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Feng C, Qiao C, Ji W, Pang H, Wang L, Feng Q, Ge Y, Rui M. In silico screening and in vivo experimental validation of 15-PGDH inhibitors from traditional Chinese medicine promoting liver regeneration. Int J Biol Macromol 2024:133263. [PMID: 38901515 DOI: 10.1016/j.ijbiomac.2024.133263] [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: 04/16/2024] [Revised: 05/25/2024] [Accepted: 06/17/2024] [Indexed: 06/22/2024]
Abstract
The enzyme 15-hydroxyprostaglandin dehydrogenase (15-PGDH), which acts as a negative regulator of prostaglandin E2 (PGE2) levels and activity, represents a promising pharmacological target for promoting liver regeneration. In this study, we collected data on 15-PGDH homologous family proteins, their inhibitors, and traditional Chinese medicine (TCM) compounds. Leveraging machine learning and molecular docking techniques, we constructed a prediction model for virtual screening of 15-PGDH inhibitors from TCM compound library and successfully screened genistein as a potential 15-PGDH inhibitor. Through further validation, it was discovered that genistein considerably enhances liver regeneration by inhibiting 15-PGDH, resulting in a significant increase in the PGE2 level. Genistein's effectiveness suggests its potential as a novel therapeutic agent for liver diseases, highlighting this study's contribution to expanding the clinical applications of TCM.
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Affiliation(s)
- Chunlai Feng
- Department of Pharmaceutics, School of Pharmacy, Jiangsu University, Zhenjiang, PR China
| | - Chunxue Qiao
- Department of Pharmaceutics, School of Pharmacy, Jiangsu University, Zhenjiang, PR China
| | - Wei Ji
- Department of Pharmaceutics, School of Pharmacy, Jiangsu University, Zhenjiang, PR China
| | - Hui Pang
- Department of Pharmaceutics, School of Pharmacy, Jiangsu University, Zhenjiang, PR China
| | - Li Wang
- Department of Pharmaceutics, School of Pharmacy, Jiangsu University, Zhenjiang, PR China
| | - Qiuqi Feng
- Department of Pharmaceutics, School of Pharmacy, Jiangsu University, Zhenjiang, PR China
| | - Yingying Ge
- Department of Pharmaceutics, School of Pharmacy, Jiangsu University, Zhenjiang, PR China
| | - Mengjie Rui
- Department of Pharmaceutics, School of Pharmacy, Jiangsu University, Zhenjiang, PR China.
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2
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Koyama H. Machine learning application in otology. Auris Nasus Larynx 2024; 51:666-673. [PMID: 38704894 DOI: 10.1016/j.anl.2024.04.003] [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: 12/07/2023] [Revised: 03/13/2024] [Accepted: 04/02/2024] [Indexed: 05/07/2024]
Abstract
This review presents a comprehensive history of Artificial Intelligence (AI) in the context of the revolutionary application of machine learning (ML) to medical research and clinical utilization, particularly for the benefit of researchers interested in the application of ML in otology. To this end, we discuss the key components of ML-input, output, and algorithms. In particular, some representation algorithms commonly used in medical research are discussed. Subsequently, we review ML applications in otology research, including diagnosis, influential identification, and surgical outcome prediction. In the context of surgical outcome prediction, specific surgical treatments, including cochlear implantation, active middle ear implantation, tympanoplasty, and vestibular schwannoma resection, are considered. Finally, we highlight the obstacles and challenges that need to be overcome in future research.
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Affiliation(s)
- Hajime Koyama
- Department of Otorhinolaryngology and Head and Neck Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
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3
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M. Abdelhaleem Ali A, M. Alrobaian M. Strengths and weaknesses of current and future prospects of artificial intelligence-mounted technologies applied in the development of pharmaceutical products and services. Saudi Pharm J 2024; 32:102043. [PMID: 38585196 PMCID: PMC10997913 DOI: 10.1016/j.jsps.2024.102043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 03/18/2024] [Indexed: 04/09/2024] Open
Abstract
Starting from drug discovery, through research and development, to clinical trials and FDA approval, artificial intelligence (AI) plays a vital role in planning, developing, assessing modelling, and optimization of product attributes. In recent decades, machine-learning algorithms integrated into artificial neural networks, neuro-fuzzy logic and decision trees have been applied to tremendous domains related to drug formulation development. Optimized formulations were transformed from lab to market based on optimized properties derived from AI Technologies. Research and development in pharmaceutical industry rely upon computer-driven equipment and machine learning technology to extract data, perform simulations, modelling, and optimization to get optimum solutions. Merging AI technologies in various steps of pharmaceutical manufacture is a major challenge due to lack of in-house technologies. In silico studies based on artificial intelligence are widely applied as effective tools to screen the market needs of medications and pharmaceutical services through inspecting scientific literature and prioritizing medicines for specific illnesses or a particular patient. Specialized personnel who excel in scientific and data science with analytical knowledge are essential for transformation to smart manufacturing and offering services. However, privacy, cybersecurity, AI-dependent unemployment, and ownership rights of AI technologies require proper regulations to gain the benefits and minimize the drawbacks.
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Affiliation(s)
- Ahmed M. Abdelhaleem Ali
- Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, P. O. Box 11099, P. Code 21944, Taif, Saudi Arabia
| | - Majed M. Alrobaian
- Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, P. O. Box 11099, P. Code 21944, Taif, Saudi Arabia
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4
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Agu PC, Obulose CN. Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications. Drug Dev Res 2024; 85:e22159. [PMID: 38375772 DOI: 10.1002/ddr.22159] [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: 11/08/2023] [Revised: 01/12/2024] [Accepted: 01/29/2024] [Indexed: 02/21/2024]
Abstract
The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and expedient options to conventional approaches. The paper opens with an overview of well-known information sources for drug development. The discussion then moves on to molecular representation techniques that make it possible to convert data into representations that computers can understand. The paper also gives a general overview of the algorithms used in the creation of drug discovery models based on AI. In particular, the paper looks at how AI algorithms might be used to forecast drug toxicity, drug bioactivity, and drug physicochemical properties. De novo drug design, binding affinity prediction, and other AI-based models for drug-target interaction were covered in deeper detail. Modern applications of AI in nanomedicine design and pharmacological synergism/antagonism prediction were also covered. The potential advantages of AI in drug development are highlighted as the evaluation comes to a close. It underlines how AI may greatly speed up and improve the efficiency of drug discovery, resulting in the creation of new and better medicines. To fully realize the promise of AI in drug discovery, the review acknowledges the difficulties that come with its uses in this field and advocates for more study and development.
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Affiliation(s)
- Peter Chinedu Agu
- Department of Biochemistry, College of Science, Evangel University, Akaeze, Ebonyi State, Nigeria
| | - Chidiebere Nwiboko Obulose
- Department of Computer Sciences, Our Savior Institute of Science, Agriculture, and Technology (OSISATECH Polytechnic), Enugu, Nigeria
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5
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Zhuang J, Midgley AC, Wei Y, Liu Q, Kong D, Huang X. Machine-Learning-Assisted Nanozyme Design: Lessons from Materials and Engineered Enzymes. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2210848. [PMID: 36701424 DOI: 10.1002/adma.202210848] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/03/2023] [Indexed: 05/11/2023]
Abstract
Nanozymes are nanomaterials that exhibit enzyme-like biomimicry. In combination with intrinsic characteristics of nanomaterials, nanozymes have broad applicability in materials science, chemical engineering, bioengineering, biochemistry, and disease theranostics. Recently, the heterogeneity of published results has highlighted the complexity and diversity of nanozymes in terms of consistency of catalytic capacity. Machine learning (ML) shows promising potential for discovering new materials, yet it remains challenging for the design of new nanozymes based on ML approaches. Alternatively, ML is employed to promote optimization of intelligent design and application of catalytic materials and engineered enzymes. Incorporation of the successful ML algorithms used in the intelligent design of catalytic materials and engineered enzymes can concomitantly facilitate the guided development of next-generation nanozymes with desirable properties. Here, recent progress in ML, its utilization in the design of catalytic materials and enzymes, and how emergent ML applications serve as promising strategies to circumvent challenges associated with time-expensive and laborious testing in nanozyme research and development are summarized. The potential applications of successful examples of ML-aided catalytic materials and engineered enzymes in nanozyme design are also highlighted, with special focus on the unified aims in enhancing design and recapitulation of substrate selectivity and catalytic activity.
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Affiliation(s)
- Jie Zhuang
- School of Medicine, and State, Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, 300071, China
| | - Adam C Midgley
- Key Laboratory of Bioactive Materials for the Ministry of Education, College of Life Sciences, State Key Laboratory of Medicinal Chemical Biology, and Frontiers, Science Center for Cell Responses, Nankai University, Tianjin, 300071, China
| | - Yonghua Wei
- Key Laboratory of Bioactive Materials for the Ministry of Education, College of Life Sciences, State Key Laboratory of Medicinal Chemical Biology, and Frontiers, Science Center for Cell Responses, Nankai University, Tianjin, 300071, China
| | - Qiqi Liu
- Key Laboratory of Bioactive Materials for the Ministry of Education, College of Life Sciences, State Key Laboratory of Medicinal Chemical Biology, and Frontiers, Science Center for Cell Responses, Nankai University, Tianjin, 300071, China
| | - Deling Kong
- Key Laboratory of Bioactive Materials for the Ministry of Education, College of Life Sciences, State Key Laboratory of Medicinal Chemical Biology, and Frontiers, Science Center for Cell Responses, Nankai University, Tianjin, 300071, China
| | - Xinglu Huang
- Key Laboratory of Bioactive Materials for the Ministry of Education, College of Life Sciences, State Key Laboratory of Medicinal Chemical Biology, and Frontiers, Science Center for Cell Responses, Nankai University, Tianjin, 300071, China
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6
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Joy A, Seethi V F, Cyriac MC, Habeeb J, Sudhakaran S, Shah S. Modelling of AgrA inhibitors to combat anti-microbial resistance in Staphylococcus aureus. J Biomol Struct Dyn 2024; 42:551-558. [PMID: 37166373 DOI: 10.1080/07391102.2023.2203260] [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: 11/02/2022] [Accepted: 03/15/2023] [Indexed: 05/12/2023]
Abstract
Staphylococcus aureus is a Gram-positive bacterium found on human skin that causes skin and soft tissue infections, as well as pneumonia, osteomyelitis, and endocarditis. The prevalence of antibiotic resistant strains has made the treatments less effective. An efficient alternate method for battling these contagious diseases is anti-virulence strategy. The AgrA protein, a key activator of Accessory Gene Regulator system in S. aureus, is vital to the virulence of the organism and, consequently, its pathogenesis. Using a Machine Learning algorithm, the Support Vector Machine (SVM), and a ligand-based pharmacophore modelling method, prediction models of AgrA inhibitors were developed. The metrics of the SVM model were inadequate, hence it was not used for virtual screening. For ligand-based pharmacophore modelling, 14 of 29 compounds were removed from the active set due to a lack of shared pharmacophore properties, and 504 compounds were designated as decoys. A 3D pharmacophore model was created using LigandScout 4.4.5, with a fit score of 57.48, including a positive ionizable group, one hydrogen bond donor, and three hydrogen bond acceptors. The model after further validation was used to virtually screen an external database which resulted in six hits. These compounds were docked with the AgrA domain crystal structure to determine the inhibitor activity. Further, each docked complex was subjected to a 100 ns molecular dynamics simulation. CID238 and CID20510252 demonstrated potent inhibitory binding interactions and hence can be used to develop AgrA inhibitors in future after proper validation.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Amitha Joy
- Department of Biotechnology, Sahrdaya College of Engineering and Technology, Thrissur, Kerala, India
| | | | - Marria C Cyriac
- Department of Biotechnology, Sahrdaya College of Engineering and Technology, Thrissur, Kerala, India
| | - Jasmin Habeeb
- Division of Crop Improvement, ICAR-Central Plantation Crops Research Institute, Kasaragod, Kerala, India
| | | | - Shaheen Shah
- Genomics Central [MaGenomics], Thrissur, Kerala, India
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Zhao X, Kong Y, Ji Y, Xin X, Chen L, Chen G, Yu C. Classification models for predicting the bioactivity of pan-TRK inhibitors and SAR analysis. Mol Divers 2023:10.1007/s11030-023-10735-2. [PMID: 37910346 DOI: 10.1007/s11030-023-10735-2] [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: 07/14/2023] [Accepted: 09/22/2023] [Indexed: 11/03/2023]
Abstract
Tropomyosin receptor kinases (TRKs) are important broad-spectrum anticancer targets. The oncogenic rearrangement of the NTRK gene disrupts the extracellular structural domain and epitopes for therapeutic antibodies, making small-molecule inhibitors essential for treating NTRK fusion-driven tumors. In this work, several algorithms were used to construct descriptor-based and nondescriptor-based models, and the models were evaluated by outer 10-fold cross-validation. To find a model with good generalization ability, the dataset was partitioned by random and cluster-splitting methods to construct in- and cross-domain models, respectively. Among the 48 models built, the model with the combination of the deep neural network (DNN) algorithm and extended connectivity fingerprints 4 (ECFP4) descriptors achieved excellent performance in both dataset divisions. The results indicate that the DNN algorithm has a strong generalization prediction ability, and the richness of features plays a vital role in predicting unknown spatial molecules. Additionally, we combined the clustering results and decision tree models of fingerprint descriptors to perform structure-activity relationship analysis. It was found that nitrogen-containing aromatic heterocyclic and benzo heterocyclic structures play a crucial role in enhancing the activity of TRK inhibitors. Workflow for generating predictive models for TRK inhibitors.
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Affiliation(s)
- Xiaoman Zhao
- College of Life Science and Technology, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing, 100029, People's Republic of China
- College of Bio engineering, No. 9 Liangshuihe 1st Street, Beijing, 100176, People's Republic of China
| | - Yue Kong
- College of Life Science and Technology, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing, 100029, People's Republic of China
| | - Yueshan Ji
- College of Life Science and Technology, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing, 100029, People's Republic of China
| | - Xiulan Xin
- College of Bio engineering, No. 9 Liangshuihe 1st Street, Beijing, 100176, People's Republic of China
| | - Liang Chen
- College of Bio engineering, No. 9 Liangshuihe 1st Street, Beijing, 100176, People's Republic of China
| | - Guang Chen
- College of Life Science and Technology, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing, 100029, People's Republic of China
| | - Changyuan Yu
- College of Life Science and Technology, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing, 100029, People's Republic of China.
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8
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Wang Y, Wang J, Yan Z, Liu S, Xu W. Microenvironment modulation by key regulators of RNA N6-methyladenosine modification in respiratory allergic diseases. BMC Pulm Med 2023; 23:210. [PMID: 37328853 DOI: 10.1186/s12890-023-02499-0] [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: 11/15/2022] [Accepted: 05/30/2023] [Indexed: 06/18/2023] Open
Abstract
BACKGROUND RNA N6-methyladenosine (m6A) regulators are considered post-transcriptional regulators that affect several biological functions, and their role in immunity, in particular, is emerging. However, the role of m6A regulators in respiratory allergic diseases remains unclear. Therefore, we aimed to investigate the role of key m6A regulators in mediating respiratory allergic diseases and immune microenvironment infiltration characteristics. METHODS We downloaded gene expression profiles of respiratory allergies from the Gene Expression Omnibus (GEO) database and we performed hierarchical clustering, difference analysis, and construction of predictive models to identify hub m6A regulators that affect respiratory allergies. Next, we investigate the underlying biological mechanisms of key m6A regulators by performing PPI network analysis, functional enrichment analysis, and immune microenvironment infiltration analysis. In addition, we performed a drug sensitivity analysis on the key m6A regulator, hoping to be able to provide some implications for clinical medication. RESULTS In this study, we identified four hub m6A regulators that affect the respiratory allergy and investigated the underlying biological mechanisms. In addition, studies on the characteristics of immune microenvironment infiltration revealed that the expression of METTL14, METTL16, and RBM15B correlated with the infiltration of the mast and Th2 cells in respiratory allergy, and METTL16 expression was found to be significantly negatively correlated with macrophages for the first time (R = -0.53, P < 0.01). Finally, a key m6A regulator, METTL14, was screened by combining multiple algorithms. In addition, by performing a drug sensitivity analysis on METTL14, we hypothesized that it may play an important role in the improvement of allergic symptoms in the upper and lower airways with topical nasal glucocorticoids. CONCLUSIONS Our findings suggest that m6A regulators, particularly METTL14, play a crucial role in the development of respiratory allergic diseases and the infiltration of immune cells. These results may provide insight into the mechanism of action of methylprednisolone in treating respiratory allergic diseases.
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Affiliation(s)
- Yuting Wang
- Department of Otorhinolaryngology, Dongfang Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China
| | - Jiaxi Wang
- Department of Otorhinolaryngology, Dongfang Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China.
| | - Zhanfeng Yan
- Department of Otorhinolaryngology, Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China
| | - Siming Liu
- Department of Otorhinolaryngology, Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China
| | - Wenlong Xu
- Department of Otorhinolaryngology, Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China
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9
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Bagnolini G, Luu TB, Hargrove AE. Recognizing the power of machine learning and other computational methods to accelerate progress in small molecule targeting of RNA. RNA (NEW YORK, N.Y.) 2023; 29:473-488. [PMID: 36693763 PMCID: PMC10019373 DOI: 10.1261/rna.079497.122] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
RNA structures regulate a wide range of processes in biology and disease, yet small molecule chemical probes or drugs that can modulate these functions are rare. Machine learning and other computational methods are well poised to fill gaps in knowledge and overcome the inherent challenges in RNA targeting, such as the dynamic nature of RNA and the difficulty of obtaining RNA high-resolution structures. Successful tools to date include principal component analysis, linear discriminate analysis, k-nearest neighbor, artificial neural networks, multiple linear regression, and many others. Employment of these tools has revealed critical factors for selective recognition in RNA:small molecule complexes, predictable differences in RNA- and protein-binding ligands, and quantitative structure activity relationships that allow the rational design of small molecules for a given RNA target. Herein we present our perspective on the value of using machine learning and other computation methods to advance RNA:small molecule targeting, including select examples and their validation as well as necessary and promising future directions that will be key to accelerate discoveries in this important field.
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Affiliation(s)
- Greta Bagnolini
- Department of Chemistry, Duke University, Durham, North Carolina 27708, USA
| | - TinTin B Luu
- Department of Chemistry, Duke University, Durham, North Carolina 27708, USA
| | - Amanda E Hargrove
- Department of Chemistry, Duke University, Durham, North Carolina 27708, USA
- Department of Biochemistry, Duke University School of Medicine, Durham, North Carolina 27710, USA
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10
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Chen W, Liu X, Zhang S, Chen S. Artificial intelligence for drug discovery: Resources, methods, and applications. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 31:691-702. [PMID: 36923950 PMCID: PMC10009646 DOI: 10.1016/j.omtn.2023.02.019] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
Conventional wet laboratory testing, validations, and synthetic procedures are costly and time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques have revolutionized their applications to drug discovery. Combined with accessible data resources, AI techniques are changing the landscape of drug discovery. In the past decades, a series of AI-based models have been developed for various steps of drug discovery. These models have been used as complements of conventional experiments and have accelerated the drug discovery process. In this review, we first introduced the widely used data resources in drug discovery, such as ChEMBL and DrugBank, followed by the molecular representation schemes that convert data into computer-readable formats. Meanwhile, we summarized the algorithms used to develop AI-based models for drug discovery. Subsequently, we discussed the applications of AI techniques in pharmaceutical analysis including predicting drug toxicity, drug bioactivity, and drug physicochemical property. Furthermore, we introduced the AI-based models for de novo drug design, drug-target structure prediction, drug-target interaction, and binding affinity prediction. Moreover, we also highlighted the advanced applications of AI in drug synergism/antagonism prediction and nanomedicine design. Finally, we discussed the challenges and future perspectives on the applications of AI to drug discovery.
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Affiliation(s)
- Wei Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Xuesong Liu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Sanyin Zhang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Shilin Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
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Qin T, Gao X, Lei L, Feng J, Zhang W, Hu Y, Shen Z, Liu Z, Huan Y, Wu S, Xia J, Zhang L. Machine learning- and structure-based discovery of a novel chemotype as FXR agonists for potential treatment of nonalcoholic fatty liver disease. Eur J Med Chem 2023; 252:115307. [PMID: 37003047 DOI: 10.1016/j.ejmech.2023.115307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 03/12/2023] [Accepted: 03/22/2023] [Indexed: 03/29/2023]
Abstract
Farnesoid X receptor (FXR) is a promising target for drug discovery against nonalcoholic fatty liver disease (NAFLD). However, no FXR agonist has been approved for NAFLD so far. The R & D of FXR agonists are somewhat hindered by the lack of effective and safe chemotypes. To this end, we developed a multi-stage computational workflow to screen the Specs and ChemDiv chemical library for FXR agonists, which consisted of machine learning (ML)-based classifiers, shape-based and electrostatic-based models, a FRED-based molecular docking protocol, an ADMET prediction protocol and substructure search. As a result, we identified a novel chemotype that has never been reported before, with compound XJ02862 (ChemDiv ID: Y020-6413) as the representative. By designing an asymmetric synthesis strategy, we were able to prepare four isomers of compound XJ02862. Interestingly, one of the isomers, 2-((S)-1-((2S,4R)-2-methyl-4-(phenylamino)-3,4-dihydroquinolin-1(2H)-yl)-1-oxopropan-2-yl)hexahydro-1H-isoindole-1,3(2H)-dione (XJ02862-S2), showed potent FXR agonistic activity in HEK293T cells. The molecular docking, molecular dynamics simulations and site-directed mutagenesis suggested the hydrogen bond between compound XJ02862-S2 and HIS294 of FXR is essential for ligand binding. We further demonstrated that compound XJ02862-S2 had no agonistic effect on TGR5. Further biological experiments have shown that compound XJ02862-S2 could ameliorate hypercholesterolemia, hepatic steatosis, hyperglycemia, insulin resistance (IR) in high-fat-diet induced obese (DIO) mice. In term of molecular mechanism, compound XJ02862-S2 regulates the expression of FXR downstream genes involved in lipogenesis, cholesterol transport and bile acid biosynthesis and transport. Taken together, we have discovered a novel chemotype as potent FXR agonists for NAFLD by computational modeling, chemical synthesis and biological evaluation.
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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, 100050, China
| | - Xuefeng Gao
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Lei Lei
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Jing Feng
- 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
| | - Wenxuan Zhang
- 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
| | - Yuhua Hu
- 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
| | - Zhufang Shen
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Zhenming Liu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
| | - Yi Huan
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, 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.
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
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12
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Metwally AA, Nayel AA, Hathout RM. In silico prediction of siRNA ionizable-lipid nanoparticles In vivo efficacy: Machine learning modeling based on formulation and molecular descriptors. Front Mol Biosci 2022; 9:1042720. [PMID: 36619167 PMCID: PMC9811823 DOI: 10.3389/fmolb.2022.1042720] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
In silico prediction of the in vivo efficacy of siRNA ionizable-lipid nanoparticles is desirable as it can save time and resources dedicated to wet-lab experimentation. This study aims to computationally predict siRNA nanoparticles in vivo efficacy. A data set containing 120 entries was prepared by combining molecular descriptors of the ionizable lipids together with two nanoparticles formulation characteristics. Input descriptor combinations were selected by an evolutionary algorithm. Artificial neural networks, support vector machines and partial least squares regression were used for QSAR modeling. Depending on how the data set is split, two training sets and two external validation sets were prepared. Training and validation sets contained 90 and 30 entries respectively. The results showed the successful predictions of validation set log (siRNA dose) with Rval 2= 0.86-0.89 and 0.75-80 for validation sets one and two, respectively. Artificial neural networks resulted in the best Rval 2 for both validation sets. For predictions that have high bias, improvement of Rval 2 from 0.47 to 0.96 was achieved by selecting the training set lipids lying within the applicability domain. In conclusion, in vivo performance of siRNA nanoparticles was successfully predicted by combining cheminformatics with machine learning techniques.
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Affiliation(s)
- Abdelkader A. Metwally
- Department of Pharmaceutics, Faculty of Pharmacy, Health Sciences Center, Kuwait University, Kuwait City, Kuwait,Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt,*Correspondence: Abdelkader A. Metwally,
| | - Amira A. Nayel
- Clinical Pharmacy Department, Alexandria Ophthalmology Hospital, Alexandria, Egypt,Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, Alexandria University, Alexandria, Egypt
| | - Rania M. Hathout
- Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt
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13
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Houssein EH, Hosney ME, Mohamed WM, Ali AA, Younis EMG. Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data. Neural Comput Appl 2022; 35:5251-5275. [PMID: 36340595 PMCID: PMC9628476 DOI: 10.1007/s00521-022-07916-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 09/30/2022] [Indexed: 11/06/2022]
Abstract
Feature selection (FS) is one of the basic data preprocessing steps in data mining and machine learning. It is used to reduce feature size and increase model generalization. In addition to minimizing feature dimensionality, it also enhances classification accuracy and reduces model complexity, which are essential in several applications. Traditional methods for feature selection often fail in the optimal global solution due to the large search space. Many hybrid techniques have been proposed depending on merging several search strategies which have been used individually as a solution to the FS problem. This study proposes a modified hunger games search algorithm (mHGS), for solving optimization and FS problems. The main advantages of the proposed mHGS are to resolve the following drawbacks that have been raised in the original HGS; (1) avoiding the local search, (2) solving the problem of premature convergence, and (3) balancing between the exploitation and exploration phases. The mHGS has been evaluated by using the IEEE Congress on Evolutionary Computation 2020 (CEC'20) for optimization test and ten medical and chemical datasets. The data have dimensions up to 20000 features or more. The results of the proposed algorithm have been compared to a variety of well-known optimization methods, including improved multi-operator differential evolution algorithm (IMODE), gravitational search algorithm, grey wolf optimization, Harris Hawks optimization, whale optimization algorithm, slime mould algorithm and hunger search games search. The experimental results suggest that the proposed mHGS can generate effective search results without increasing the computational cost and improving the convergence speed. It has also improved the SVM classification performance.
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Affiliation(s)
- Essam H. Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt
| | - Mosa E. Hosney
- Faculty of Computers and Information, Luxor University, Luxor, Egypt
| | - Waleed M. Mohamed
- Faculty of Computers and Information, Minia University, Minia, Egypt
| | - Abdelmgeid A. Ali
- Faculty of Computers and Information, Minia University, Minia, Egypt
| | - Eman M. G. Younis
- Faculty of Computers and Information, Minia University, Minia, Egypt
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14
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Abrahamsson D, Siddharth A, Robinson JF, Soshilov A, Elmore S, Cogliano V, Ng C, Khan E, Ashton R, Chiu WA, Fung J, Zeise L, Woodruff TJ. Modeling the transplacental transfer of small molecules using machine learning: a case study on per- and polyfluorinated substances (PFAS). JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2022; 32:808-819. [PMID: 36207486 PMCID: PMC9742309 DOI: 10.1038/s41370-022-00481-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 09/14/2022] [Accepted: 09/15/2022] [Indexed: 05/10/2023]
Abstract
BACKGROUND Despite their large numbers and widespread use, very little is known about the extent to which per- and polyfluoroalkyl substances (PFAS) can cross the placenta and expose the developing fetus. OBJECTIVE The aim of our study is to develop a computational approach that can be used to evaluate the of extend to which small molecules, and in particular PFAS, can cross to cross the placenta and partition to cord blood. METHODS We collected experimental values of the concentration ratio between cord and maternal blood (RCM) for 260 chemical compounds and calculated their physicochemical descriptors using the cheminformatics package Mordred. We used the compiled database to, train and test an artificial neural network (ANN). And then applied the best performing model to predict RCM for a large dataset of PFAS chemicals (n = 7982). We, finally, examined the calculated physicochemical descriptors of the chemicals to identify which properties correlated significantly with RCM. RESULTS We determined that 7855 compounds were within the applicability domain and 127 compounds are outside the applicability domain of our model. Our predictions of RCM for PFAS suggested that 3623 compounds had a log RCM > 0 indicating preferable partitioning to cord blood. Some examples of these compounds were bisphenol AF, 2,2-bis(4-aminophenyl)hexafluoropropane, and nonafluoro-tert-butyl 3-methylbutyrate. SIGNIFICANCE These observations have important public health implications as many PFAS have been shown to interfere with fetal development. In addition, as these compounds are highly persistent and many of them can readily cross the placenta, they are expected to remain in the population for a long time as they are being passed from parent to offspring. IMPACT Understanding the behavior of chemicals in the human body during pregnancy is critical in preventing harmful exposures during critical periods of development. Many chemicals can cross the placenta and expose the fetus, however, the mechanism by which this transport occurs is not well understood. In our study, we developed a machine learning model that describes the transplacental transfer of chemicals as a function of their physicochemical properties. The model was then used to make predictions for a set of 7982 per- and polyfluorinated alkyl substances that are listed on EPA's CompTox Chemicals Dashboard. The model can be applied to make predictions for other chemical categories of interest, such as plasticizers and pesticides. Accurate predictions of RCM can help scientists and regulators to prioritize chemicals that have the potential to cause harm by exposing the fetus.
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Affiliation(s)
- Dimitri Abrahamsson
- Department of Obstetrics, Gynecology and Reproductive Sciences, Program on Reproductive Health and the Environment, University of California, San Francisco, 490 Illinois Street, San Francisco, CA, 94143, USA.
| | - Adi Siddharth
- Department of Obstetrics, Gynecology and Reproductive Sciences, Program on Reproductive Health and the Environment, University of California, San Francisco, 490 Illinois Street, San Francisco, CA, 94143, USA
| | - Joshua F Robinson
- Department of Obstetrics, Gynecology and Reproductive Sciences, Program on Reproductive Health and the Environment, University of California, San Francisco, 490 Illinois Street, San Francisco, CA, 94143, USA
| | - Anatoly Soshilov
- California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1001 I St, Sacramento, CA, 95814, USA
- California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1515 Clay St, Oakland, CA, 94612, USA
| | - Sarah Elmore
- California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1001 I St, Sacramento, CA, 95814, USA
- California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1515 Clay St, Oakland, CA, 94612, USA
| | - Vincent Cogliano
- California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1001 I St, Sacramento, CA, 95814, USA
- California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1515 Clay St, Oakland, CA, 94612, USA
| | - Carla Ng
- Department of Civil and Environmental Engineering, University of Pittsburgh, 3700 O'Hara St, Pittsburgh, PA, 15261, USA
| | - Elaine Khan
- California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1001 I St, Sacramento, CA, 95814, USA
- California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1515 Clay St, Oakland, CA, 94612, USA
| | - Randolph Ashton
- Wisconsin Institute for Discovery, University of Wisconsin, Madison, 330 N Orchard St, Madison, WI, 53715, USA
- The Stem Cell and Regenerative Medicine Center, University of Wisconsin, Madison, 1111 Highland Avenue, Madison, WI, 53705, USA
- Department of Biomedical Engineering, University of Wisconsin - Madison, 1550 Engineering Drive, Madison, WI, 53706, USA
| | - Weihsueh A Chiu
- Department of Veterinary Physiology and Pharmacology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, 77843, USA
| | - Jennifer Fung
- Department of Obstetrics, Gynecology, and Reproductive Science and the Center of Reproductive Science, University of California, San Francisco, San Francisco, CA, 94143-2240, USA
| | - Lauren Zeise
- California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1001 I St, Sacramento, CA, 95814, USA
- California Environmental Protection Agency, Office of Environmental Health Hazard Assessment, 1515 Clay St, Oakland, CA, 94612, USA
| | - Tracey J Woodruff
- Department of Obstetrics, Gynecology and Reproductive Sciences, Program on Reproductive Health and the Environment, University of California, San Francisco, 490 Illinois Street, San Francisco, CA, 94143, USA.
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15
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Yu XX, Liu MQ, Li XY, Zhang YH, Tao BJ. Qualitative and Quantitative Prediction of Food Allergen Epitopes Based on Machine Learning Combined with In Vitro Experimental Validation. Food Chem 2022; 405:134796. [DOI: 10.1016/j.foodchem.2022.134796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 10/25/2022] [Accepted: 10/26/2022] [Indexed: 11/24/2022]
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16
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Li X, Liang C, Su R, Wang X, Yao Y, Ding H, Zhou G, Luo Z, Zhang H, Li Y. An integrated strategy combining metabolomics and machine learning for the evaluation of bioactive markers that differentiate various bile. Front Chem 2022; 10:1005843. [DOI: 10.3389/fchem.2022.1005843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 10/10/2022] [Indexed: 11/13/2022] Open
Abstract
Animal bile is an important component of natural medicine and is widely used in clinical treatment. However, it is easy to cause mixed applications during processing, resulting in uneven quality, which seriously affects and harms the interests and health of consumers. Bile acids are the major bioactive constituents of bile and contain a variety of isomeric constituents. Although the components are structurally similar, they exhibit different pharmacological activities. Identifying the characteristics of each animal bile is particularly important for processing and reuse. It is necessary to establish an accurate analysis method to distinguish different types of animal bile. We evaluated the biological activity of key feature markers from various animal bile samples. In this study, a strategy combining metabolomics and machine learning was used to compare the bile of three different animals, and four key markers were screened. Quantitative analysis of the key markers showed that the levels of Glycochenodeoxycholic acid (GCDCA) and Taurodeoxycholic acid (TDCA) were highest in pig bile; Glycocholic acid (GCA) and Cholic acid (CA) were the most abundant in bovine and sheep bile, respectively. In addition, four key feature markers significantly inhibited the production of NO in LPS-stimulated RAW264.7 macrophage cells. These findings will contribute to the targeted development of bile in various animals and provide a basis for its rational application.
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17
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Ji Y, Li R, Tian Y, Chen G, Yan A. Classification models and SAR analysis on thromboxane A 2 synthase inhibitors by machine learning methods. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:429-462. [PMID: 35678125 DOI: 10.1080/1062936x.2022.2078880] [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: 01/25/2022] [Accepted: 05/11/2022] [Indexed: 06/15/2023]
Abstract
Thromboxane A2 synthase (TXS) is a promising drug target for cardiovascular diseases and cancer. In this work, we conducted a structure-activity relationship (SAR) study on 526 TXS inhibitors for bioactivity prediction. Three types of descriptors (MACCS fingerprints, ECFP4 fingerprints, and MOE descriptors) were utilized to characterize inhibitors, 24 classification models were developed by support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and deep neural networks (DNN). Then we reduced the number of fingerprints according to the contribution of descriptors to the models, and constructed 16 extra models on simplified fingerprints. In general, Model_4D built by DNN algorithm and 67 bits MACCS fingerprints performs best. The prediction accuracy of the model on the test set is 0.969, and Matthews correlation coefficient (MCC) is 0.936. The distance between compound and model (dSTD-PRO) was used to characterize the application domain of the model. In the test set of Model_4D, dSTD-PRO of 91.5% compounds is lower than the corresponding training set threshold (threshold0.90 = 0.1055), and the accuracy of these compounds is 0.983. In addition, the important descriptors were summarized and further analyzed. It showed that aromatic nitrogenous heterocyclic groups were beneficial to improve the bioactivity of TXS inhibitors.
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Affiliation(s)
- Y Ji
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, P. R. China
| | - R Li
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, P. R. China
| | - Y Tian
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, P. R. China
| | - G Chen
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China
| | - A Yan
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, P. R. China
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18
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Jukič M, Bren U. Machine Learning in Antibacterial Drug Design. Front Pharmacol 2022; 13:864412. [PMID: 35592425 PMCID: PMC9110924 DOI: 10.3389/fphar.2022.864412] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/28/2022] [Indexed: 12/17/2022] Open
Abstract
Advances in computer hardware and the availability of high-performance supercomputing platforms and parallel computing, along with artificial intelligence methods are successfully complementing traditional approaches in medicinal chemistry. In particular, machine learning is gaining importance with the growth of the available data collections. One of the critical areas where this methodology can be successfully applied is in the development of new antibacterial agents. The latter is essential because of the high attrition rates in new drug discovery, both in industry and in academic research programs. Scientific involvement in this area is even more urgent as antibacterial drug resistance becomes a public health concern worldwide and pushes us increasingly into the post-antibiotic era. In this review, we focus on the latest machine learning approaches used in the discovery of new antibacterial agents and targets, covering both small molecules and antibacterial peptides. For the benefit of the reader, we summarize all applied machine learning approaches and available databases useful for the design of new antibacterial agents and address the current shortcomings.
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Affiliation(s)
- Marko Jukič
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Maribor, Slovenia.,Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Slovenia
| | - Urban Bren
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Maribor, Slovenia.,Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Slovenia
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19
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Choudhury C, Arul Murugan N, Deva Priyakumar U. Structure-based drug repurposing: traditional and advanced AI/ML-aided methods. Drug Discov Today 2022; 27:1847-1861. [PMID: 35301148 PMCID: PMC8920090 DOI: 10.1016/j.drudis.2022.03.006] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 02/16/2022] [Accepted: 03/10/2022] [Indexed: 02/08/2023]
Abstract
The current global health emergency in the form of the Coronavirus 2019 (COVID-19) pandemic has highlighted the need for fast, accurate, and efficient drug discovery pipelines. Traditional drug discovery projects relying on in vitro high-throughput screening (HTS) involve large investments and sophisticated experimental set-ups, affordable only to big biopharmaceutical companies. In this scenario, application of efficient state-of-the-art computational methods and modern artificial intelligence (AI)-based algorithms for rapid screening of repurposable chemical space [approved drugs and natural products (NPs) with proven pharmacokinetic profiles] to identify the initial leads is a powerful option to save resources and time. Structure-based drug repurposing is a popular in silico repurposing approach. In this review, we discuss traditional and modern AI-based computational methods and tools applied at various stages for structure-based drug discovery (SBDD) pipelines. Additionally, we highlight the role of generative models in generating molecules with scaffolds from repurposable chemical space. Teaser: This review highlights the importance of repurposable chemical space, and the contributions of conventional in silico approaches and modern machine-learning algorithms for rapid structure-based drug repurposing.
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Affiliation(s)
- Chinmayee Choudhury
- Department of Experimental Medicine and Biotechnology, Postgraduate Institute of Medical Education and Research, Sector-12, Chandigarh 160012, India
| | - N Arul Murugan
- Department of Computer Science, School of Electrical Engineering and Computer Sciences, KTH Royal Institute of Technology, S-100 44, Stockholm, Sweden; Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi 110020, India.
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
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20
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Alves LA, Ferreira NCDS, Maricato V, Alberto AVP, Dias EA, Jose Aguiar Coelho N. Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs. Front Chem 2022; 9:787194. [PMID: 35127645 PMCID: PMC8811035 DOI: 10.3389/fchem.2021.787194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 12/10/2021] [Indexed: 11/23/2022] Open
Abstract
Despite the increasing number of pharmaceutical companies, university laboratories and funding, less than one percent of initially researched drugs enter the commercial market. In this context, virtual screening (VS) has gained much attention due to several advantages, including timesaving, reduced reagent and consumable costs and the performance of selective analyses regarding the affinity between test molecules and pharmacological targets. Currently, VS is based mainly on algorithms that apply physical and chemistry principles and quantum mechanics to estimate molecule affinities and conformations, among others. Nevertheless, VS has not reached the expected results concerning the improvement of market-approved drugs, comprising less than twenty drugs that have reached this goal to date. In this context, graph neural networks (GNN), a recent deep-learning subtype, may comprise a powerful tool to improve VS results concerning natural products that may be used both simultaneously with standard algorithms or isolated. This review discusses the pros and cons of GNN applied to VS and the future perspectives of this learnable algorithm, which may revolutionize drug discovery if certain obstacles concerning spatial coordinates and adequate datasets, among others, can be overcome.
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Affiliation(s)
- Luiz Anastacio Alves
- Laboratory of Cellular Communication, Oswaldo Cruz Institute – Fiocruz, Rio de Janeiro, Brazil
- *Correspondence: Luiz Anastacio Alves,
| | | | - Victor Maricato
- Laboratory of Cellular Communication, Oswaldo Cruz Institute – Fiocruz, Rio de Janeiro, Brazil
| | | | - Evellyn Araujo Dias
- Laboratory of Cellular Communication, Oswaldo Cruz Institute – Fiocruz, Rio de Janeiro, Brazil
| | - Nt Jose Aguiar Coelho
- National Institute of Industrial Property - INPI and Veiga de Almeida University - UVA, Rio de Janeiro, Brazil
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21
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Li X, Yao Y, Chen M, Ding H, Liang C, Lv L, Zhao H, Zhou G, Luo Z, Li Y, Zhang H. Comprehensive evaluation integrating omics strategy and machine learning algorithms for consistency of calculus bovis from different sources. Talanta 2022; 237:122873. [PMID: 34736706 DOI: 10.1016/j.talanta.2021.122873] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 08/31/2021] [Accepted: 09/09/2021] [Indexed: 01/20/2023]
Abstract
In the clinical application of Traditional Chinese Medicine (TCM) substitutes, the consistency evaluation of TCM substitutes from different sources is recognized as the main bottleneck. As the most widely used analytical method in TCM consistency evaluation, fingerprint similarity evaluation suffers from insufficient method sensitivity and poor conformity with the actual characteristics of TCM, which is difficult to adapt to the analytical needs of complex substance systems of TCM. This work aims to develop an effective and more accurate method for consistency evaluation using omics strategy and machine learning algorithms. The natural calculus bovis (NCB) were graded into three groups according to the similarity to in vitro cultured bovis (IVCB), and chemical markers between samples of each grade were screened out. Support vector machine (SVM) models with different kernels were then constructed by using the chemical markers as feature variables. The results showed that the classification accuracy of the SVM classifier of NCB and the consistency evaluation SVM model classifier was 95.74% and 100.0%, respectively. The approach demonstrated in the study presented a good analytical performance with higher sensitivity, accuracy for consistency evaluation of TCM.
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Affiliation(s)
- Xinyue Li
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China; Key Laboratory of Pharmacology of Traditional Chinese Medicine Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China.
| | - Yaqi Yao
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China.
| | - Meiling Chen
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China.
| | - Haoran Ding
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China.
| | - Chenrui Liang
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China.
| | - Ling Lv
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China; Key Laboratory of Pharmacology of Traditional Chinese Medicine Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China.
| | - Huan Zhao
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China.
| | - Guanru Zhou
- Wuhan Jianmin Dapeng Pharmaceutical Co., Ltd, Wuhan, Hubei Province, 430000, PR China.
| | - Zhanglong Luo
- Wuhan Jianmin Dapeng Pharmaceutical Co., Ltd, Wuhan, Hubei Province, 430000, PR China.
| | - Yubo Li
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China.
| | - Han Zhang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China; Key Laboratory of Pharmacology of Traditional Chinese Medicine Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China.
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22
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Machine learning and statistical approach in modeling and optimization of surface roughness in wire electrical discharge machining. MACHINE LEARNING WITH APPLICATIONS 2021. [DOI: 10.1016/j.mlwa.2021.100099] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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23
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Selvaraj C, Chandra I, Singh SK. Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries. Mol Divers 2021; 26:1893-1913. [PMID: 34686947 PMCID: PMC8536481 DOI: 10.1007/s11030-021-10326-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 09/24/2021] [Indexed: 12/27/2022]
Abstract
The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.
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Affiliation(s)
- Chandrabose Selvaraj
- CADD and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India.
| | - Ishwar Chandra
- CADD and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India
| | - Sanjeev Kumar Singh
- CADD and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India.
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24
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Agyapong O, Miller WA, Wilson MD, Kwofie SK. Development of a proteochemometric-based support vector machine model for predicting bioactive molecules of tubulin receptors. Mol Divers 2021; 26:2231-2242. [PMID: 34626303 DOI: 10.1007/s11030-021-10329-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 09/23/2021] [Indexed: 11/26/2022]
Abstract
Microtubules are receiving enormous interest in drug discovery due to the important roles they play in cellular functions. Targeting tubulin polymerization presents an excellent opportunity for the development of anti-tubulin drugs. Drug resistance and high toxicity of currently used tubulin-binding agents have necessitated the pursuit of novel drug candidates with increased therapeutic potency. The design of novel drug candidates can be achieved using efficient computational techniques to support existing efforts. Proteochemometric (PCM) modeling is a computational technique that can be employed to elucidate the bioactivity relations between related targets and multiple ligands. We have developed a PCM-based Support Vector Machine (SVM) approach for predicting the bioactivity between tubulin receptors and small, drug-like molecules. The bioactivity datasets used for training the SVM algorithm were obtained from the Binding DB database. The SVM-based PCM model yielded a good overall predictive performance with an area under the curve (AUC) of 87%, Matthews correlation coefficient (MCC) of 72%, overall accuracy of 93%, and a classification error of 7%. The algorithm allows the prediction of the likelihood of new interactions based on confidence scores between the query datasets, comprising ligands in SMILES format and protein sequences of tubulin targets. The algorithm has been implemented as a web server known as TubPred, accessible via http://35.167.90.225:5000/ .
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Affiliation(s)
- Odame Agyapong
- Department of Biomedical Engineering, School of Engineering Sciences, College of Basic and Applied Sciences, University of Ghana, PMB LG 77, Legon, Accra, Ghana
- Department of Parasitology, Noguchi Memorial Institute for Medical Research (NMIMR), College of Health Sciences (CHS), University of Ghana, P.O. Box LG 581, Legon, Accra, Ghana
| | - Whelton A Miller
- Department of Medicine, Loyola University Medical Center, Maywood, IL, 60153, USA
- School of Engineering and Applied Science, Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Molecular Pharmacology and Neuroscience, Loyola University Medical Center, Maywood, IL, 60153, USA
| | - Michael D Wilson
- Department of Parasitology, Noguchi Memorial Institute for Medical Research (NMIMR), College of Health Sciences (CHS), University of Ghana, P.O. Box LG 581, Legon, Accra, Ghana
- Department of Medicine, Loyola University Medical Center, Maywood, IL, 60153, USA
| | - Samuel K Kwofie
- Department of Biomedical Engineering, School of Engineering Sciences, College of Basic and Applied Sciences, University of Ghana, PMB LG 77, Legon, Accra, Ghana.
- West African Centre for Cell Biology of Infectious Pathogens, Department of Biochemistry, Cell and Molecular Biology, College of Basic and Applied Sciences, University of Ghana, Accra, Ghana.
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25
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Abstract
This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.
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Affiliation(s)
- Suresh Dara
- Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Medak, 502313 Telangana India
| | - Swetha Dhamercherla
- Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Medak, 502313 Telangana India
| | - Surender Singh Jadav
- Centre for Molecular Cancer Research (CMCR) and Vishnu Institute of Pharmaceutical Education and Research (VIPER), Narsapur, Medak, 502313 Telangana India
| | - CH Madhu Babu
- Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Medak, 502313 Telangana India
| | - Mohamed Jawed Ahsan
- Department of Pharmaceutical Chemistry, Maharishi Arvind College of Pharmacy, Jaipur, 302023 Rajasthan India
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Kashyap K, Siddiqi MI. Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents. Mol Divers 2021; 25:1517-1539. [PMID: 34282519 DOI: 10.1007/s11030-021-10274-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 07/05/2021] [Indexed: 12/12/2022]
Abstract
Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.
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Affiliation(s)
- Kushagra Kashyap
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Research Institute (CSIR-CDRI) Campus, Lucknow, India.,Molecular and Structural Biology Division, CSIR-Central Drug Research Institute (CSIR-CDRI), Sector 10, Jankipuram Extension, Sitapur Road, Lucknow, 226031, India
| | - Mohammad Imran Siddiqi
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Research Institute (CSIR-CDRI) Campus, Lucknow, India. .,Molecular and Structural Biology Division, CSIR-Central Drug Research Institute (CSIR-CDRI), Sector 10, Jankipuram Extension, Sitapur Road, Lucknow, 226031, India.
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Jiang Z, Fa B, Zhang X, Wang J, Feng Y, Shi H, Zhang Y, Sun D, Wang H, Yin S. Identifying genetic risk variants associated with noise-induced hearing loss based on a novel strategy for evaluating individual susceptibility. Hear Res 2021; 407:108281. [PMID: 34157653 DOI: 10.1016/j.heares.2021.108281] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 05/10/2021] [Accepted: 05/26/2021] [Indexed: 12/20/2022]
Abstract
BACKGROUND The overall genetic profile for noise-induced hearing loss (NIHL) remains elusive. Herein we proposed a novel machine learning (ML) based strategy to evaluate individual susceptibility to NIHL and identify the underlying genetic risk variants based on a subsample of participants with extreme phenotypes. METHODS Five features (age, sex, cumulative noise exposure [CNE], smoking, and alcohol drinking status) of 5,539 shipbuilding workers from large cross-sectional surveys were included in four ML classification models to predict their hearing levels. The area under the curve (AUC) and prediction accuracy were exploited to evaluate the performance of the models. Based on the prediction error of the ML models, the NIHL-susceptible group (n=150) and NIHL-resistant group (n=150) with a paradoxical relationship between hearing levels and features were separately screened, to identify the underlying variants associated with NIHL risk using whole-exome sequencing (WES). Subsequently, candidate risk variants were validated in an additional replication cohort (n=2108), followed by a meta-analysis. RESULTS With 10-fold cross-validation, the performances of the four ML models were robust and similar, with average AUCs and accuracies ranging from 0.783 to 0.798 and 73.7% to 73.8%, respectively. The phenotypes of the NIHL-susceptible and NIHL-resistant groups were significantly different (all p<0.001). After WES analysis and filtering, 12 risk variants contributing to NIHL susceptibility were identified and replicated. The meta-analyses showed that the A allele of CDH23 rs41281334 (odds ratio [OR]=1.506, 95% confidence interval [CI]=1.106-2.051) and the C allele of WHRN rs12339210 (OR=3.06, 95% CI=1.398-6.700) were significantly associated with increased risk of NIHL after adjustment for confounding factors. CONCLUSIONS This study revealed two genetic variants in CDH23 rs41281334 and WHRN rs12339210 that associated with NIHL risk, based on a promising approach for evaluating individual susceptibility using ML models.
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Affiliation(s)
- Zhuang Jiang
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai 200233, China; Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai 200233, China; Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai 200233, China
| | - Botao Fa
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Xunmiao Zhang
- Department of Occupational Disease, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Jiping Wang
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai 200233, China; Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai 200233, China; Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai 200233, China
| | - Yanmei Feng
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai 200233, China; Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai 200233, China; Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai 200233, China
| | - Haibo Shi
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai 200233, China; Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai 200233, China; Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai 200233, China
| | - Yue Zhang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Daoyuan Sun
- Department of Occupational Disease, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China.
| | - Hui Wang
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai 200233, China; Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai 200233, China; Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai 200233, China.
| | - Shankai Yin
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai 200233, China; Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai 200233, China; Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai 200233, China
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Serafim MSM, Dos Santos Júnior VS, Gertrudes JC, Maltarollo VG, Honorio KM. Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade. Expert Opin Drug Discov 2021; 16:961-975. [PMID: 33957833 DOI: 10.1080/17460441.2021.1918098] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Introduction: Drug design and discovery of new antivirals will always be extremely important in medicinal chemistry, taking into account known and new viral diseases that are yet to come. Although machine learning (ML) have shown to improve predictions on the biological potential of chemicals and accelerate the discovery of drugs over the past decade, new methods and their combinations have improved their performance and established promising perspectives regarding ML in the search for new antivirals.Areas covered: The authors consider some interesting areas that deal with different ML techniques applied to antivirals. Recent innovative studies on ML and antivirals were selected and analyzed in detail. Also, the authors provide a brief look at the past to the present to detect advances and bottlenecks in the area.Expert opinion: From classical ML techniques, it was possible to boost the searches for antivirals. However, from the emergence of new algorithms and the improvement in old approaches, promising results will be achieved every day, as we have observed in the case of SARS-CoV-2. Recent experience has shown that it is possible to use ML to discover new antiviral candidates from virtual screening and drug repurposing.
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Affiliation(s)
- Mateus Sá Magalhães Serafim
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | | | - Jadson Castro Gertrudes
- Departamento de Computação, Instituto de Ciências Exatas e Biológicas, Universidade Federal de Ouro Preto (UFOP), Ouro Preto, Brazil
| | - Vinícius Gonçalves Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Kathia Maria Honorio
- Escola de Artes, Ciências e Humanidades, Universidade de São Paulo (USP), São Paulo, Brazil.,Centro de Ciências Naturais e Humanas, Universidade Federal do ABC (UFABC), Santo André, Brazil
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Serafim MS, Gertrudes JC, Costa DM, Oliveira PR, Maltarollo VG, Honorio KM. Knowing and combating the enemy: a brief review on SARS-CoV-2 and computational approaches applied to the discovery of drug candidates. Biosci Rep 2021; 41:BSR20202616. [PMID: 33624754 PMCID: PMC7982772 DOI: 10.1042/bsr20202616] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 02/15/2021] [Accepted: 02/23/2021] [Indexed: 01/18/2023] Open
Abstract
Since the emergence of the new severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) at the end of December 2019 in China, and with the urge of the coronavirus disease 2019 (COVID-19) pandemic, there have been huge efforts of many research teams and governmental institutions worldwide to mitigate the current scenario. Reaching more than 1,377,000 deaths in the world and still with a growing number of infections, SARS-CoV-2 remains a critical issue for global health and economic systems, with an urgency for available therapeutic options. In this scenario, as drug repurposing and discovery remains a challenge, computer-aided drug design (CADD) approaches, including machine learning (ML) techniques, can be useful tools to the design and discovery of novel potential antiviral inhibitors against SARS-CoV-2. In this work, we describe and review the current knowledge on this virus and the pandemic, the latest strategies and computational approaches applied to search for treatment options, as well as the challenges to overcome COVID-19.
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Affiliation(s)
- Mateus S.M. Serafim
- Department of Microbiology, Biological Sciences Institute, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil
| | - Jadson C. Gertrudes
- Department of Computer Science, Federal University of Ouro Preto (UFOP), Ouro Preto, MG, Brazil
| | - Débora M.A. Costa
- Department of Pharmaceutical Products, Faculty of Pharmacy, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil
| | - Patricia R. Oliveira
- School of Arts, Sciences and Humanities, University of São Paulo (USP), 03828-000, São Paulo, SP, Brazil
| | - Vinicius G. Maltarollo
- Department of Pharmaceutical Products, Faculty of Pharmacy, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil
| | - Kathia M. Honorio
- School of Arts, Sciences and Humanities, University of São Paulo (USP), 03828-000, São Paulo, SP, Brazil
- Center for Natural and Human Sciences, Federal University of ABC (UFABC), Santo Andre, SP, Brazil
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Sarfraz M, Rauf A, Keller P, Qureshi AM. N, N′-dialkyl-2-thiobarbituric acid based sulfonamides as potential SARS-CoV-2 main protease inhibitors. CAN J CHEM 2021. [DOI: 10.1139/cjc-2020-0332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
An efficient methodology was developed to generate novel N,N′-dialkyl-2-thiobarbituric acid based sulfonamides S1–S4 in good to excellent yields (84%–95%). The synthesized compounds S1–S4 were docked to screen their in silico activities against two enzymes i.e., SARS-CoV-2 main protease enzyme with unliganded active site (2019-nCoV, coronavirus disease 2019, COVID-19) PDB ID: 6Y84 and SARS-CoV-2 Mpro PDB ID: 6LU7. Furthermore, some in silico physicochemical and physicokinetic properties were evaluated using the OSIRIS Property Explorer, Molinspiration property calculator, ADMET property calculator, and GUSAR to assess these compounds as potential candidates as lead compounds for the quest of SARS-CoV-2 main protease inhibitors. Molecular docking analyses of the synthesized compounds predicted that compound S3 is more potent as SARS-CoV-2 main protease inhibitor with binding energy –11.65 kcal/mol in comparison with reference inhibitor N3 (–10.95 kcal/mol), whereas compounds S1, S2, and S4 recorded comparable binding energies –9.89, –10.84, and –10.94 kcal/mol with reference inhibitor N3, which were much better than remdesivir (–9.85 kcal/mol). In case of SARS-CoV-2 Mpro, all compounds S1–S4 with docking energy values of –7.28, –8.38, –8.31, and –7.34 kcal/mol, respectively, were found to be potent in comparison with reference inhibitor N3 (–6.31 kcal/mol) and remdesivir (–6.33 kcal/mol). Ligand efficiency values against the target SARS-CoV-2 proteins, as well as α-glucosidase and DNA-(apurinic or apyrimidinic site) lyase inhibition results of these newly synthesized compounds, were also found to be promising.
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Affiliation(s)
- Muhammad Sarfraz
- Department of Chemistry, The Islamia University of Bahawalpur, 63100, Pakistan
| | - Abdul Rauf
- Department of Chemistry, The Islamia University of Bahawalpur, 63100, Pakistan
| | - Paul Keller
- School of Chemistry and Molecular Bioscience, Molecular Horizons, Illawarra health and Medical Research Institute, University of Wollongong, 2522, Australia
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Vlachakis D, Vlamos P. Mathematical Multidimensional Modelling and Structural Artificial Intelligence Pipelines Provide Insights for the Designing of Highly Specific AntiSARS-CoV2 Agents. MATHEMATICS IN COMPUTER SCIENCE 2021; 15. [PMCID: PMC8205651 DOI: 10.1007/s11786-021-00517-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
COVID19 is the most impactful pandemic of recent times worldwide. It is a highly infectious disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 virus), To date there is specific drug nor vaccination against COVID19. Therefor the need for novel and pioneering anti-COVID19 is of paramount importance. In this direction, computer-aided drug design constitutes a very promising antiviral approach for the discovery and analysis of drugs and molecules with biological activity against SARS-CoV2. In silico modelling takes advantage of the massive amounts of biological and chemical data available on the nature of the interactions between the targeted systems and molecules, as well as the rapid progress of computational tools and software. Herein, we describe the potential of the merging of mathematical modelling, artificial intelligence and learning techniques into seamless computational pipelines for the rapid and efficient discovery and design of potent anti- SARS-CoV-2 modulators.
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Affiliation(s)
- Dimitrios Vlachakis
- Laboratory of Genetics, Department of Biotechnology, Genetics and Computational Biology Group, School of Applied Biology and Biotechnology, Agricultural University of Athens, Iera Odos 75 Str. GR11855, Athens, Greece
- Laboratory of Molecular Endocrinology, Division of Endocrinology and Metabolism, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, Soranou Ephessiou Str. GR11527, Athens, Greece
- University Research Institute of Maternal and Child Health and Precision Medicine, Medical School, National and Kapodistrian University of Athens, Thivon 1 & Papadiamantopoulou Str. GR11527, Athens, Greece
| | - Panayiotis Vlamos
- Department of Informatics, Ionian University, Plateia Tsirigoti 7, 49100 Corfu, Greece
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Coupling Square Wave Anodic Stripping Voltammetry with Support Vector Regression to Detect the Concentration of Lead in Soil under the Interference of Copper Accurately. SENSORS 2020; 20:s20236792. [PMID: 33261107 PMCID: PMC7731166 DOI: 10.3390/s20236792] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 11/19/2020] [Accepted: 11/25/2020] [Indexed: 12/02/2022]
Abstract
In this study, an effective method for accurately detecting Pb(II) concentration was developed by coupling square wave anodic stripping voltammetry (SWASV) with support vector regression (SVR) based on a bismuth-film modified electrode. The interference of different Cu2+ contents on the SWASV signals of Pb2+ was investigated, and a nonlinear relationship between Pb2+ concentration and the peak currents of Pb2+ and Cu2+ was determined. Thus, an SVR model with two inputs (i.e., peak currents of Pb2+ and Cu2+) and one output (i.e., Pb2+ concentration) was trained to quantify the above nonlinear relationship. The SWASV measurement conditions and the SVR parameters were optimized. In addition, the SVR mode, multiple linear regression model, and direct calibration mode were compared to verify the detection performance by using the determination coefficient (R2) and root-mean-square error (RMSE). Results showed that the SVR model with R2 and RMSE of the test dataset of 0.9942 and 1.1204 μg/L, respectively, had better detection accuracy than other models. Lastly, real soil samples were applied to validate the practicality and accuracy of the developed method for the detection of Pb2+ with approximately equal detection results to the atomic absorption spectroscopy method and a satisfactory average recovery rate of 98.70%. This paper provided a new method for accurately detecting the concentration of heavy metals (HMs) under the interference of non-target HMs for environmental monitoring.
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Patel L, Shukla T, Huang X, Ussery DW, Wang S. Machine Learning Methods in Drug Discovery. Molecules 2020; 25:E5277. [PMID: 33198233 PMCID: PMC7696134 DOI: 10.3390/molecules25225277] [Citation(s) in RCA: 109] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 11/04/2020] [Accepted: 11/09/2020] [Indexed: 12/30/2022] Open
Abstract
The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.
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Affiliation(s)
- Lauv Patel
- Chemistry Department, University of Arkansas at Little Rock, Little Rock, AR 72204, USA; (L.P.); (T.S.)
| | - Tripti Shukla
- Chemistry Department, University of Arkansas at Little Rock, Little Rock, AR 72204, USA; (L.P.); (T.S.)
| | - Xiuzhen Huang
- Department of Computer Science, Arkansas State University, Jonesboro, AR 72467, USA;
| | - David W. Ussery
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA;
| | - Shanzhi Wang
- Chemistry Department, University of Arkansas at Little Rock, Little Rock, AR 72204, USA; (L.P.); (T.S.)
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Vahedi N, Mohammadhosseini M, Nekoei M. QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches. CURR ANAL CHEM 2020. [DOI: 10.2174/1573411016999200518083359] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily
present in eukaryotes.
Methods:
In the present report, some efficient linear and non-linear methods including multiple linear
regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully
used to develop and establish quantitative structure-activity relationship (QSAR) models
capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP
inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set
and selection of the training and test sets. A genetic algorithm (GA) variable selection method was
employed to select the optimal subset of descriptors that have the most significant contributions to
the overall inhibitory activity from the large pool of calculated descriptors.
Results:
The accuracy and predictability of the proposed models were further confirmed using crossvalidation,
validation through an external test set and Y-randomization (chance correlations) approaches.
Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed
models. The results revealed that non-linear modeling approaches, including SVM and ANN
could provide much more prediction capabilities.
Conclusion:
Among the constructed models and in terms of root mean square error of predictions
(RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for
the training set, the predictive power of the GA-SVM approach was better. However, compared with
MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.
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Affiliation(s)
- Nafiseh Vahedi
- Department of Chemistry, College of Basic Sciences, Shahrood Branch, Islamic Azad University, Shahrood, Iran
| | - Majid Mohammadhosseini
- Department of Chemistry, College of Basic Sciences, Shahrood Branch, Islamic Azad University, Shahrood, Iran
| | - Mehdi Nekoei
- Department of Chemistry, College of Basic Sciences, Shahrood Branch, Islamic Azad University, Shahrood, Iran
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Alberto AVP, da Silva Ferreira NC, Soares RF, Alves LA. Molecular Modeling Applied to the Discovery of New Lead Compounds for P2 Receptors Based on Natural Sources. Front Pharmacol 2020; 11:01221. [PMID: 33117147 PMCID: PMC7553047 DOI: 10.3389/fphar.2020.01221] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 07/27/2020] [Indexed: 12/24/2022] Open
Abstract
P2 receptors are a family of transmembrane receptors activated by nucleotides and nucleosides. Two classes have been described in mammals, P2X and P2Y, which are implicated in various diseases. Currently, only P2Y12 has medicines approved for clinical use as antiplatelet agents and natural products have emerged as a source of new drugs with action on P2 receptors due to the diversity of chemical structures. In drug discovery, in silico virtual screening (VS) techniques have become popular because they have numerous advantages, which include the evaluation of thousands of molecules against a target, usually proteins, faster and cheaper than classical high throughput screening (HTS). The number of studies using VS techniques has been growing in recent years and has led to the discovery of new molecules of natural origin with action on different P2X and P2Y receptors. Using different algorithms it is possible to obtain information on absorption, distribution, metabolism, toxicity, as well as predictions on biological activity and the lead-likeness of the selected hits. Selected biomolecules may then be tested by molecular dynamics and, if necessary, rationally designed or modified to improve their interaction for the target. The algorithms of these in silico tools are being improved to permit the precision development of new drugs and, in the future, this process will take the front of drug development against some central nervous system (CNS) disorders. Therefore, this review discusses the methodologies of in silico tools concerning P2 receptors, as well as future perspectives and discoveries, such as the employment of artificial intelligence in drug discovery.
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Affiliation(s)
- Anael Viana Pinto Alberto
- Laboratory of Cellular Communication, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | | | - Rafael Ferreira Soares
- Laboratory of Functional Genomics and Bioinformatics, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Luiz Anastacio Alves
- Laboratory of Cellular Communication, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
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Serafim MSM, Kronenberger T, Oliveira PR, Poso A, Honório KM, Mota BEF, Maltarollo VG. The application of machine learning techniques to innovative antibacterial discovery and development. Expert Opin Drug Discov 2020; 15:1165-1180. [PMID: 32552005 DOI: 10.1080/17460441.2020.1776696] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION After the initial wave of antibiotic discovery, few novel classes of antibiotics have emerged, with the latest dating back to the 1980's. Furthermore, the pace of antibiotic drug discovery is unable to keep up with the increasing prevalence of antibiotic drug resistance. However, the increasing amount of available data promotes the use of machine learning techniques (MLT) in drug discovery projects (e.g. construction of regression/classification models and ranking/virtual screening of compounds). AREAS COVERED In this review, the authors cover some of the applications of MLT in medicinal chemistry, focusing on the development of new antibiotics, the prediction of resistance and its mechanisms. The aim of this review is to illustrate the main advantages and disadvantages and the major trends from studies over the past 5 years. EXPERT OPINION The application of MLT to antibacterial drug discovery can aid the selection of new and potent lead compounds, with desirable pharmacokinetic and toxic profiles for further optimization. The increasing volume of available data along with the constant improvement in computational power and algorithms has meant that we are experiencing a transition in the way we face modern issues such as drug resistance, where our decisions are data-driven and experiments can be focused by data-suggested hypotheses.
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Affiliation(s)
- Mateus Sá Magalhães Serafim
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG) , Belo Horizonte, Brazil
| | - Thales Kronenberger
- Department of Internal Medicine VIII, University Hospital of Tübingen , Tübingen, Germany
| | | | - Antti Poso
- Department of Internal Medicine VIII, University Hospital of Tübingen , Tübingen, Germany.,School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland , Kuopio, Finland
| | - Káthia Maria Honório
- Escola de Artes, Ciências e Humanidades, Universidade de São Paulo (USP) , São Paulo, Brazil.,Centro de Ciências Naturais e Humanas, Universidade Federal do ABC , Santo André, Brazil
| | - Bruno Eduardo Fernandes Mota
- Departamento de Análises Clínicas e Toxicológicas, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG) , Belo Horizonte, Brazil
| | - Vinícius Gonçalves Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG) , Belo Horizonte, Brazil
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Artificial Intelligence Algorithms for Discovering New Active Compounds Targeting TRPA1 Pain Receptors. AI 2020. [DOI: 10.3390/ai1020018] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Transient receptor potential ankyrin 1 (TRPA1) is a ligand-gated calcium channel activated by cold temperatures and by a plethora of electrophilic environmental irritants (allicin, acrolein, mustard-oil) and endogenously oxidized lipids (15-deoxy-∆12, 14-prostaglandin J2 and 5, 6-eposyeicosatrienoic acid). These oxidized lipids work as agonists, making TRPA1 a key player in inflammatory and neuropathic pain. TRPA1 antagonists acting as non-central pain blockers are a promising choice for future treatment of pain-related conditions having advantages over current therapeutic choices A large variety of in silico methods have been used in drug design to speed up the development of new active compounds such as molecular docking, quantitative structure-activity relationship models (QSAR), and machine learning classification algorithms. Artificial intelligence methods can significantly improve the drug discovery process and it is an attractive field that can bring together computer scientists and experts in drug development. In our paper, we aimed to develop three machine learning algorithms frequently used in drug discovery research: feedforward neural networks (FFNN), random forests (RF), and support vector machines (SVM), for discovering novel TRPA1 antagonists. All three machine learning methods used the same class of independent variables (multilevel neighborhoods of atoms descriptors) as prediction of activity spectra for substances (PASS) software. The model with the highest accuracy and most optimal performance metrics was the random forest algorithm, showing 99% accuracy and 0.9936 ROC AUC. Thus, our study emphasized that simpler and robust machine learning algorithms such as random forests perform better in correctly classifying TRPA1 antagonists since the dimension of the dependent variables dataset is relatively modest.
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Arian R, Hariri A, Mehridehnavi A, Fassihi A, Ghasemi F. Protein kinase inhibitors' classification using K-Nearest neighbor algorithm. Comput Biol Chem 2020; 86:107269. [PMID: 32413830 DOI: 10.1016/j.compbiolchem.2020.107269] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 03/15/2020] [Accepted: 04/20/2020] [Indexed: 10/24/2022]
Abstract
Protein kinases are enzymes acting as a source of phosphate through ATP to regulate protein biological activities by phosphorylating groups of specific amino acids. For that reason, inhibiting protein kinases with an active small molecule plays a significant role in cancer treatment. To achieve this aim, computational drug design, especially QSAR model, is one of the best economical approaches to reduce time and save in costs. In this respect, active inhibitors are attempted to be distinguished from inactive ones using hybrid QSAR model. Therefore, genetic algorithm and K-Nearest Neighbor method were suggested as a dimensional reduction and classification model, respectively. Finally, to evaluate the proposed model's performance, support vector machine and Naïve Bayesian algorithm were examined. The outputs of the proposed model demonstrated significant superiority to other QSAR models.
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Affiliation(s)
- Roya Arian
- Department of Bioinformatics and Systems Biology, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Amirali Hariri
- School of Pharmacology and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Alireza Mehridehnavi
- Department of Bioinformatics and Systems Biology, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Afshin Fassihi
- School of Pharmacology and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Fahimeh Ghasemi
- Department of Bioinformatics and Systems Biology, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
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