central Research Institute, 2012 Laboratories, Huawei Technologies Co. Ltd, Nanjing 210000, Jiangsu, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
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Natural Compounds as DPP-4 Inhibitors: 3D-Similarity Search, ADME Toxicity, and Molecular Docking Approaches. Symmetry (Basel) 2022. [DOI: 10.3390/sym14091842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Type 2 diabetes mellitus is one of the most common diseases of the 21st century, caused by a sedentary lifestyle, poor diet, high blood pressure, family history, and obesity. To date, there are no known complete cures for type 2 diabetes. To identify bioactive natural products (NPs) to manage type 2 diabetes, the NPs from the ZINC15 database (ZINC-NPs DB) were screened using a 3D shape similarity search, molecular docking approaches, and ADMETox approaches. Frequently, in silico studies result in asymmetric structures as “hit” molecules. Therefore, the asymmetrical FDA-approved diabetes drugs linagliptin (8-[(3R)-3-aminopiperidin-1-yl]-7-but-2-ynyl-3-methyl-1-[(4-methylquinazolin-2-yl)methyl]purine-2,6-dione), sitagliptin ((3R)-3-amino-1-[3-(trifluoromethyl)-6,8-dihydro-5H-[1,2,4]triazolo[4,3-a]pyrazin-7-yl]-4-(2,4,5-trifluorophenyl)butan-1-one), and alogliptin (2-[[6-[(3R)-3-aminopiperidin-1-yl]-3-methyl-2,4-dioxopyrimidin-1-yl]methyl]benzonitrile) were used as queries to virtually screen the ZINC-NPs DB and detect novel potential dipeptidyl peptidase-4 (DPP-4) inhibitors. The most promising NPs, characterized by the best sets of similarity and ADMETox features, were used during the molecular docking stage. The results highlight that 11 asymmetrical NPs out of 224,205 NPs are potential DPP-4 candidates from natural sources and deserve consideration for further in vitro/in vivo tests.
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Chen Y, Wang ZZ, Hao GF, Song BA. Web support for the more efficient discovery of kinase inhibitors. Drug Discov Today 2022; 27:2216-2225. [DOI: 10.1016/j.drudis.2022.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 02/16/2022] [Accepted: 04/01/2022] [Indexed: 11/24/2022]
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Pacureanu L, Avram S, Crisan L. Comprehensive investigation of selectivity landscape of glycogen synthase kinase-3 inhibitors. J Biomol Struct Dyn 2020; 39:2318-2337. [PMID: 32216607 DOI: 10.1080/07391102.2020.1747544] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Interaction signatures of drug candidates are characteristic to off-target (neutral) and antitarget (negative) effects, inferring reduced efficiency, side-effects and high attrition rate. Today's retroactive scaled-down virtual screening (VS) experiments relying on benchmarking datasets are extensively involved to assess ligand enrichment in the real-world problem. In recent years, unbiased benchmarking sets turned into a tremendous need to assist virtual screening methodologies for emerging drug targets. To date, the benchmarking datasets are quite limited, whereas glycogen synthase kinase-3 (GSK-3) is not included into directories of benchmarking datasets such as DUD-e, MUV, etc. Herein we introduced our in-house algorithm to build an unbiased benchmarking dataset, including highly selective, moderately selective and nonselective inhibitors for a significant therapeutic target - GSK-3, suitable for both ligand-based and structure-based VS approaches. These datasets are unbiased in terms of physico-chemical properties and topological descriptors, as resulted from mean(ROC-AUC) leave-one-out cross-validation (LOO CV). and additional 2 D similarity search. Moreover, we investigated the gradual selectivity dataset by application of multiple 2 D similarity coefficients and distances, 3 D similarity and docking. Besides the resulted links between the enrichment of selective GSK-3 inhibitors and their chemical structures, a database of compounds and their 3 D similarity signatures including cut-off thresholds for enhanced selectivity was generated. 2 D similarity space analysis revealed that selectivity problem cannot be evaluated appropriately with 2 D similarity searching alone. The current analysis provided useful, comprehensive insights, which may facilitate the knowledge-based identification of novel selective GSK-3 inhibitors.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Liliana Pacureanu
- "Coriolan Dragulescu" Institute of Chemistry, Romanian Academy, Timisoara, Romania
| | - Sorin Avram
- "Coriolan Dragulescu" Institute of Chemistry, Romanian Academy, Timisoara, Romania
| | - Luminita Crisan
- "Coriolan Dragulescu" Institute of Chemistry, Romanian Academy, Timisoara, Romania
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Crisan L, Avram S, Kurunczi L, Pacureanu L. Partial Least Squares Discriminant Analysis and 3D Similarity Perspective Applied to Analyze Comprehensively the Selectivity of Glycogen Synthase Kinase 3 Inhibitors. Mol Inform 2020; 39:e1900142. [PMID: 31944600 DOI: 10.1002/minf.201900142] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 12/25/2019] [Indexed: 01/25/2023]
Abstract
The current work was conducted to investigate the effectiveness of two conceptually distinct in silico ligand-based tools: Partial Least Squares Discriminant Analysis (PLS-DA) and 3D similarity, including shape, physico-chemical and electrostatics to classify target-specific pharmacophores with enrichment power for selective GSK-3 inhibitors against the phylogenetically related CDK-2, CDK-4, CDK-5 and PKC. All virtual screens were performed on four data sets of targets matched pairwise, including selective and nonselective inhibitors for GSK-3. The classification method PLS-DA results revealed that all obtained models are statistically robust according to the cross-validation and response permutation tests. Regarding selective GSK-3 inhibitors differentiation in terms of selectivity (Se), specificity (Sp), and accuracy (ACC), the PLS-DA models for CDK-4/GSK-3, and PKC/GSK-3 datasets are highly efficient discriminative. 3D similarity searches for CDK-4/GSK-3, PKC/GSK-3, and CDK-2/GSK-3 datasets using the most selective reference molecules lead to highest enrichments of selective GSK-3 inhibitors. EON yields excellent early and overall enrichments for ET_ST and ET_combo for most selective query for CDK-4/GSK-3. CDK-5/GSK-3 dataset didn't show consistent statistically significant enrichments for 3D similarity virtual screening. The current methodology is reliable and could be used as a powerful tool for the detection of potentially selective molecules targeting GSK-3.
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Affiliation(s)
- Luminita Crisan
- "Coriolan Dragulescu" Institute of Chemistry, Romanian Academy, 24 Mihai Viteazul Ave., 300223, Timisoara, Romania
| | - Sorin Avram
- "Coriolan Dragulescu" Institute of Chemistry, Romanian Academy, 24 Mihai Viteazul Ave., 300223, Timisoara, Romania
| | - Ludovic Kurunczi
- "Coriolan Dragulescu" Institute of Chemistry, Romanian Academy, 24 Mihai Viteazul Ave., 300223, Timisoara, Romania
| | - Liliana Pacureanu
- "Coriolan Dragulescu" Institute of Chemistry, Romanian Academy, 24 Mihai Viteazul Ave., 300223, Timisoara, Romania
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Diarylthiazole and diarylimidazole selective COX-1 inhibitor analysis through pharmacophore modeling, virtual screening, and DFT-based approaches. Struct Chem 2019. [DOI: 10.1007/s11224-019-01414-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Avram S, Curpan R, Bora A, Neanu C, Halip L. Enhancing Molecular Promiscuity Evaluation Through Assay Profiles. Pharm Res 2018; 35:240. [DOI: 10.1007/s11095-018-2523-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Accepted: 10/10/2018] [Indexed: 10/28/2022]
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Avram S, Bora A, Halip L, Curpăn R. Modeling Kinase Inhibition Using Highly Confident Data Sets. J Chem Inf Model 2018; 58:957-967. [DOI: 10.1021/acs.jcim.7b00729] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Sorin Avram
- Department of Computational Chemistry, Institute of Chemistry Timişoara of Romanian Academy, 24 Mihai Viteazu Avenue, 300223-Timişoara, Romania
| | - Alina Bora
- Department of Computational Chemistry, Institute of Chemistry Timişoara of Romanian Academy, 24 Mihai Viteazu Avenue, 300223-Timişoara, Romania
| | - Liliana Halip
- Department of Computational Chemistry, Institute of Chemistry Timişoara of Romanian Academy, 24 Mihai Viteazu Avenue, 300223-Timişoara, Romania
| | - Ramona Curpăn
- Department of Computational Chemistry, Institute of Chemistry Timişoara of Romanian Academy, 24 Mihai Viteazu Avenue, 300223-Timişoara, Romania
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Caunii A, Oprean C, Cristea M, Ivan A, Danciu C, Tatu C, Paunescu V, Marti D, Tzanakakis G, Spandidos DA, Tsatsakis A, Susan R, Soica C, Avram S, Dehelean C. Effects of ursolic and oleanolic on SK‑MEL‑2 melanoma cells: In vitro and in vivo assays. Int J Oncol 2017; 51:1651-1660. [PMID: 29039461 PMCID: PMC5673023 DOI: 10.3892/ijo.2017.4160] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Accepted: 10/02/2017] [Indexed: 12/21/2022] Open
Abstract
Among the triterpenoids, oleanolic acid (OA) and its isomer, ursolic acid (UA) are promising therapeutic candidates, with potential benefits in the management of melanoma. In this study, we aimed to examine the in vitro and in vivo anti‑invasive and anti‑metastatic activity of OA and UA to determine their possible usefulness as chemopreventive or chemotherapeutic agents in melanoma. For the in vitro experiments, the anti‑proliferative activity of the triterpenic compounds on SK‑MEL‑2 melanoma cells was examined. The anti‑invasive potential was assessed by testing the effects of the active compound on vascular cell adhesion molecule (VCAM) and intercellular adhesion molecule (ICAM) adhesion to melanoma cells. Normal and tumor angiogenesis were evaluated in vivo by chicken embryo chorioallantoic membrane (CAM) assay. The two test triterpenoid acids, UA and OA, exerted differential effects in vitro and in vivo on the SK‑MEL‑2 melanoma cells. UA exerted a significant and dose‑dependent anti‑proliferative effect in vitro, compared to OA. The cytotoxic effects in vitro on the melanoma cells were determined by the examining alterations in the cell cycle phases induced by UA that lead to cell arrest in the S phase. Moreover, UA was found to affect SK‑MEL‑2 melanoma cell invasiveness by limiting the cell adhesion capacity to ICAM molecules, but not influencing their adhesion to VCAM molecules. On the whole, in this study, by assessing the effects of the two triterpenoids in vivo, our results revealed that OA had a greater potential to impair the invasive capacity and tumor angiogenesis compared with UA.
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Affiliation(s)
- Angela Caunii
- Faculty of Pharmacy, 'Victor Babeş' University of Medicine and Pharmacy, 300041 Timişoara
| | - Camelia Oprean
- Faculty of Pharmacy, 'Victor Babeş' University of Medicine and Pharmacy, 300041 Timişoara
- 'Pius Brinzeu' Timişoara County Emergency Clinical Hospital, Oncogen Institute, 300723 Timişoara
| | - Mirabela Cristea
- 'Pius Brinzeu' Timişoara County Emergency Clinical Hospital, Oncogen Institute, 300723 Timişoara
| | - Alexandra Ivan
- 'Pius Brinzeu' Timişoara County Emergency Clinical Hospital, Oncogen Institute, 300723 Timişoara
- Faculty of Medicine, 'Victor Babeş' University of Medicine and Pharmacy, 300041 Timişoara
| | - Corina Danciu
- Faculty of Pharmacy, 'Victor Babeş' University of Medicine and Pharmacy, 300041 Timişoara
| | - Calin Tatu
- 'Pius Brinzeu' Timişoara County Emergency Clinical Hospital, Oncogen Institute, 300723 Timişoara
- Faculty of Medicine, 'Victor Babeş' University of Medicine and Pharmacy, 300041 Timişoara
| | - Virgil Paunescu
- 'Pius Brinzeu' Timişoara County Emergency Clinical Hospital, Oncogen Institute, 300723 Timişoara
- Faculty of Medicine, 'Victor Babeş' University of Medicine and Pharmacy, 300041 Timişoara
| | - Daniela Marti
- Faculty of Medicine, Western University Vasile Goldis, Arad 310025, Romania
| | - George Tzanakakis
- Faculty of Medicine, University of Crete, 71003 Heraklion, Crete, Greece
| | | | - Aristides Tsatsakis
- Faculty of Medicine, 'Victor Babeş' University of Medicine and Pharmacy, 300041 Timişoara
- Faculty of Medicine, University of Crete, 71003 Heraklion, Crete, Greece
| | - Razvan Susan
- Faculty of Medicine, 'Victor Babeş' University of Medicine and Pharmacy, 300041 Timişoara
| | - Codruta Soica
- Faculty of Pharmacy, 'Victor Babeş' University of Medicine and Pharmacy, 300041 Timişoara
| | - Stefana Avram
- Faculty of Pharmacy, 'Victor Babeş' University of Medicine and Pharmacy, 300041 Timişoara
| | - Cristina Dehelean
- Faculty of Pharmacy, 'Victor Babeş' University of Medicine and Pharmacy, 300041 Timişoara
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Bora A, Avram S, Ciucanu I, Raica M, Avram S. Predictive Models for Fast and Effective Profiling of Kinase Inhibitors. J Chem Inf Model 2016; 56:895-905. [DOI: 10.1021/acs.jcim.5b00646] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Alina Bora
- Department of Chemistry, Faculty of Chemistry-Biology-Geography, West University of Timisoara, 16 Pestalozzi Str., 300115, Timisoara, Romania
- Department
of Computational Chemistry, Institute of Chemistry, Timisoara of Romanian Academy, 24 Mihai Viteazu Avenue, Timisoara, 300223, Romania
| | - Sorin Avram
- Department
of Computational Chemistry, Institute of Chemistry, Timisoara of Romanian Academy, 24 Mihai Viteazu Avenue, Timisoara, 300223, Romania
| | - Ionel Ciucanu
- Department of Chemistry, Faculty of Chemistry-Biology-Geography, West University of Timisoara, 16 Pestalozzi Str., 300115, Timisoara, Romania
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