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Schieferdecker S, Rottach F, Vock E. In Silico Prediction of Oral Acute Rodent Toxicity Using Consensus Machine Learning. J Chem Inf Model 2024; 64:3114-3122. [PMID: 38498695 DOI: 10.1021/acs.jcim.4c00056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
Acute oral toxicity (AOT) is required for the classification and labeling of chemicals according to the global harmonized system (GHS). Acute oral toxicity studies are optimized to minimize the use of animals. However, with the advent of the three Rs principles and machine learning in toxicology, alternative in silico methods became a reasonable alternative approach for addressing the AOT of new chemical matter. Here, we describe the compilation of AOT data from a commercial database and the development of a consensus classification model after evaluating different combinations of molecular representations and machine learning algorithms. The model shows significantly better performance compared to publicly available AOT models. Its performance was evaluated on an external validation data set, which was compiled from the literature, and an applicability domain was deduced.
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Affiliation(s)
| | - Florian Rottach
- Boehringer Ingelheim Pharma GmbH & Co. KG, 88397 Biberach, Germany
| | - Esther Vock
- Boehringer Ingelheim Pharma GmbH & Co. KG, 88397 Biberach, Germany
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2
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Fan J, Shi S, Xiang H, Fu L, Duan Y, Cao D, Lu H. Predicting Elimination of Small-Molecule Drug Half-Life in Pharmacokinetics Using Ensemble and Consensus Machine Learning Methods. J Chem Inf Model 2024; 64:3080-3092. [PMID: 38563433 DOI: 10.1021/acs.jcim.3c02030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Half-life is a significant pharmacokinetic parameter included in the excretion phase of absorption, distribution, metabolism, and excretion. It is one of the key factors for the successful marketing of drug candidates. Therefore, predicting half-life is of great significance in drug design. In this study, we employed eXtreme Gradient Boosting (XGboost), randomForest (RF), gradient boosting machine (GBM), and supporting vector machine (SVM) to build quantitative structure-activity relationship (QSAR) models on 3512 compounds and evaluated model performance by using root-mean-square error (RMSE), R2, and mean absolute error (MAE) metrics and interpreted features by SHapley Additive exPlanation (SHAP). Furthermore, we developed consensus models through integrating four individual models and validated their performance using a Y-randomization test and applicability domain analysis. Finally, matched molecular pair analysis was used to extract the transformation rules. Our results revealed that XGboost outperformed other individual models (RMSE = 0.176, R2 = 0.845, MAE = 0.141). The consensus model integrating all four models continued to enhance prediction performance (RMSE = 0.172, R2 = 0.856, MAE = 0.138). We evaluated the reliability, robustness, and generalization ability via Y-randomization test and applicability domain analysis. Meanwhile, we utilized SHAP to interpret features and employed matched molecular pair analysis to extract chemical transformation rules that provide suggestions for optimizing drug structure. In conclusion, we believe that the consensus model developed in this study serve as a reliable tool to evaluate half-life in drug discovery, and the chemical transformation rules concluded in this study could provide valuable suggestions in drug discovery.
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Affiliation(s)
- Jianing Fan
- Health Management Center, Third Xiangya Hospital of Central South University, Changsha, Hunan 410013, P. R. China
- Department of Cardiology, Third Xiangya Hospital of Central South University, Changsha, Hunan 410013, P. R. China
| | - Shaohua Shi
- School of Chinese Medicine, Hong Kong Baptist University, Kowloon, Hong Kong 999077, P. R. China
| | - Hong Xiang
- Center for Experimental Medicine, Third Xiangya Hospital of Central South University, Changsha, Hunan 410013, P. R. China
| | - Li Fu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan 410013, P. R. China
| | - Yanjing Duan
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan 410013, P. R. China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan 410013, P. R. China
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Kowloon, Hong Kong SAR 999077, P. R. China
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan P. R. China
| | - Hongwei Lu
- Health Management Center, Third Xiangya Hospital of Central South University, Changsha, Hunan 410013, P. R. China
- Department of Cardiology, Third Xiangya Hospital of Central South University, Changsha, Hunan 410013, P. R. China
- Center for Experimental Medicine, Third Xiangya Hospital of Central South University, Changsha, Hunan 410013, P. R. China
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3
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Vinh T, Nguyen L, Trinh QH, Nguyen-Vo TH, Nguyen BP. Predicting Cardiotoxicity of Molecules Using Attention-Based Graph Neural Networks. J Chem Inf Model 2024; 64:1816-1827. [PMID: 38438914 DOI: 10.1021/acs.jcim.3c01286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Abstract
In drug discovery, the search for new and effective medications is often hindered by concerns about toxicity. Numerous promising molecules fail to pass the later phases of drug development due to strict toxicity assessments. This challenge significantly increases the cost, time, and human effort needed to discover new therapeutic molecules. Additionally, a considerable number of drugs already on the market have been withdrawn or re-evaluated because of their unwanted side effects. Among the various types of toxicity, drug-induced heart damage is a severe adverse effect commonly associated with several medications, especially those used in cancer treatments. Although a number of computational approaches have been proposed to identify the cardiotoxicity of molecules, the performance and interpretability of the existing approaches are limited. In our study, we proposed a more effective computational framework to predict the cardiotoxicity of molecules using an attention-based graph neural network. Experimental results indicated that the proposed framework outperformed the other methods. The stability of the model was also confirmed by our experiments. To assist researchers in evaluating the cardiotoxicity of molecules, we have developed an easy-to-use online web server that incorporates our model.
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Affiliation(s)
- Tuan Vinh
- Department of Chemistry, Emory University, 201 Dowman Drive, Atlanta, Georgia 30322-1007, United States
| | - Loc Nguyen
- School of Mathematics and Statistics, Victoria University of Wellington, Kelburn Parade, Wellington 6012, New Zealand
| | - Quang H Trinh
- School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi 100000, Vietnam
| | - Thanh-Hoang Nguyen-Vo
- School of Mathematics and Statistics, Victoria University of Wellington, Kelburn Parade, Wellington 6012, New Zealand
- School of Innovation, Design and Technology, Wellington Institute of Technology, 21 Kensington Avenue, Lower Hutt 5012, New Zealand
| | - Binh P Nguyen
- School of Mathematics and Statistics, Victoria University of Wellington, Kelburn Parade, Wellington 6012, New Zealand
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Souza MAD, Rodrigues LG, Rocha JE, de Freitas TS, Bandeira PN, Marinho MM, Nunes da Rocha M, Marinho ES, Honorato Barreto AC, Coutinho HDM, Silva LMA, Julião MSDS, Marques Canuto K, Marques da Fonseca A, Teixeira AMR, Dos Santos HS. Synthesis, structural, characterization, antibacterial and antibiotic modifying activity, ADMET study, molecular docking and dynamics of chalcone ( E)-1-(4-aminophenyl)-3-(4-nitrophenyl)prop-2-en-1-one in strains of Staphylococcus aureus carrying NorA and MepA efflux pumps. J Biomol Struct Dyn 2024; 42:1670-1691. [PMID: 37222682 DOI: 10.1080/07391102.2023.2213777] [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: 11/15/2022] [Accepted: 04/05/2023] [Indexed: 05/25/2023]
Abstract
Chalcones have an open chain flavonoid structure that can be obtained from natural sources or by synthesis and are widely distributed in fruits, vegetables, and tea. They have a simple and easy to handle structure due to the α-β-unsaturated bridge responsible for most biological activities. The facility to synthesize chalcones combined with its efficient in combating serious bacterial infections make these compounds important agents in the fight against microorganisms. In this work, the chalcone (E)-1-(4-aminophenyl)-3-(4-nitrophenyl)prop-2-en-1-one (HDZPNB) was characterized by spectroscopy and electronic methods. In addition, microbiological tests were performed to investigate the modulator potential and efflux pump inhibition on S. aureus multi-resistant strains. The modulating effect of HDZPNB chalcone in association with the antibiotic norfloxacin, on the resistance of the S. aureus 1199 strain, resulted in increase the MIC. In addition, when HDZPNB was associated with ethidium bromide (EB), it caused an increase in the MIC value, thus not inhibiting the efflux pump. For the strain of S. aureus 1199B, carrying the NorA pump, the HDZPNB associated with norfloxacin showed no modulatory, and when the chalcone was used in association with EB, it had no inhibitory effect on the efflux pump. For the tested strain of S. aureus K2068, which carries the MepA pump, it can be observed that the chalcone together the antibiotic resulted in an increase the MIC. On the other hand, when chalcone was used in association with EB, it caused a decrease in bromide MIC, equal to the reduction caused by standard inhibitors. Thus, these results indicate that the HDZPNB could also act as an inhibitor of the S. aureus gene overexpressing pump MepA. The molecular docking reveals that chalcone has a good binding energies -7.9 for HDZPNB/MepA complexes, molecular dynamics simulations showed that Chalcone/MetA complexes showed good stability of the structure in an aqueous solution, and ADMET study showed that the chalcone has a good oral bioavailability, high passive permeability, low risk of efflux, low clearance rate and low toxic risk by ingestion. The microbiological tests show that the chalcone can be used as a possible inhibitor of the Mep A efflux pump.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Mikael Amaro de Souza
- Graduate Program in Biological Chemistry, Department of Biological Chemistry, Regional University of Cariri, Crato, CE, Brazil
| | - Leilane Gomes Rodrigues
- Science and Technology Centre, Course of Chemistry, State University Vale do Acaraú, Sobral, CE, Brazil
| | - Janaina Esmeraldo Rocha
- Graduate Program in Biological Chemistry, Department of Biological Chemistry, Regional University of Cariri, Crato, CE, Brazil
| | - Thiago Sampaio de Freitas
- Graduate Program in Biological Chemistry, Department of Biological Chemistry, Regional University of Cariri, Crato, CE, Brazil
| | - Paulo Nogueira Bandeira
- Science and Technology Centre, Course of Chemistry, State University Vale do Acaraú, Sobral, CE, Brazil
| | - Márcia Machado Marinho
- Science and Technology Centre, Course of Chemistry, State University Vale do Acaraú, Sobral, CE, Brazil
| | | | | | | | - Henrique Douglas Melo Coutinho
- Graduate Program in Biological Chemistry, Department of Biological Chemistry, Regional University of Cariri, Crato, CE, Brazil
| | | | - Murilo Sergio da Silva Julião
- Science and Technology Centre, Course of Chemistry, State University Vale do Acaraú, Sobral, CE, Brazil
- Graduate Program in Natural Science, State University of Ceará, Fortaleza, CE, Brazil
| | - Kirley Marques Canuto
- Multiusuary Laboratory of Natural Products Chemistry, Embrapa Tropical Agroindustry, Fortaleza, CE, Brazil
| | - Aluísio Marques da Fonseca
- Academic Master's Degree in Sociobiodiversity and Sustainable Technologies - MASTS, Institute of Engineering and Development Sustainable, University of International Integration of Afro-Brazilian Lusofonia, Acarape, CE, Brazil
| | - Alexandre Magno Rodrigues Teixeira
- Graduate Program in Biological Chemistry, Department of Biological Chemistry, Regional University of Cariri, Crato, CE, Brazil
- Graduate Program in Natural Science, State University of Ceará, Fortaleza, CE, Brazil
| | - Hélcio Silva Dos Santos
- Graduate Program in Biological Chemistry, Department of Biological Chemistry, Regional University of Cariri, Crato, CE, Brazil
- Science and Technology Centre, Course of Chemistry, State University Vale do Acaraú, Sobral, CE, Brazil
- Graduate Program in Natural Science, State University of Ceará, Fortaleza, CE, Brazil
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5
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Ren ZH, Yu CQ, Li LP, You ZH, Li ZW, Zhang SW, Zeng X, Shang YF. SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution. J Chem Inf Model 2024; 64:238-249. [PMID: 38103039 DOI: 10.1021/acs.jcim.3c01665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
Drug repositioning plays a key role in disease treatment. With the large-scale chemical data increasing, many computational methods are utilized for drug-disease association prediction. However, most of the existing models neglect the positive influence of non-Euclidean data and multisource information, and there is still a critical issue for graph neural networks regarding how to set the feature diffuse distance. To solve the problems, we proposed SiSGC, which makes full use of the biological knowledge information as initial features and learns the structure information from the constructed heterogeneous graph with the adaptive selection of the information diffuse distance. Then, the structural features are fused with the denoised similarity information and fed to the advanced classifier of CatBoost to make predictions. Three different data sets are used to confirm the robustness and generalization of SiSGC under two splitting strategies. Experiment results demonstrate that the proposed model achieves superior performance compared with the six leading methods and four variants. Our case study on breast neoplasms further indicates that SiSGC is trustworthy and robust yet simple. We also present four drugs for breast cancer treatment with high confidence and further give an explanation for demonstrating the rationality. There is no doubt that SiSGC can be used as a beneficial supplement for drug repositioning.
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Affiliation(s)
- Zhong-Hao Ren
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Chang-Qing Yu
- School of Information Engineering, Xijing University, Xi'an 710123, China
| | - Li-Ping Li
- College of Agriculture and Forestry, Longdong University, Qingyang 745000, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China
| | - Zheng-Wei Li
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
| | - Shan-Wen Zhang
- School of Information Engineering, Xijing University, Xi'an 710123, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Yi-Fan Shang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
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Liu Z, Gao J, Li C, Xu L, Lv X, Deng H, Gao Y, Wang H, Li H, Wang Z. Application of QSAR models for acute toxicity of tetrazole compounds administrated orally and intraperitoneally in rat and mouse. Toxicology 2023; 500:153679. [PMID: 38042272 DOI: 10.1016/j.tox.2023.153679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 11/10/2023] [Accepted: 11/20/2023] [Indexed: 12/04/2023]
Abstract
Tetrazoles and their derivatives possess various biological activities, such as antibacterial, anti-fungal, and other activities. However, these compounds may induce specific cumulative and toxic effects in living organisms. Therefore, quantitative structure-activity relationship (QSAR) models were constructed to study the acute oral toxicity of tetrazoles in rats and mice. The toxicity data of 111 tetrazole compounds were collected using the ChemIDplus, ChEMBL and ECHA databases as response variables, while the PaDEL-descriptor generated the 2D descriptors as independent variables. The models were developed and validated following the OECD guidelines by the DTC-QSAR tool. Three QSAR models were successfully established for the oral routes of rat and mouse and the intraperitoneal route of mouse, respectively. The scatter plots showed high consistency between the training and test data sets. All the models successfully met the external and internal validation criteria. Most of the descriptors kept in the final models exhibited positive correlations with toxicity, whereas only 6 descriptors exhibited negative associations. Several chemicals were identified as response or structural outliers, based on the standardized residuals and leverage values. In conclusion, the findings of this investigation demonstrate that the proposed QSAR models hold promise in forecasting the acute toxicity of recently developed or synthesized tetrazole compounds, thereby mitigating potential risks to human health and the environment.
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Affiliation(s)
- Zhiyong Liu
- Toxicology Research Center, Xi'an Key Laboratory of Toxicology and Biological effect, Institute for Hygiene of Ordnance Industry, Xi'an, Shaanxi 710065, China.
| | - Junhong Gao
- Toxicology Research Center, Xi'an Key Laboratory of Toxicology and Biological effect, Institute for Hygiene of Ordnance Industry, Xi'an, Shaanxi 710065, China.
| | - Cunzhi Li
- Toxicology Research Center, Xi'an Key Laboratory of Toxicology and Biological effect, Institute for Hygiene of Ordnance Industry, Xi'an, Shaanxi 710065, China
| | - Lihong Xu
- Department of Infectious Disease Supervision, Xi'an Health Supervision Institute, Xi'an, Shaanxi 710018, China
| | - Xiaoqiang Lv
- Toxicology Research Center, Xi'an Key Laboratory of Toxicology and Biological effect, Institute for Hygiene of Ordnance Industry, Xi'an, Shaanxi 710065, China
| | - Hui Deng
- Toxicology Research Center, Xi'an Key Laboratory of Toxicology and Biological effect, Institute for Hygiene of Ordnance Industry, Xi'an, Shaanxi 710065, China
| | - Yongchao Gao
- Toxicology Research Center, Xi'an Key Laboratory of Toxicology and Biological effect, Institute for Hygiene of Ordnance Industry, Xi'an, Shaanxi 710065, China
| | - Hong Wang
- Toxicology Research Center, Xi'an Key Laboratory of Toxicology and Biological effect, Institute for Hygiene of Ordnance Industry, Xi'an, Shaanxi 710065, China
| | - Huan Li
- Toxicology Research Center, Xi'an Key Laboratory of Toxicology and Biological effect, Institute for Hygiene of Ordnance Industry, Xi'an, Shaanxi 710065, China
| | - Zhigang Wang
- Toxicology Research Center, Xi'an Key Laboratory of Toxicology and Biological effect, Institute for Hygiene of Ordnance Industry, Xi'an, Shaanxi 710065, China
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7
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Oh KK, Choi I, Gupta H, Raja G, Sharma SP, Won SM, Jeong JJ, Lee SB, Cha MG, Kwon GH, Jeong MK, Min BH, Hyun JY, Eom JA, Park HJ, Yoon SJ, Choi MR, Kim DJ, Suk KT. New insight into gut microbiota-derived metabolites to enhance liver regeneration via network pharmacology study. ARTIFICIAL CELLS, NANOMEDICINE, AND BIOTECHNOLOGY 2023; 51:1-12. [PMID: 36562095 DOI: 10.1080/21691401.2022.2155661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
We intended to identify favourable metabolite(s) and pharmacological mechanism(s) of gut microbiota (GM) for liver regeneration (LR) through network pharmacology. We utilized the gutMGene database to obtain metabolites of GM, and targets associated with metabolites as well as LR-related targets were identified using public databases. Furthermore, we performed a molecular docking assay on the active metabolite(s) and target(s) to verify the network pharmacological concept. We mined a total of 208 metabolites in the gutMGene database and selected 668 targets from the SEA (1,256 targets) and STP (947 targets) databases. Finally, 13 targets were identified between 61 targets and the gutMGene database (243 targets). Protein-protein interaction network analysis showed that AKT1 is a hub target correlated with 12 additional targets. In this study, we describe the potential microbe from the microbiota (E. coli), chemokine signalling pathway, AKT1 and myricetin that accelerate LR, providing scientific evidence for further clinical trials.
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Affiliation(s)
- Ki-Kwang Oh
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Ickwon Choi
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Haripriya Gupta
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Ganesan Raja
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Satya Priya Sharma
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Sung-Min Won
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Jin-Ju Jeong
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Su-Been Lee
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Min-Gi Cha
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Goo-Hyun Kwon
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Min-Kyo Jeong
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Byeong-Hyun Min
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Ji-Ye Hyun
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Jung-A Eom
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Hee-Jin Park
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Sang-Jun Yoon
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Mi-Ran Choi
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Dong Joon Kim
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Ki-Tae Suk
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
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Czeleń P, Jeliński T, Skotnicka A, Szefler B, Szupryczyński K. ADMET and Solubility Analysis of New 5-Nitroisatine-Based Inhibitors of CDK2 Enzymes. Biomedicines 2023; 11:3019. [PMID: 38002019 PMCID: PMC10669656 DOI: 10.3390/biomedicines11113019] [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: 10/23/2023] [Revised: 11/03/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
The development of new substances with the ability to interact with a biological target is only the first stage in the process of the creation of new drugs. The 5-nitroisatin derivatives considered in this study are new inhibitors of cyclin-dependent kinase 2 (CDK2) intended for anticancer therapy. The research, carried out based on the ADMET (absorption, distribution, metabolism, excretion, toxicity) methods, allowed a basic assessment of the physicochemical parameters of the tested drugs to be made. The collected data clearly showed the good oral absorption, membrane permeability, and bioavailability of the tested substances. The analysis of the metabolite activity and toxicity of the tested drugs did not show any critical hazards in terms of the toxicity of the tested substances. The substances' low solubility in water meant that extended studies tested compounds were required, which helped to select solvents with a high dissolving capacity of the examined substances, such as DMSO or NMP. The use of aqueous binary mixtures based on these two solvents allowed a relatively high solubility with significantly reduced toxicity and environmental index compared to pure solvents to be maintained, which is important in the context of the search for green solvents for pharmaceutical use.
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Affiliation(s)
- Przemysław Czeleń
- Department of Physical Chemistry, Faculty of Pharmacy, Collegium Medicum, Nicolaus Copernicus University, Kurpinskiego 5, 85-096 Bydgoszcz, Poland
| | - Tomasz Jeliński
- Department of Physical Chemistry, Faculty of Pharmacy, Collegium Medicum, Nicolaus Copernicus University, Kurpinskiego 5, 85-096 Bydgoszcz, Poland
| | - Agnieszka Skotnicka
- Faculty of Chemical Technology and Engineering, Bydgoszcz University of Science and Technology, Seminaryjna 3, 85-326 Bydgoszcz, Poland
| | - Beata Szefler
- Department of Physical Chemistry, Faculty of Pharmacy, Collegium Medicum, Nicolaus Copernicus University, Kurpinskiego 5, 85-096 Bydgoszcz, Poland
| | - Kamil Szupryczyński
- Doctoral School of Medical and Health Sciences, Faculty of Pharmacy, Collegium Medicum, Nicolaus Copernicus University, Jagiellońska 13, 85-067 Bydgoszcz, Poland
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9
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Bettadj FZY, Benchouk W. Computer-aided analysis for identification of novel analogues of ketoprofen based on molecular docking, ADMET, drug-likeness and DFT studies for the treatment of inflammation. J Biomol Struct Dyn 2023; 41:9915-9930. [PMID: 36444967 DOI: 10.1080/07391102.2022.2148750] [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: 08/11/2022] [Accepted: 11/12/2022] [Indexed: 11/30/2022]
Abstract
Computer-based drug design is increasingly used in strategies for discovering new molecules for therapeutic purposes. The targeted drug is ketoprofen (KTP), which belongs to the family of non-steroidal anti-inflammatory drugs, which are widely used for the treatment of pain, fever, inflammation and certain types of cancers. In an attempt to rationalize the search for 72 new potential anti-inflammatory compounds on the COX-2 enzyme, we carried out an in silico protocol that successfully combines molecular docking towards COX-2 receptor (5F1A), ADMET pharmacokinetic parameters, drug-likeness rules and molecular electrostatic potential (MEP). It was found that six of the compounds analyzed satisfy with the associated values to physico-chemical properties as key evaluation parameters for the drug-likeness and demonstrate a hydrophobic character which makes their solubility in aqueous media difficult and easy in lipids. All the compounds presented good ADMET profile and they showed an interaction with the amino acids responsible for anti-inflammatory activity of the COX-2 isoenzyme. The calculation of the MEP of the six analogues reveals new preferential sites involving the formation of new bonds. Consequently, this result allowed us to understand the origin of the potential increase in the anti-inflammatory activity of the candidates. Finally, it was obtained that six compounds have a binding mode, binding energy, and stability in the active site of COX-2 like the reference drug ketoprofen, suggesting that these compounds could become a powerful candidate in the inhibition of the COX-2 enzyme.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Fatima Zohra Yasmine Bettadj
- Laboratory of Applied Thermodynamics and Molecular Modeling, Department of Chemistry, Faculty of Science, University of Tlemcen, Tlemcen, Algeria
| | - Wafaa Benchouk
- Laboratory of Applied Thermodynamics and Molecular Modeling, Department of Chemistry, Faculty of Science, University of Tlemcen, Tlemcen, Algeria
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10
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Zhao D, Guo K, Zhang Q, Wu Y, Ma C, He W, Jin X, Zhang X, Wang Y, Lin S, Shang H. Mechanism of XiJiaQi in the treatment of chronic heart failure: Integrated analysis by pharmacoinformatics, molecular dynamics simulation, and SPR validation. Comput Biol Med 2023; 166:107479. [PMID: 37783074 DOI: 10.1016/j.compbiomed.2023.107479] [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: 06/20/2023] [Revised: 08/27/2023] [Accepted: 09/15/2023] [Indexed: 10/04/2023]
Abstract
OBJECTIVE Chronic heart failure (CHF) is a complicated clinical syndrome with a high mortality rate. XiJiaQi (XJQ) is a traditional Chinese medicine used in the clinical treatment of CHF, but its bioactive components and their modes of action remain unknown. This study was designed to unravel the molecular mechanism of XJQ in the treatment of CHF using multiple computer-assisted and experimental methods. METHODS Pharmacoinformatics-based methods were used to explore the active components and targets of XJQ in the treatment of CHF. ADMETlab was then utilized to evaluate the pharmacokinetic and toxicological properties of core components. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were to explore the underlying mechanism of XJQ treatment. Molecular docking, surface plasmon resonance (SPR), and molecular dynamics (MD) were employed to evaluate the binding of active components to putative targets. RESULTS Astragaloside IV, formononetin, kirenol, darutoside, periplocin and periplocymarin were identified as core XJQ-related components, and IL6 and STAT3 were identified as core XJQ targets. ADME/T results indicated that periplocin and periplocymarin may have potential toxicity. GO and KEGG pathway analyses revealed that XJQ mainly intervenes in inflammation, apoptosis, diabetes, and atherosclerosis-related biological pathways. Molecular docking and SPR revealed that formononetin had a high affinity with IL6 and STAT3. Furthermore, MD simulation confirmed that formononetin could firmly bind to the site 2 region of IL6 and the DNA binding domain of STAT3. CONCLUSION This study provides a mechanistic rationale for the clinical application of XJQ. Modulation of STAT3 and IL-6 by XJQ can impact CHF, further guiding research efforts into the molecular underpinnings of CHF.
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Affiliation(s)
- Dongyang Zhao
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Kaijing Guo
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Qian Zhang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education and Beijing, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China
| | - Yan Wu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Chen Ma
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Wenyi He
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Xiangju Jin
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Xinyu Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Yanan Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China.
| | - Sheng Lin
- Key Laboratory of Chinese Internal Medicine of Ministry of Education and Beijing, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China.
| | - Hongcai Shang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education and Beijing, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China.
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11
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Guo W, Liu J, Dong F, Song M, Li Z, Khan MKH, Patterson TA, Hong H. Review of machine learning and deep learning models for toxicity prediction. Exp Biol Med (Maywood) 2023; 248:1952-1973. [PMID: 38057999 PMCID: PMC10798180 DOI: 10.1177/15353702231209421] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023] Open
Abstract
The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.
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Affiliation(s)
- Wenjing Guo
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Jie Liu
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Fan Dong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Meng Song
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Zoe Li
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Md Kamrul Hasan Khan
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Tucker A Patterson
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
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12
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Feng H, Wang R, Zhan CG, Wei GW. Multiobjective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex. J Med Chem 2023; 66:12479-12498. [PMID: 37623046 PMCID: PMC11037444 DOI: 10.1021/acs.jmedchem.3c01053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Abstract
Opioid use disorder (OUD) has emerged as a significant global public health issue, necessitating the discovery of new medications. In this study, we propose a deep generative model that combines a stochastic differential equation (SDE)-based diffusion model with a pretrained autoencoder. The molecular generator enables efficient generation of molecules that target multiple opioid receptors, including mu, kappa, and delta. Additionally, we assess the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the generated molecules to identify druglike compounds. We develop a molecular optimization approach to enhance the pharmacokinetic properties of some lead compounds. Advanced binding affinity predictors were built using molecular fingerprints, including autoencoder embeddings, transformer embeddings, and topological Laplacians. Our process yields druglike molecules that can be used in highly focused experimental studies to further evaluate their pharmacological effects. Our machine learning platform serves as a valuable tool for designing effective molecules to address OUD.
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Affiliation(s)
- Hongsong Feng
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Rui Wang
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Chang-Guo Zhan
- Department of Pharmaceutical Sciences, University of Kentucky, Lexington, Kentucky 40506, United States
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
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13
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Bo T, Lin Y, Han J, Hao Z, Liu J. Machine learning-assisted data filtering and QSAR models for prediction of chemical acute toxicity on rat and mouse. JOURNAL OF HAZARDOUS MATERIALS 2023; 452:131344. [PMID: 37027914 DOI: 10.1016/j.jhazmat.2023.131344] [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: 12/07/2022] [Revised: 03/20/2023] [Accepted: 03/31/2023] [Indexed: 05/03/2023]
Abstract
Machine learning (ML) methods provide a new opportunity to build quantitative structure-activity relationship (QSAR) models for predicting chemicals' toxicity based on large toxicity data sets, but they are limited in insufficient model robustness due to poor data set quality for chemicals with certain structures. To address this issue and improve model robustness, we built a large data set on rat oral acute toxicity for thousands of chemicals, then used ML to filter chemicals favorable for regression models (CFRM). In comparison to chemicals not favorable for regression models (CNRM), CFRM accounted for 67% of chemicals in the original data set, and had a higher structural similarity and a smaller toxicity distribution in 2-4 log10 (mg/kg). The performance of established regression models for CFRM was greatly improved, with root-mean-square deviations (RMSE) in the range of 0.45-0.48 log10 (mg/kg). Classification models were built for CNRM using all chemicals in the original data set, and the area under receiver operating characteristic (AUROC) reached 0.75-0.76. The proposed strategy was successfully applied to a mouse oral acute data set, yielding RMSE and AUROC in the range of 0.36-0.38 log10 (mg/kg) and 0.79, respectively.
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Affiliation(s)
- Tao Bo
- School of Environment, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China; State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China
| | - Yaohui Lin
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China; Key Laboratory for Analytical Science of Food Safety and Biology of MOE, Fujian Provincial Key Lab of Analysis and Detection for Food Safety, College of Chemistry, Fuzhou University, Fuzhou, Fujian 350116, China
| | - Jinglong Han
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China
| | - Zhineng Hao
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China.
| | - Jingfu Liu
- School of Environment, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China; State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China.
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14
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Feng H, Jiang J, Wei GW. Machine-learning repurposing of DrugBank compounds for opioid use disorder. Comput Biol Med 2023; 160:106921. [PMID: 37178605 DOI: 10.1016/j.compbiomed.2023.106921] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/30/2023] [Accepted: 04/13/2023] [Indexed: 05/15/2023]
Abstract
Opioid use disorder (OUD) is a chronic and relapsing condition that involves the continued and compulsive use of opioids despite harmful consequences. The development of medications with improved efficacy and safety profiles for OUD treatment is urgently needed. Drug repurposing is a promising option for drug discovery due to its reduced cost and expedited approval procedures. Computational approaches based on machine learning enable the rapid screening of DrugBank compounds, identifying those with the potential to be repurposed for OUD treatment. We collected inhibitor data for four major opioid receptors and used advanced machine learning predictors of binding affinity that fuse the gradient boosting decision tree algorithm with two natural language processing (NLP)-based molecular fingerprints and one traditional 2D fingerprint. Using these predictors, we systematically analyzed the binding affinities of DrugBank compounds on four opioid receptors. Based on our machine learning predictions, we were able to discriminate DrugBank compounds with various binding affinity thresholds and selectivities for different receptors. The prediction results were further analyzed for ADMET (absorption, distribution, metabolism, excretion, and toxicity), which provided guidance on repurposing DrugBank compounds for the inhibition of selected opioid receptors. The pharmacological effects of these compounds for OUD treatment need to be tested in further experimental studies and clinical trials. Our machine learning studies provide a valuable platform for drug discovery in the context of OUD treatment.
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Affiliation(s)
- Hongsong Feng
- Department of Mathematics, Michigan State University, MI 48824, USA
| | - Jian Jiang
- Department of Mathematics, Michigan State University, MI 48824, USA; Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, 430200, PR China
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, MI 48824, USA; Department of Electrical and Computer Engineering, Michigan State University, MI 48824, USA; Department of Biochemistry and Molecular Biology, Michigan State University, MI 48824, USA.
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15
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Identification of medicinal plant-based phytochemicals as a potential inhibitor for SARS-CoV-2 main protease (M pro) using molecular docking and deep learning methods. Comput Biol Med 2023; 157:106785. [PMID: 36931201 PMCID: PMC10008098 DOI: 10.1016/j.compbiomed.2023.106785] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 02/15/2023] [Accepted: 03/10/2023] [Indexed: 03/14/2023]
Abstract
Highly transmissive and rapidly evolving Coronavirus disease-2019 (COVID-19), a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), triggered a global pandemic, which is one of the most researched viruses in the academia. Effective drugs to treat people with COVID-19 have yet to be developed to reduce mortality and transmission. Studies on the SARS-CoV-2 virus identified that its main protease (Mpro) might be a potential therapeutic target for drug development, as this enzyme plays a key role in viral replication. In search of potential inhibitors of Mpro, we developed a phytochemical library consisting of 2431 phytochemicals from 104 Korean medicinal plants that exhibited medicinal and antioxidant properties. The library was screened by molecular docking, followed by revalidation by re-screening with a deep learning method. Recurrent Neural Networks (RNN) computing system was used to develop an inhibitory predictive model using SARS coronavirus Mpro dataset. It was deployed to screen the top 12 compounds based on their docked binding affinity that ranged from -8.0 to -8.9 kcal/mol. The top two lead compounds, Catechin gallate and Quercetin 3-O-malonylglucoside, were selected depending on inhibitory potency against Mpro. Interactions with the target protein active sites, including His41, Met49, Cys145, Met165, and Thr190 were also examined. Molecular dynamics simulation was performed to analyze root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RG), solvent accessible surface area (SASA), and number of hydrogen bonds. Results confirmed the inflexible nature of the docked complexes. Absorption, distribution, metabolism, excretion, and toxicity (ADMET), as well as bioactivity prediction confirmed the pharmaceutical activities of the lead compound. Findings of this research might help scientists to optimize compatible drugs for the treatment of COVID-19 patients.
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16
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Torres L, Arrais JP, Ribeiro B. Few-shot learning via graph embeddings with convolutional networks for low-data molecular property prediction. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08403-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
AbstractGraph neural networks and convolutional architectures have proven to be pivotal in improving the prediction of molecular properties in drug discovery. However, this is fundamentally a low data problem that is incompatible with regular deep learning approaches. Contemporary deep networks require large amounts of training data, which severely limits the prediction of new molecular entities from limited available data. In this paper, we address the challenge of low data in molecular property prediction by: (1) defining a set of deep learning architectures that accept compound chemical structures in the form of molecular graphs, (2) creating a few-shot learning strategy across graph neural networks and convolutional neural networks to leverage the rich information of graph embeddings, and (3) proposing a two-module meta-learning framework to learn from task-transferable knowledge and predict molecular properties on few-shot data. Furthermore, we conduct multiple experiments on two benchmark multiproperty datasets to demonstrate a superior performance over conventional graph-based baselines. ROC-AUC results for 10-shot experiments show an average improvement of $$+11.37\%$$
+
11.37
%
on Tox21 and $$+0.53\%$$
+
0.53
%
on SIDER, which are representative small-sized biological datasets for molecular property prediction.
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17
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Lane TR, Harris J, Urbina F, Ekins S. Comparing LD 50/LC 50 Machine Learning Models for Multiple Species. ACS CHEMICAL HEALTH & SAFETY 2023. [DOI: 10.1021/acs.chas.2c00088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Affiliation(s)
- Thomas R. Lane
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Joshua Harris
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Fabio Urbina
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
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18
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The identification of metabolites from gut microbiota in NAFLD via network pharmacology. Sci Rep 2023; 13:724. [PMID: 36639568 PMCID: PMC9839744 DOI: 10.1038/s41598-023-27885-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 01/10/2023] [Indexed: 01/15/2023] Open
Abstract
The metabolites of gut microbiota show favorable therapeutic effects on nonalcoholic fatty liver disease (NAFLD), but the active metabolites and mechanisms against NAFLD have not been documented. The aim of the study was to investigate the active metabolites and mechanisms of gut microbiota against NAFLD by network pharmacology. We obtained a total of 208 metabolites from the gutMgene database and retrieved 1256 targets from similarity ensemble approach (SEA) and 947 targets from the SwissTargetPrediction (STP) database. In the SEA and STP databases, we identified 668 overlapping targets and obtained 237 targets for NAFLD. Thirty-eight targets were identified out of those 237 and 223 targets retrieved from the gutMgene database, and were considered the final NAFLD targets of metabolites from the microbiome. The results of molecular docking tests suggest that, of the 38 targets, mitogen-activated protein kinase 8-compound K and glycogen synthase kinase-3 beta-myricetin complexes might inhibit the Wnt signaling pathway. The microbiota-signaling pathways-targets-metabolites network analysis reveals that Firmicutes, Fusobacteria, the Toll-like receptor signaling pathway, mitogen-activated protein kinase 1, and phenylacetylglutamine are notable components of NAFLD and therefore to understanding its processes and possible therapeutic approaches. The key components and potential mechanisms of metabolites from gut microbiota against NAFLD were explored utilizing network pharmacology analyses. This study provides scientific evidence to support the therapeutic efficacy of metabolites for NAFLD and suggests holistic insights on which to base further research.
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19
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Cavasotto CN, Scardino V. Machine Learning Toxicity Prediction: Latest Advances by Toxicity End Point. ACS OMEGA 2022; 7:47536-47546. [PMID: 36591139 PMCID: PMC9798519 DOI: 10.1021/acsomega.2c05693] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 11/28/2022] [Indexed: 05/29/2023]
Abstract
Machine learning (ML) models to predict the toxicity of small molecules have garnered great attention and have become widely used in recent years. Computational toxicity prediction is particularly advantageous in the early stages of drug discovery in order to filter out molecules with high probability of failing in clinical trials. This has been helped by the increase in the number of large toxicology databases available. However, being an area of recent application, a greater understanding of the scope and applicability of ML methods is still necessary. There are various kinds of toxic end points that have been predicted in silico. Acute oral toxicity, hepatotoxicity, cardiotoxicity, mutagenicity, and the 12 Tox21 data end points are among the most commonly investigated. Machine learning methods exhibit different performances on different data sets due to dissimilar complexity, class distributions, or chemical space covered, which makes it hard to compare the performance of algorithms over different toxic end points. The general pipeline to predict toxicity using ML has already been analyzed in various reviews. In this contribution, we focus on the recent progress in the area and the outstanding challenges, making a detailed description of the state-of-the-art models implemented for each toxic end point. The type of molecular representation, the algorithm, and the evaluation metric used in each research work are explained and analyzed. A detailed description of end points that are usually predicted, their clinical relevance, the available databases, and the challenges they bring to the field are also highlighted.
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Affiliation(s)
- Claudio N. Cavasotto
- Computational
Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones
en Medicina Traslacional (IIMT), CONICET-Universidad
Austral, Pilar, B1629AHJ Buenos Aires, Argentina
- Austral
Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, B1629AHJ Buenos Aires, Argentina
- Facultad
de Ciencias Biomédicas, Facultad de Ingenierá, Universidad Austral, Pilar, B1630FHB Buenos
Aires, Argentina
| | - Valeria Scardino
- Austral
Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, B1629AHJ Buenos Aires, Argentina
- Meton
AI, Inc., Wilmington, Delaware 19801, United
States
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20
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Wang Z, Zhan J, Gao H. Computer-aided drug design combined network pharmacology to explore anti-SARS-CoV-2 or anti-inflammatory targets and mechanisms of Qingfei Paidu Decoction for COVID-19. Front Immunol 2022; 13:1015271. [PMID: 36618410 PMCID: PMC9816407 DOI: 10.3389/fimmu.2022.1015271] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022] Open
Abstract
Introduction Coronavirus Disease-2019 (COVID-19) is an infectious disease caused by SARS-CoV-2. Severe cases of COVID-19 are characterized by an intense inflammatory process that may ultimately lead to organ failure and patient death. Qingfei Paidu Decoction (QFPD), a traditional Chines e medicine (TCM) formula, is widely used in China as anti-SARS-CoV-2 and anti-inflammatory. However, the potential targets and mechanisms for QFPD to exert anti-SARS-CoV-2 or anti-inflammatory effects remain unclear. Methods In this study, Computer-Aided Drug Design was performed to identify the antiviral or anti-inflammatory components in QFPD and their targets using Discovery Studio 2020 software. We then investigated the mechanisms associated with QFPD for treating COVID-19 with the help of multiple network pharmacology approaches. Results and discussion By overlapping the targets of QFPD and COVID-19, we discovered 8 common targets (RBP4, IL1RN, TTR, FYN, SFTPD, TP53, SRPK1, and AKT1) of 62 active components in QFPD. These may represent potential targets for QFPD to exert anti-SARS-CoV-2 or anti-inflammatory effects. The result showed that QFPD might have therapeutic effects on COVID-19 by regulating viral infection, immune and inflammation-related pathways. Our work will promote the development of new drugs for COVID-19.
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21
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Xiao L, Deng J, Yang L, Huang X, Yu X. Random forest algorithm-based accurate prediction of rat acute oral toxicity. Mol Phys 2022. [DOI: 10.1080/00268976.2022.2140083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Linrong Xiao
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, People’s Republic of China
| | - Jiyong Deng
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, People’s Republic of China
| | - Liping Yang
- Shenzhen Expressway Environment Co., Ltd., Shenzhen, People’s Republic of China
| | - Xianwei Huang
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, People’s Republic of China
| | - Xinliang Yu
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, People’s Republic of China
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22
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Zwickl CM, Graham J, Jolly R, Bassan A, Ahlberg E, Amberg A, Anger LT, Barton-Maclaren T, Beilke L, Bellion P, Brigo A, Cronin MT, Custer L, Devlin A, Burleigh-Flayers H, Fish T, Glover K, Glowienke S, Gromek K, Jones D, Karmaus A, Kemper R, Piparo EL, Madia F, Martin M, Masuda-Herrera M, McAtee B, Mestre J, Milchak L, Moudgal C, Mumtaz M, Muster W, Neilson L, Patlewicz G, Paulino A, Roncaglioni A, Ruiz P, Suarez D, Szabo DT, Valentin JP, Vardakou I, Woolley D, Myatt G. Principles and Procedures for Assessment of Acute Toxicity Incorporating In Silico Methods. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2022; 24:100237. [PMID: 36818760 PMCID: PMC9934006 DOI: 10.1016/j.comtox.2022.100237] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Acute toxicity in silico models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an in silico analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including in silico methods and in vitro or in vivo experiments. In silico methods that can assist the prediction of in vivo outcomes (i.e., LD50) are analyzed concluding that predictions obtained using in silico approaches are now well-suited for reliably supporting assessment of LD50-based acute toxicity for the purpose of GHS classification. A general overview is provided of the endpoints from in vitro studies commonly evaluated for predicting acute toxicity (e.g., cytotoxicity/cytolethality as well as assays targeting specific mechanisms). The increased understanding of pathways and key triggering mechanisms underlying toxicity and the increased availability of in vitro data allow for a shift away from assessments solely based on endpoints such as LD50, to mechanism-based endpoints that can be accurately assessed in vitro or by using in silico prediction models. This paper also highlights the importance of an expert review of all available information using weight-of-evidence considerations and illustrates, using a series of diverse practical use cases, how in silico approaches support the assessment of acute toxicity.
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Affiliation(s)
| | - Jessica Graham
- Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Robert Jolly
- Eli Lilly and Company, Indianapolis, IN 46285, USA
| | - Arianna Bassan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova, Italy
| | - Ernst Ahlberg
- Universal Prediction AB, Gothenburg, Sweden
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Alexander Amberg
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | | | - Tara Barton-Maclaren
- Healthy Environments and Consumer Safety Branch, Health Canada / Government of Canada
| | - Lisa Beilke
- Toxicology Solutions, Inc., 10531 4S Commons Dr. #594, San Diego, CA 92127, USA
| | - Phillip Bellion
- Boehringer Ingelheim Animal Health, Binger Str. 128, 55216 Ingelheim am Rhein, Germany
| | - Alessandro Brigo
- Roche Pharmaceutical Research & Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | | | | | - Amy Devlin
- FDA Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | | | - Trevor Fish
- Nelson Laboratories, Salt Lake City, Utah, USA
| | | | | | | | - David Jones
- MHRA, 10 South Colonnade, Canary Wharf, London E14 4PU
| | - Agnes Karmaus
- Integrated Laboratory Systems, LLC, Morrisville, NC, USA
| | | | - Elena Lo Piparo
- Chemical Food Safety Group, Nestlé Research, Lausanne, Switzerland
| | - Federica Madia
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | | | | | | | - Jordi Mestre
- IMIM Institut Hospital Del Mar d’Investigacions Mèdiques and Universitat Pompeu Fabra, Doctor Aiguader 88, Parc de Recerca Biomèdica, 08003 Barcelona, Spain
- Chemotargets SL, Baldiri Reixac 4, Parc Científic de Barcelona, 08028 Barcelona, Spain
| | | | | | - Moiz Mumtaz
- Office of the Associate Director for Science, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA
| | - Wolfgang Muster
- Roche Pharmaceutical Research & Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | | | - Grace Patlewicz
- Centre for Computational Toxicology and Exposure (CCTE), US Environmental Protection Agency, Research Triangle Park, NC, USA
| | | | - Alessandra Roncaglioni
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Patricia Ruiz
- Centers for Disease Control and Prevention (CDC), Atlanta, GA 30341, USA
| | - Diana Suarez
- FSTox Consulting LTD, 2 Brooks Road Raunds Wellingborough NN9 6NS
| | | | - Jean-Pierre Valentin
- UCB-Biopharma SRL, Development Science, Avenue de l’industrie, Braine l’Alleud, Wallonia, Belgium
| | - Ioanna Vardakou
- British American Tobacco (Investments) Ltd., R&D Centre, Southampton, Hampshire SO15 8TL, UK
| | | | - Glenn Myatt
- Instem, 1393 Dublin Rd, Columbus, OH 43215, USA
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Silva J, Esmeraldo Rocha J, da Cunha Xavier J, Sampaio de Freitas T, Douglas Melo Coutinho H, Nogueira Bandeira P, Rodrigues de Oliveira M, Nunes da Rocha M, Machado Marinho E, de Kassio Vieira Monteiro N, Ribeiro LR, Róseo Paula Pessoa Bezerra de Menezes R, Machado Marinho M, Magno Rodrigues Teixeira A, Silva dos Santos H, Silva Marinho E. Antibacterial and antibiotic modifying activity of chalcone (2E)-1-(4′-aminophenyl)-3-(4-methoxyphenyl)-prop-2-en-1-one in strains of Staphylococcus aureus carrying NorA and MepA efflux pumps: In vitro and in silico approaches. Microb Pathog 2022; 169:105664. [DOI: 10.1016/j.micpath.2022.105664] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 06/19/2022] [Accepted: 06/28/2022] [Indexed: 01/11/2023]
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Orosz Á, Héberger K, Rácz A. Comparison of Descriptor- and Fingerprint Sets in Machine Learning Models for ADME-Tox Targets. Front Chem 2022; 10:852893. [PMID: 35755260 PMCID: PMC9214226 DOI: 10.3389/fchem.2022.852893] [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: 01/11/2022] [Accepted: 04/14/2022] [Indexed: 01/12/2023] Open
Abstract
The screening of compounds for ADME-Tox targets plays an important role in drug design. QSPR models can increase the speed of these specific tasks, although the performance of the models highly depends on several factors, such as the applied molecular descriptors. In this study, a detailed comparison of the most popular descriptor groups has been carried out for six main ADME-Tox classification targets: Ames mutagenicity, P-glycoprotein inhibition, hERG inhibition, hepatotoxicity, blood–brain-barrier permeability, and cytochrome P450 2C9 inhibition. The literature-based, medium-sized binary classification datasets (all above 1,000 molecules) were used for the model building by two common algorithms, XGBoost and the RPropMLP neural network. Five molecular representation sets were compared along with their joint applications: Morgan, Atompairs, and MACCS fingerprints, and the traditional 1D and 2D molecular descriptors, as well as 3D molecular descriptors, separately. The statistical evaluation of the model performances was based on 18 different performance parameters. Although all the developed models were close to the usual performance of QSPR models for each specific ADME-Tox target, the results clearly showed the superiority of the traditional 1D, 2D, and 3D descriptors in the case of the XGBoost algorithm. It is worth trying the classical tools in single model building because the use of 2D descriptors can produce even better models for almost every dataset than the combination of all the examined descriptor sets.
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Affiliation(s)
- Álmos Orosz
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
| | - Károly Héberger
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
| | - Anita Rácz
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
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25
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Computational Study of Asian Propolis Compounds as Potential Anti-Type 2 Diabetes Mellitus Agents by Using Inverse Virtual Screening with the DIA-DB Web Server, Tanimoto Similarity Analysis, and Molecular Dynamic Simulation. Molecules 2022; 27:molecules27133972. [PMID: 35807241 PMCID: PMC9268573 DOI: 10.3390/molecules27133972] [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: 05/14/2022] [Revised: 06/10/2022] [Accepted: 06/15/2022] [Indexed: 02/01/2023] Open
Abstract
Propolis contains a wide range of pharmacological activities because of their various bioactive compounds. The beneficial effect of propolis is interesting for treating type-2 diabetes mellitus (T2DM) owing to dysregulation of multiple metabolic processes. In this study, 275 of 658 Asian propolis compounds were evaluated as potential anti-T2DM agents using the DIA-DB web server towards 18 known anti-diabetes protein targets. More than 20% of all compounds could bind to more than five diabetes targets with high binding affinity (<−9.0 kcal/mol). Filtering with physicochemical and pharmacokinetic properties, including ADMET parameters, 12 compounds were identified as potential anti-T2DM with favorable ADMET properties. Six of those compounds, (2R)-7,4′-dihydroxy-5-methoxy-8-methylflavone; (RR)-(+)-3′-senecioylkhellactone; 2′,4′,6′-trihydroxy chalcone; alpinetin; pinobanksin-3-O-butyrate; and pinocembrin-5-methyl ether were first reported as anti-T2DM agents. We identified the significant T2DM targets of Asian propolis, namely retinol-binding protein-4 (RBP4) and aldose reductase (AKR1B1) that have important roles in insulin sensitivity and diabetes complication, respectively. Molecular dynamic simulations showed stable interaction of selected propolis compounds in the active site of RBP4 and AKR1B1. These findings suggest that Asian propolis compound may be effective for treatment of T2DM by targeting RBP4 and AKR1B1.
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26
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Saeed M, Tasleem M, Shoaib A, Alabdallah NM, Alam MJ, El Asmar Z, Jamal QMS, Bardakci F, Ansari IA, Ansari MJ, Wang F, Badraoui R, Yadav DK. Investigation of antidiabetic properties of shikonin by targeting aldose reductase enzyme: In silico and in vitro studies. Biomed Pharmacother 2022; 150:112985. [PMID: 35658219 DOI: 10.1016/j.biopha.2022.112985] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 04/14/2022] [Accepted: 04/14/2022] [Indexed: 02/08/2023] Open
Abstract
Diabetes is a complicated multifactorial disorder in which the patient generally observes polyphagia, polydipsia, and polyuria due to uncontrolled growth in blood sugar levels. For its management, the pharmaceutical industry is working day and night to find a better drug with no or least toxicity. That's why nowadays a more focused branch is to use herbal phytoconstituents for its prevention. Shikonin is a naphthoquinone natural dye that is isolated from the plants of the Boraginaceae family and has proven its role as an anti-cancer, anti-inflammatory, and anti-gonadotrophic agent. In our previous study, we have published its anti-diabetic action by inhibiting the enzyme protein tyrosine phosphatase 1B. In this study, we were more focused on finding out the role of Shikonin and its pharmacophores by inhibiting the action of aldose reductase (AR) enzyme. The study was conducted using pharmacophore modeling, molecular docking, and molecular dynamics simulation studies. The absorption, distribution, metabolism, excretion (ADME), and toxicity profile were also evaluated in this study. Along with all the computational biology parameters we also focused on the in vitro activity and kinetic study of inhibitory activity of Shikonin against aldose reductase.
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Affiliation(s)
- Mohd Saeed
- Department of Biology, College of Sciences, University of Hail, Hail 81451, Saudi Arabia.
| | - Munazzah Tasleem
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Ambreen Shoaib
- Department of Clinical Pharmacy, College of Pharmacy, Jazan University, P.O. Box No. 114, Jazan 45142, Saudi Arabia
| | - Nadiyah M Alabdallah
- Department of Biology, College of Science, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, 31441, Dammam, Saudi Arabia
| | - Md Jahoor Alam
- Department of Biology, College of Sciences, University of Hail, Hail 81451, Saudi Arabia
| | - Zeina El Asmar
- Department of Biology, College of Sciences, University of Hail, Hail 81451, Saudi Arabia
| | - Qazi Mohammad Sajid Jamal
- Department of Health Informatics, College of Public Health and Health Informatics, Qassim University, Al Bukayriyah 52741, Saudi Arabia
| | - Fevzi Bardakci
- Department of Biology, College of Sciences, University of Hail, Hail 81451, Saudi Arabia
| | | | - Mohammad Javed Ansari
- Department of Botany, Hindu College Moradabad, Mahatma Jyotiba Phule Rohilkhand University, Bareilly 244001, India
| | - Feng Wang
- Department of Medical Oncology, Cancer Center, West China Hospital, West China Medical School, Sichuan University, Sichuan, PR China.
| | - Riadh Badraoui
- Department of Biology, College of Sciences, University of Hail, Hail 81451, Saudi Arabia; Section of Histology-Cytology, Medicine Faculty of Tunis, University of Tunis El Manar, La Rabta-Tunis, 1007, Tunisia
| | - Dharmendra Kumar Yadav
- Department of Pharmacy and Gachon Institute of Pharmaceutical Science, College of Pharmacy, Gachon University, Yeonsu-gu, Incheon 21924, South Korea.
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27
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Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther 2022; 7:156. [PMID: 35538061 PMCID: PMC9090746 DOI: 10.1038/s41392-022-00994-0] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 03/14/2022] [Accepted: 04/05/2022] [Indexed: 02/08/2023] Open
Abstract
Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.
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28
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Zhang X, Mao J, Wei M, Qi Y, Zhang JZH. HergSPred: Accurate Classification of hERG Blockers/Nonblockers with Machine-Learning Models. J Chem Inf Model 2022; 62:1830-1839. [DOI: 10.1021/acs.jcim.2c00256] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Xudong Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University at Shanghai, Shanghai 200062, China
| | - Jun Mao
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University at Shanghai, Shanghai 200062, China
| | - Min Wei
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University at Shanghai, Shanghai 200062, China
| | - Yifei Qi
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, Shanghai 201203, China
| | - John Z. H. Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University at Shanghai, Shanghai 200062, China
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- NYU-ECNU Center for Computational Chemistry at NYU, Shanghai 200062, China
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29
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HobPre: accurate prediction of human oral bioavailability for small molecules. J Cheminform 2022; 14:1. [PMID: 34991690 PMCID: PMC8740492 DOI: 10.1186/s13321-021-00580-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 12/28/2021] [Indexed: 11/10/2022] Open
Abstract
Human oral bioavailability (HOB) is a key factor in determining the fate of new drugs in clinical trials. HOB is conventionally measured using expensive and time-consuming experimental tests. The use of computational models to evaluate HOB before the synthesis of new drugs will be beneficial to the drug development process. In this study, a total of 1588 drug molecules with HOB data were collected from the literature for the development of a classifying model that uses the consensus predictions of five random forest models. The consensus model shows excellent prediction accuracies on two independent test sets with two cutoffs of 20% and 50% for classification of molecules. The analysis of the importance of the input variables allowed the identification of the main molecular descriptors that affect the HOB class value. The model is available as a web server at www.icdrug.com/ICDrug/ADMET for quick assessment of oral bioavailability for small molecules. The results from this study provide an accurate and easy-to-use tool for screening of drug candidates based on HOB, which may be used to reduce the risk of failure in late stage of drug development. ![]()
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30
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Tallei TE, Kepel BJ, Alorabi M, El-Shehawi AM, Bodhi W, Tumilaar SG, Celik I, Mostafa-Hedeab G, Mohamed AAR, Emran TB. Appraisal of Bioactive Compounds of Betel Fruit as Antimalarial Agents by Targeting Plasmepsin 1 and 2: A Computational Approach. Pharmaceuticals (Basel) 2021; 14:ph14121285. [PMID: 34959685 PMCID: PMC8707617 DOI: 10.3390/ph14121285] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 12/01/2021] [Accepted: 12/03/2021] [Indexed: 11/16/2022] Open
Abstract
In many countries, the fruit of betel (Piper betle Linn) is traditionally used as medicine for treating malaria. It is a fatal disease, and existing medications are rapidly losing potency, necessitating the development of innovative pharmaceutics. The current study attempted to determine the compounds in the n-hexane fraction of betel fruit extract and investigate the potential inhibition of bioactive compounds against aspartic protease plasmepsin 1 (PDB ID: 3QS1) and plasmepsin 2 (PDB ID: 1LEE) of Plasmodium falciparum using a computational approach. The ethanol extract was fractionated into n-hexane and further analyzed using gas chromatography-mass spectrometry (GC-MS) to obtain information regarding the compounds contained in betel fruit. Each compound's potential antimalarial activity was evaluated using AutoDock Vina and compared to artemisinin, an antimalarial drug. Molecular dynamics simulations (MDSs) were performed to evaluate the stability of the interaction between the ligand and receptors. Results detected 20 probable compounds in the n-hexane extract of betel fruit based on GC-MS analysis. The docking study revealed that androstan-17-one,3-ethyl-3-hydroxy-, (5 alpha)- has the highest binding affinity for plasmepsin 1 and plasmepsin 2. The compound exhibits a similar interaction with artemisinin at the active site of the receptors. The compound does not violate Lipinski's rules of five. It belongs to class 5 toxicity with an LD50 of 3000 mg/kg. MDS results showed stable interactions between the compound and the receptors. Our study concluded that androstan-17-one,3-ethyl-3-hydroxy-, (5 alpha)- from betel fruit has the potential to be further investigated as a potential inhibitor of the aspartic protease plasmepsin 1 and plasmepsin 2 of Plasmodium falciparum.
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Affiliation(s)
- Trina Ekawati Tallei
- The University Center of Excellence for Biotechnology and Conservation of Wallacea, Institute for Research and Community Services, Sam Ratulangi University, Manado 95115, Indonesia
- Department of Biology, Faculty of Mathematics and Natural Sciences, Sam Ratulangi University, Manado 95115, Indonesia
- Correspondence: (F.); (T.E.T.); (T.B.E.); Tel.: +62-812-4408-855 (F.); +62-811-4314-880 (T.E.T.); +880-01819-942214 (T.B.E.)
| | - Billy Johnson Kepel
- Department of Chemistry, Faculty of Medicine, Sam Ratulangi University, Manado 95115, Indonesia; (B.J.K.); (W.B.)
| | - Mohammed Alorabi
- Department of Biotechnology, College of Science, Taif University, Taif 21944, Saudi Arabia; (M.A.); (A.M.E.-S.)
| | - Ahmed M. El-Shehawi
- Department of Biotechnology, College of Science, Taif University, Taif 21944, Saudi Arabia; (M.A.); (A.M.E.-S.)
| | - Widdhi Bodhi
- Department of Chemistry, Faculty of Medicine, Sam Ratulangi University, Manado 95115, Indonesia; (B.J.K.); (W.B.)
| | - Sefren Geiner Tumilaar
- Pharmacy Study Program, Faculty of Mathematics and Natural Sciences, Sam Ratulangi University, Manado 95115, Indonesia;
| | - Ismail Celik
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Erciyes University, Kayseri 38039, Turkey;
| | - Gomaa Mostafa-Hedeab
- Pharmacology Department, Health Sciences Research Unit, Medical College, Jouf University, Sakaka 72446, Saudi Arabia;
- Pharmacology Department, Faculty of Medicine, Beni-Suef University, Beni Suef 62521, Egypt
| | | | - Talha Bin Emran
- Department of Pharmacy, BGC Trust University Bangladesh, Chittagong 4381, Bangladesh
- Correspondence: (F.); (T.E.T.); (T.B.E.); Tel.: +62-812-4408-855 (F.); +62-811-4314-880 (T.E.T.); +880-01819-942214 (T.B.E.)
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31
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Muller C, Rabal O, Diaz Gonzalez C. Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2021; 2390:383-407. [PMID: 34731478 DOI: 10.1007/978-1-0716-1787-8_16] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The discovery and development of drugs is a long and expensive process with a high attrition rate. Computational drug discovery contributes to ligand discovery and optimization, by using models that describe the properties of ligands and their interactions with biological targets. In recent years, artificial intelligence (AI) has made remarkable modeling progress, driven by new algorithms and by the increase in computing power and storage capacities, which allow the processing of large amounts of data in a short time. This review provides the current state of the art of AI methods applied to drug discovery, with a focus on structure- and ligand-based virtual screening, library design and high-throughput analysis, drug repurposing and drug sensitivity, de novo design, chemical reactions and synthetic accessibility, ADMET, and quantum mechanics.
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Affiliation(s)
- Christophe Muller
- Evotec (France) SAS, Computational Drug Discovery, Integrated Drug Discovery, Toulouse, France
| | - Obdulia Rabal
- Evotec (France) SAS, Computational Drug Discovery, Integrated Drug Discovery, Toulouse, France
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32
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Zafar H, Atif M, Atia-tul-Wahab, Choudhary MI. Fucosyltransferase 2 inhibitors: Identification via docking and STD-NMR studies. PLoS One 2021; 16:e0257623. [PMID: 34648519 PMCID: PMC8516197 DOI: 10.1371/journal.pone.0257623] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 09/05/2021] [Indexed: 12/18/2022] Open
Abstract
Fucosyltransferase 2 (FUT2) catalyzes the biosynthesis of A, B, and H antigens and other important glycans, such as (Sialyl Lewisx) sLex, and (Sialyl Lewisy) sLey. The production of these glycans is increased in various cancers, hence to design and develop specific inhibitors of FUT2 is a therapeutic strategy. The current study was designed to identify the inhibitors for FUT2. In silico screening of 300 synthetic compounds was performed. Molecular docking studies highlighted the interactions of ligands with critical amino acid residues, present in the active site of FUT2. The epitope mapping in ligands was performed using the STD-NMR experiments to identify the interactions between ligands, and receptor protein. Finally, we have identified 5 lead compounds 4, 5, 26, 27, and 28 that can be studied for further development as cancer therapeutic agents.
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Affiliation(s)
- Humaira Zafar
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
| | - Muhammad Atif
- H. E. J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
| | - Atia-tul-Wahab
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
| | - M. Iqbal Choudhary
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
- H. E. J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
- Faculty of Science and Technology, Department of Chemistry, Universitas Airlangga, Komplek Campus C, Surabaya, Indonesia
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33
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Forrestall KL, Burley DE, Cash MK, Pottie IR, Darvesh S. Phenothiazines as dual inhibitors of SARS-CoV-2 main protease and COVID-19 inflammation. CAN J CHEM 2021. [DOI: 10.1139/cjc-2021-0139] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
COVID-19, caused by the severe acute respiratory coronavirus 2 (SARS-CoV-2), currently has no treatment for acute infection. The main protease (Mpro) of SARS-CoV-2 is an essential enzyme for viral replication and an attractive target for disease intervention. The phenothiazine moiety has demonstrated drug versatility for biological systems, including inhibition of butyrylcholinesterase, a property important in the cholinesterase anti-inflammatory cascade. Nineteen phenothiazine drugs were investigated using in silico modelling techniques to predict binding energies and inhibition constants (Ki values) with SARS-CoV-2 Mpro. Because most side-effects of phenothiazines are due to interactions with various neurotransmitter receptors and transporters, phenothiazines with few such interactions were also investigated. All compounds were found to bind to the active site of SARS-CoV-2 Mpro and showed Ki values ranging from 1.30 to 52.4 µM in a rigid active site. Nine phenothiazines showed inhibition constants <10 µM. The compounds with limited interactions with neurotransmitter receptors and transporters showed micromolar (µM) Ki values. Docking results were compared with remdesivir and showed similar interactions with key residues Glu-166 and Gln-189 in the active site. This work has identified several phenothiazines with limited neurotransmitter receptor and transporter interactions and that may provide the dual action of inhibiting SARS-CoV-2 Mpro to prevent viral replication and promote the release of anti-inflammatory cytokines to curb viral-induced inflammation. These compounds are promising candidates for further investigation against SARS-CoV-2.
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Affiliation(s)
- Katrina L. Forrestall
- Department of Medical Neuroscience, Faculty of Medicine, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Darcy E. Burley
- Department of Medical Neuroscience, Faculty of Medicine, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Meghan K. Cash
- Department of Medical Neuroscience, Faculty of Medicine, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Ian R. Pottie
- Department of Chemistry and Physics, Faculty of Arts and Science, Mount Saint Vincent University, Halifax, NS B3M 2J6, Canada
- Department of Chemistry, Faculty of Science, Saint Mary’s University, Halifax, NS B3H 3C3, Canada
| | - Sultan Darvesh
- Department of Medical Neuroscience, Faculty of Medicine, Dalhousie University, Halifax, NS B3H 4R2, Canada
- Department of Chemistry and Physics, Faculty of Arts and Science, Mount Saint Vincent University, Halifax, NS B3M 2J6, Canada
- Department of Medicine (Neurology & Geriatric Medicine), Faculty of Medicine, Dalhousie University, Halifax, NS B3H 4R2, Canada
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34
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Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021; 25:1315-1360. [PMID: 33844136 PMCID: PMC8040371 DOI: 10.1007/s11030-021-10217-3] [Citation(s) in RCA: 253] [Impact Index Per Article: 84.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/22/2021] [Indexed: 02/06/2023]
Abstract
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Devesh Srivastava
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Swati Tiwari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India.
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Hua Y, Shi Y, Cui X, Li X. In silico prediction of chemical-induced hematotoxicity with machine learning and deep learning methods. Mol Divers 2021; 25:1585-1596. [PMID: 34196933 DOI: 10.1007/s11030-021-10255-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 06/14/2021] [Indexed: 12/15/2022]
Abstract
Chemical-induced hematotoxicity is an important concern in the drug discovery, since it can often be fatal when it happens. It is quite useful for us to give special attention to chemicals which can cause hematotoxicity. In the present study, we focused on in silico prediction of chemical-induced hematotoxicity with machine learning (ML) and deep learning (DL) methods. We collected a large data set contained 632 hematotoxic chemicals and 1525 approved drugs without hematotoxicity. Computational models were built using several different machine learning and deep learning algorithms integrated on the Online Chemical Modeling Environment (OCHEM). Based on the three best individual models, a consensus model was developed. It yielded the prediction accuracy of 0.83 and balanced accuracy of 0.77 on external validation. The consensus model and the best individual model developed with random forest regression and classification algorithm (RFR) and QNPR descriptors were made available at https://ochem.eu/article/135149 , respectively. The relevance of 8 commonly used molecular properties and chemical-induced hematotoxicity was also investigated. Several molecular properties have an obvious differentiating effect on chemical-induced hematotoxicity. Besides, 12 structural alerts responsible for chemical hematotoxicity were identified using frequency analysis of substructures from Klekota-Roth fingerprint. These results should provide meaningful knowledge and useful tools for hematotoxicity evaluation in drug discovery and environmental risk assessment.
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Affiliation(s)
- Yuqing Hua
- School of Pharmacy, Shandong First Medical University, Taian, 271000, China.,Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China
| | - Yinping Shi
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China
| | - Xueyan Cui
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China
| | - Xiao Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China. .,Department of Clinical Pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China.
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Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review. Mol Divers 2021; 25:1643-1664. [PMID: 34110579 DOI: 10.1007/s11030-021-10237-z] [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: 03/18/2021] [Accepted: 05/26/2021] [Indexed: 10/21/2022]
Abstract
Artificial intelligence (AI) renders cutting-edge applications in diverse sectors of society. Due to substantial progress in high-performance computing, the development of superior algorithms, and the accumulation of huge biological and chemical data, computer-assisted drug design technology is playing a key role in drug discovery with its advantages of high efficiency, fast speed, and low cost. Over recent years, due to continuous progress in machine learning (ML) algorithms, AI has been extensively employed in various drug discovery stages. Very recently, drug design and discovery have entered the big data era. ML algorithms have progressively developed into a deep learning technique with potent generalization capability and more effectual big data handling, which further promotes the integration of AI technology and computer-assisted drug discovery technology, hence accelerating the design and discovery of the newest drugs. This review mainly summarizes the application progression of AI technology in the drug discovery process, and explores and compares its advantages over conventional methods. The challenges and limitations of AI in drug design and discovery have also been discussed.
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Wu Z, Jiang D, Wang J, Hsieh CY, Cao D, Hou T. Mining Toxicity Information from Large Amounts of Toxicity Data. J Med Chem 2021; 64:6924-6936. [PMID: 33961429 DOI: 10.1021/acs.jmedchem.1c00421] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Safety is a main reason for drug failures, and therefore, the detection of compound toxicity and potential adverse effects in the early stage of drug development is highly desirable. However, accurate prediction of many toxicity endpoints is extremely challenging due to low accessibility of sufficient and reliable toxicity data, as well as complicated and diversified mechanisms related to toxicity. In this study, we proposed the novel multitask graph attention (MGA) framework to learn the regression and classification tasks simultaneously. MGA has shown excellent predictive power on 33 toxicity data sets and has the capability to extract general toxicity features and generate customized toxicity fingerprints. In addition, MGA provides a new way to detect structural alerts and discover the relationship between different toxicity tasks, which will be quite helpful to mine toxicity information from large amounts of toxicity data.
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Affiliation(s)
- Zhenxing Wu
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China.,State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China
| | - Dejun Jiang
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China
| | - Jike Wang
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China.,National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072 Hubei, P. R. China
| | - Chang-Yu Hsieh
- Tencent Quantum Laboratory, Tencent, Shenzhen 518057 Guangdong, P. R. China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410004 Hunan, P. R. China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China.,State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China
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Nayarisseri A, Khandelwal R, Tanwar P, Madhavi M, Sharma D, Thakur G, Speck-Planche A, Singh SK. Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery. Curr Drug Targets 2021; 22:631-655. [PMID: 33397265 DOI: 10.2174/1389450122999210104205732] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 08/21/2020] [Accepted: 09/14/2020] [Indexed: 11/22/2022]
Abstract
Artificial Intelligence revolutionizes the drug development process that can quickly identify potential biologically active compounds from millions of candidate within a short period. The present review is an overview based on some applications of Machine Learning based tools, such as GOLD, Deep PVP, LIB SVM, etc. and the algorithms involved such as support vector machine (SVM), random forest (RF), decision tree and Artificial Neural Network (ANN), etc. at various stages of drug designing and development. These techniques can be employed in SNP discoveries, drug repurposing, ligand-based drug design (LBDD), Ligand-based Virtual Screening (LBVS) and Structure- based Virtual Screening (SBVS), Lead identification, quantitative structure-activity relationship (QSAR) modeling, and ADMET analysis. It is demonstrated that SVM exhibited better performance in indicating that the classification model will have great applications on human intestinal absorption (HIA) predictions. Successful cases have been reported which demonstrate the efficiency of SVM and RF models in identifying JFD00950 as a novel compound targeting against a colon cancer cell line, DLD-1, by inhibition of FEN1 cytotoxic and cleavage activity. Furthermore, a QSAR model was also used to predict flavonoid inhibitory effects on AR activity as a potent treatment for diabetes mellitus (DM), using ANN. Hence, in the era of big data, ML approaches have been evolved as a powerful and efficient way to deal with the huge amounts of generated data from modern drug discovery to model small-molecule drugs, gene biomarkers and identifying the novel drug targets for various diseases.
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Affiliation(s)
- Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Ravina Khandelwal
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Poonam Tanwar
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Maddala Madhavi
- Department of Zoology, Nizam College, Osmania University, Hyderabad - 500001, Telangana State, India
| | - Diksha Sharma
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Garima Thakur
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Alejandro Speck-Planche
- Programa Institucional de Fomento a la Investigacion, Desarrollo e Innovacion, Universidad Tecnologica Metropolitana, Ignacio Valdivieso 2409, P.O. 8940577, San Joaquin, Santiago, Chile
| | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630003, Tamil Nadu, India
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Mitra D, Verma D, Mahakur B, Kamboj A, Srivastava R, Gupta S, Pandey A, Arora B, Pant K, Panneerselvam P, Ghosh A, Barik DP, Mohapatra PKD. Molecular docking and simulation studies of natural compounds of Vitex negundo L. against papain-like protease (PL pro) of SARS CoV-2 (coronavirus) to conquer the pandemic situation in the world. J Biomol Struct Dyn 2021; 40:5665-5686. [PMID: 33459176 DOI: 10.1080/07391102.2021.1873185] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) is β-coronavirus that is responsible for the pandemic coronavirus disease 2019 (COVID-19) all over the world. The rapid spread of the novel SARS CoV-2 worldwide is raising a significant global public health issue with nearly 61.86 million people infected and 1.4 million deaths. To date, no specific drugs are available for the treatment of COVID-19. The inhibition of proteases essential for the proteolytic treatment of viral polyproteins is a conventional therapeutic strategy for conquering viral infections. In the study, molecular docking approach was used to screen potential drug compounds among the phytochemicals of Vitex negundo L. against COVID-19 infection. Molecular docking analysis showed that oleanolic acid forms a stable complex and other phyto-compounds ursolic acid, 3β-acetoxyolean-12-en-27-oic acid and isovitexin of V. negundo natural compounds form a less-stable complex. When compared with the control the synergistic interaction of these compounds shows inhibitory activity against papain-like protease (PLpro) of SARS CoV-2 (COVID-19). The molecular dynamics (MD) simulation (50 ns) were performed on the complexes of PLpro and the phyto-compounds viz. oleanolic acid, ursolic acid, 3β-acetoxyolean-12-en-27-oic acid and isovitexin followed by the binding free energy calculations using MM-GBSA and these molecules have stable interactions with PLpro protein binding site. The MD simulation study provides more insight into the functional properties of the protein-ligand complex and suggests that these molecules can be considered as a potential drug molecule against COVID-19. In this pandemic situation, these herbal compounds provide a rich resource to produce new antivirals against COVID-19.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Debasis Mitra
- Department of Microbiology, Raiganj University, Uttar Dinajpur, West Bengal, India
| | - Devvret Verma
- Department of Biotechnology, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, India
| | - Bhaswatimayee Mahakur
- Department of Botany and Biotechnology, Ravenshaw University, Cuttack, Odisha, India
| | - Anshul Kamboj
- Department of Biotechnology, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, India
| | - Rakesh Srivastava
- Department of Applied Sciences, NGF College of Engineering and Technology, Palwal, Haryana, India
| | - Sugam Gupta
- Department of Environmental Science, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, India
| | - Ajita Pandey
- School of Biotechnology, Gautam Buddha University, Greater Noida, Uttar Pradesh, India
| | | | - Kumud Pant
- Department of Biotechnology, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, India
| | - P Panneerselvam
- Microbiology, Crop Production Division, ICAR - National Rice Research Institute, Cuttack, Odisha, India
| | - Arabinda Ghosh
- Microbiology Division, Department of Botany, Gauhati University, Guwahati, Assam, India
| | - Durga P Barik
- Department of Botany and Biotechnology, Ravenshaw University, Cuttack, Odisha, India
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GC–MS analysis of phytocompounds and antihyperglycemic property of Hydrocotyle sibthorpioides Lam. SN APPLIED SCIENCES 2021. [DOI: 10.1007/s42452-020-04101-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
AbstractHydrocotyle sibthorpioides Lam. is a popular medicinal plant of Assam having several ethnomedicinal values. The present study investigated the metallic content, phytochemical contents, α-amylase, and α-glucosidase enzymes inhibitory property of H. sibthorpioides using in-vitro and in-silico methods. Heavy metal contents were analyzed using Atomic Absorption Spectroscopy. GC–MS was used to analyze the phytochemical compounds of the plant. Enzyme inhibition study was carried out by Spectrophotometry methods. The drug-likeness and toxicity properties of the phytocompounds were studied using SwissADME and ADMETlab databases. Docking and molecular visualizations were performed in AutoDock vina and Discovery studio tools. The study found that the extract of H. sibthorpioides contains a negligible amount of toxic elements. GC–MS analysis detected four compounds from the methanolic extract of the plant. Biochemical study showed considerable α-amylase and α-glucosidase enzyme inhibitory property of the crude extract of H. sibthorpioides. The IC50 of the plant extracts were found to be 1.27 mg/ml and 430.39 µg/ml for α-amylase and α-glucosidase enzymes, respectively. All four compounds were predicted to have potential drug-likeness properties with high cell membrane permeability, intestinal absorption, and less toxic effects. The docking study also showed strong binding affinities between the plant compounds and enzymes. Plant compound C2 showed an almost similar binding affinity with the α-amylase enzyme as compared to standard acarbose. The present study, thus, suggests the antihyperglycemic property of H. sibthorpioides and can be a potential source of antidiabetic drug candidates.
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Przybyłek M. Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening. Molecules 2020; 25:E5942. [PMID: 33333961 PMCID: PMC7765417 DOI: 10.3390/molecules25245942] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 12/12/2020] [Accepted: 12/14/2020] [Indexed: 12/14/2022] Open
Abstract
Beta-glucosidase inhibitors play important medical and biological roles. In this study, simple two-variable artificial neural network (ANN) classification models were developed for beta-glucosidase inhibitors screening. All bioassay data were obtained from the ChEMBL database. The classifiers were generated using 2D molecular descriptors and the data miner tool available in the STATISTICA package (STATISTICA Automated Neural Networks, SANN). In order to evaluate the models' accuracy and select the best classifiers among automatically generated SANNs, the Matthews correlation coefficient (MCC) was used. The application of the combination of maxHBint3 and SpMax8_Bhs descriptors leads to the highest predicting abilities of SANNs, as evidenced by the averaged test set prediction results (MCC = 0.748) calculated for ten different dataset splits. Additionally, the models were analyzed employing receiver operating characteristics (ROC) and cumulative gain charts. The thirteen final classifiers obtained as a result of the model development procedure were applied for a natural compounds collection available in the BIOFACQUIM database. As a result of this beta-glucosidase inhibitors screening, eight compounds were univocally classified as active by all SANNs.
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Affiliation(s)
- Maciej Przybyłek
- Department of Physical Chemistry, Pharmacy Faculty, Collegium Medicum of Bydgoszcz, Nicolaus Copernicus University in Toruń, Kurpińskiego 5, 85-950 Bydgoszcz, Poland
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Forrestall KL, Burley DE, Cash MK, Pottie IR, Darvesh S. 2-Pyridone natural products as inhibitors of SARS-CoV-2 main protease. Chem Biol Interact 2020; 335:109348. [PMID: 33278462 PMCID: PMC7710351 DOI: 10.1016/j.cbi.2020.109348] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 10/05/2020] [Accepted: 11/26/2020] [Indexed: 12/16/2022]
Abstract
The disease, COVID-19, is caused by the severe acute respiratory coronavirus 2 (SARS-CoV-2) for which there is currently no treatment. The SARS-CoV-2 main protease (Mpro) is an important enzyme for viral replication. Small molecules that inhibit this protease could lead to an effective COVID-19 treatment. The 2-pyridone scaffold was previously identified as a possible key pharmacophore to inhibit SARS-CoV-2 Mpro. A search for natural, antimicrobial products with the 2-pyridone moiety was undertaken herein, and their calculated potency as inhibitors of SARS-CoV-2 Mpro was investigated. Thirty-three natural products containing the 2-pyridone scaffold were identified from the literature. An in silico methodology using AutoDock was employed to predict the binding energies and inhibition constants (Ki values) for each 2-pyridone-containing compound with SARS-CoV-2 Mpro. This consisted of molecular optimization of the 2-pyridone compound, docking of the compound with a crystal structure of SARS-CoV-2 Mpro, and evaluation of the predicted interactions and ligand-enzyme conformations. All compounds investigated bound to the active site of SARS-CoV-2 Mpro, close to the catalytic dyad (His-41 and Cys-145). Thirteen molecules had predicted Ki values <1 μM. Glu-166 formed a key hydrogen bond in the majority of the predicted complexes, while Met-165 had some involvement in the complex binding as a close contact to the ligand. Prominent 2-pyridone compounds were further evaluated for their ADMET properties. This work has identified 2-pyridone natural products with calculated potent inhibitory activity against SARS-CoV-2 Mpro and with desirable drug-like properties, which may lead to the rapid discovery of a treatment for COVID-19. 2-pyridone-scaffold is an inhibitory pharmacophore for SARS-CoV-2 Mpro. Thirty-three natural, antimicrobial products identified with 2-pyridone moiety. All 2-pyridone natural products bind to active site of SARS-CoV-2 Mproin silico. Thirteen molecules found to have potent inhibitory activity against SARS-CoV-2 Mpro. Inhibition of SARS-CoV-2 by natural 2-pyridones may lead to treatment of COVID-19.
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Affiliation(s)
- Katrina L Forrestall
- Department of Medical Neuroscience, Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada
| | - Darcy E Burley
- Department of Medical Neuroscience, Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada
| | - Meghan K Cash
- Department of Medical Neuroscience, Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada
| | - Ian R Pottie
- Department of Chemistry and Physics, Faculty of Arts and Science, Mount Saint Vincent University, Halifax, Nova Scotia, B3M 2J6, Canada; Department of Chemistry, Faculty of Science, Saint Mary's University, Halifax, Nova Scotia, B3H 3C3, Canada
| | - Sultan Darvesh
- Department of Medical Neuroscience, Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada; Department of Chemistry and Physics, Faculty of Arts and Science, Mount Saint Vincent University, Halifax, Nova Scotia, B3M 2J6, Canada; Department of Medicine (Neurology), Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada.
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Nelms MD, Karmaus AL, Patlewicz G. An evaluation of the performance of selected (Q)SARs/expert systems for predicting acute oral toxicity. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2020; 16:100135. [PMID: 33163737 PMCID: PMC7641510 DOI: 10.1016/j.comtox.2020.100135] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Multiple US agencies use acute oral toxicity data in a variety of regulatory contexts. One of the ad-hoc groups that the US Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) established to implement the ICCVAM Strategic Roadmap was the Acute Toxicity Workgroup (ATWG) to support the development, acceptance, and actualisation of new approach methodologies (NAMs). One of the ATWG charges was to evaluate in vitro and in silico methods for predicting rat acute systemic toxicity. Collaboratively, the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM) and the US Environmental Protection Agency (US EPA) collected a large body of rat oral acute toxicity data (~16,713 studies for 11,992 substances) to serve as a reference set to evaluate the performance and coverage of new and existing models as well as build understanding of the inherent variability of the animal data. Here, we focus on evaluating in silico models for predicting the Lethal Dose (LD50) as implemented within two expert systems, TIMES and TEST. The performance and coverage were evaluated against the reference dataset. The performance of both models were similar, but TEST was able to make predictions for more chemicals than TIMES. The subset of the data with multiple (>3) LD50 values was used to evaluate the variability in data and served as a benchmark to compare model performance. Enrichment analysis was conducted using ToxPrint chemical fingerprints to identify the types of chemicals where predictions lay outside the upper 95% confidence interval. Overall, TEST and TIMES models performed similarly but had different chemical features associated with low accuracy predictions, reaffirming that these models are complementary and both worth evaluation when seeking to predict rat LD50 values.
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Affiliation(s)
- Mark D. Nelms
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, 37830, USA
- Center for Computational Toxicology & Exposure (CCTE), U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, 27709, USA
| | | | - Grace Patlewicz
- Center for Computational Toxicology & Exposure (CCTE), U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, 27709, USA
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Synthesis, Molecular Docking, Druglikeness Analysis, and ADMET Prediction of the Chlorinated Ethanoanthracene Derivatives as Possible Antidepressant Agents. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10217727] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Ethanoanthracene cycloadducts (5–7) anti, (5–7) syn, and (5–7) dec have been synthesized from the Diels–Alder (DA) reaction of diene 1,8-dichloroanthracene 2, with the dienophiles; acrylonitrile 3, 1-cynavinyl acetate 4, and phenyl vinyl sulfone 5, individually. The steric effect of dienophile substituents were more favorable toward the anti-isomer formation as deduced from 1H-NMR spectrum. The cheminformatics prediction for (5–7) anti and (5–7) syn was investigated. The in silico anticipated anti-depression activity of the (5–7) anti and (5–7) syn compounds were investigated and compared to maprotiline 9 as reference anti-depressant drug. The study showed that steric interactions play a crucial role in the binding affinity of these compounds to the representative models; 4xnx, 2QJU, and 3GWU. The pharmacokinetic and drug-like properties of (5–7) anti and (5–7) syn exhibited that these compounds could be represented as potential candidates for further development into antidepressant-like agents.
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Zadorozhnii PV, Kiselev VV, Kharchenko AV. In silico toxicity evaluation of Salubrinal and its analogues. Eur J Pharm Sci 2020; 155:105538. [PMID: 32889087 DOI: 10.1016/j.ejps.2020.105538] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 08/14/2020] [Accepted: 08/30/2020] [Indexed: 02/06/2023]
Abstract
This paper reports on a comprehensive in silico toxicity assessment of Salubrinal and its analogues containing a cinnamic acid residue or quinoline ring using the online servers admetSAR, ADMETlab, ProTox, ADVERPred, Pred-hERG and Vienna LiverTox. Apart from rare exceptions, in all 55 studied structures, mild or practical absence of acute toxicity was predicted for rats (III or IV toxicity class). Cardiotoxic, hepatotoxic and immunotoxic effects were predicted for Salubrinal and its analogues. We constructed models of the main predicted anti-targets hERG, BSEP, MRP3, MRP4 and AhR using the principle of homologous modeling. Molecular docking studies were carried out with the obtained models. We carried out molecular docking for all targets using AutoDock Vina, implemented in the PyRx 0.8 software package. According to the results of molecular docking, the compounds analyzed are potential moderate or weak hERG blockers. Induction of cholestasis and, as a consequence, liver damage by these drugs, directly related to inhibition of BSEP, MRP3 and MRP4, most likely will not be observed. Interaction with AhR for the studied compounds is impossible for steric reasons and, as a consequence, toxic effects on the immune and other organ systems associated with the activation of the AhR signaling pathway are excluded.
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Affiliation(s)
- Pavlo V Zadorozhnii
- Department of pharmacy and technology of organic substances, Ukrainian State University of Chemical Technology, Gagarin Ave., 8, Dnipro 49005, Ukraine.
| | - Vadym V Kiselev
- Department of pharmacy and technology of organic substances, Ukrainian State University of Chemical Technology, Gagarin Ave., 8, Dnipro 49005, Ukraine
| | - Aleksandr V Kharchenko
- Department of pharmacy and technology of organic substances, Ukrainian State University of Chemical Technology, Gagarin Ave., 8, Dnipro 49005, Ukraine
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Iwaniak A, Minkiewicz P, Pliszka M, Mogut D, Darewicz M. Characteristics of Biopeptides Released In Silico from Collagens Using Quantitative Parameters. Foods 2020; 9:E965. [PMID: 32708318 PMCID: PMC7404701 DOI: 10.3390/foods9070965] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 07/17/2020] [Accepted: 07/20/2020] [Indexed: 02/06/2023] Open
Abstract
The potential of collagens to release biopeptides was evaluated using the BIOPEP-UWM-implemented quantitative criteria including the frequency of the release of fragments with a given activity by selected enzyme(s) (AE), relative frequency of release of fragments with a given activity by selected enzyme(s) (W), and the theoretical degree of hydrolysis (DHt). Cow, pig, sheep, chicken, duck, horse, salmon, rainbow trout, goat, rabbit, and turkey collagens were theoretically hydrolyzed using: stem bromelain, ficin, papain, pepsin, trypsin, chymotrypsin, pepsin+trypsin, and pepsin+trypsin+chymotrypsin. Peptides released from the collagens having comparable AE and W were estimated for their likelihood to be bioactive using PeptideRanker Score. The collagens tested were the best sources of angiotensin I-converting enzyme (ACE) and dipeptidyl peptidase IV (DPP-IV) inhibitors. AE and W values revealed that pepsin and/or trypsin were effective producers of such peptides from the majority of the collagens examined. Then, the SwissTargetPrediction program was used to estimate the possible interactions of such peptides with enzymes and proteins, whereas ADMETlab was applied to evaluate their safety and drug-likeness properties. Target prediction revealed that the collagen-derived peptides might interact with several human proteins, especially proteinases, but with relatively low probability. In turn, their bioactivity may be limited by their short half-life in the body.
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Affiliation(s)
- Anna Iwaniak
- University of Warmia and Mazury in Olsztyn, Faculty of Food Science, Chair of Food Biochemistry, Pl. Cieszyński 1, 10-719 Olsztyn-Kortowo, Poland
| | - Piotr Minkiewicz
- University of Warmia and Mazury in Olsztyn, Faculty of Food Science, Chair of Food Biochemistry, Pl. Cieszyński 1, 10-719 Olsztyn-Kortowo, Poland
| | - Monika Pliszka
- University of Warmia and Mazury in Olsztyn, Faculty of Food Science, Chair of Food Biochemistry, Pl. Cieszyński 1, 10-719 Olsztyn-Kortowo, Poland
| | - Damir Mogut
- University of Warmia and Mazury in Olsztyn, Faculty of Food Science, Chair of Food Biochemistry, Pl. Cieszyński 1, 10-719 Olsztyn-Kortowo, Poland
| | - Małgorzata Darewicz
- University of Warmia and Mazury in Olsztyn, Faculty of Food Science, Chair of Food Biochemistry, Pl. Cieszyński 1, 10-719 Olsztyn-Kortowo, Poland
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47
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Jiang D, Lei T, Wang Z, Shen C, Cao D, Hou T. ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning. J Cheminform 2020; 12:16. [PMID: 33430990 PMCID: PMC7059329 DOI: 10.1186/s13321-020-00421-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 02/20/2020] [Indexed: 12/14/2022] Open
Abstract
Breast cancer resistance protein (BCRP/ABCG2), an ATP-binding cassette (ABC) efflux transporter, plays a critical role in multi-drug resistance (MDR) to anti-cancer drugs and drug–drug interactions. The prediction of BCRP inhibition can facilitate evaluating potential drug resistance and drug–drug interactions in early stage of drug discovery. Here we reported a structurally diverse dataset consisting of 1098 BCRP inhibitors and 1701 non-inhibitors. Analysis of various physicochemical properties illustrates that BCRP inhibitors are more hydrophobic and aromatic than non-inhibitors. We then developed a series of quantitative structure–activity relationship (QSAR) models to discriminate between BCRP inhibitors and non-inhibitors. The optimal feature subset was determined by a wrapper feature selection method named rfSA (simulated annealing algorithm coupled with random forest), and the classification models were established by using seven machine learning approaches based on the optimal feature subset, including a deep learning method, two ensemble learning methods, and four classical machine learning methods. The statistical results demonstrated that three methods, including support vector machine (SVM), deep neural networks (DNN) and extreme gradient boosting (XGBoost), outperformed the others, and the SVM classifier yielded the best predictions (MCC = 0.812 and AUC = 0.958 for the test set). Then, a perturbation-based model-agnostic method was used to interpret our models and analyze the representative features for different models. The application domain analysis demonstrated the prediction reliability of our models. Moreover, the important structural fragments related to BCRP inhibition were identified by the information gain (IG) method along with the frequency analysis. In conclusion, we believe that the classification models developed in this study can be regarded as simple and accurate tools to distinguish BCRP inhibitors from non-inhibitors in drug design and discovery pipelines.![]()
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Affiliation(s)
- Dejun Jiang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, People's Republic of China
| | - Tailong Lei
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, People's Republic of China
| | - Zhe Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, People's Republic of China
| | - Chao Shen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, People's Republic of China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410004, Hunan, People's Republic of China.
| | - Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, People's Republic of China.
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Yang H, Lou C, Li W, Liu G, Tang Y. Computational Approaches to Identify Structural Alerts and Their Applications in Environmental Toxicology and Drug Discovery. Chem Res Toxicol 2020; 33:1312-1322. [DOI: 10.1021/acs.chemrestox.0c00006] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Hongbin Yang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Chaofeng Lou
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
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49
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Alberga D, Trisciuzzi D, Mansouri K, Mangiatordi GF, Nicolotti O. Prediction of Acute Oral Systemic Toxicity Using a Multifingerprint Similarity Approach. Toxicol Sci 2020; 167:484-495. [PMID: 30371864 DOI: 10.1093/toxsci/kfy255] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The implementation of nonanimal approaches is of particular importance to regulatory agencies for the prediction of potential hazards associated with acute exposures to chemicals. This work was carried out in the framework of an international modeling initiative organized by the Acute Toxicity Workgroup (ATWG) of the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) with the participation of 32 international groups across government, industry, and academia. Our contribution was to develop a multifingerprints similarity approach for predicting five relevant toxicology endpoints related to the acute oral systemic toxicity that are: the median lethal dose (LD50) point prediction, the "nontoxic" (LD50 > 2000 mg/kg) and "very toxic" (LD50<50 mg/kg) binary classification, and the multiclass categorization of chemicals based on the United States Environmental Protection Agency and Globally Harmonized System of Classification and Labeling of Chemicals schemes. Provided by the ICCVAM's ATWG, the training set used to develop the models consisted of 8944 chemicals having high-quality rat acute oral lethality data. The proposed approach integrates the results coming from a similarity search based on 19 different fingerprint definitions to return a consensus prediction value. Moreover, the herein described algorithm is tailored to properly tackling the so-called toxicity cliffs alerting that a large gap in LD50 values exists despite a high structural similarity for a given molecular pair. An external validation set made available by ICCVAM and consisting in 2896 chemicals was employed to further evaluate the selected models. This work returned high-accuracy predictions based on the evaluations conducted by ICCVAM's ATWG.
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Affiliation(s)
- Domenico Alberga
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro," I-70126 Bari, Italy
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro," I-70126 Bari, Italy
| | - Kamel Mansouri
- ScitoVation LLC, Research Triangle Park, North Carolina 27709.,Integrated Laboratory Systems, Morrisville, NC 27560
| | - Giuseppe Felice Mangiatordi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro," I-70126 Bari, Italy.,Istituto Tumori IRCCS Giovanni Paolo II, 70124 Bari, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro," I-70126 Bari, Italy
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50
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Fu L, Liu L, Yang ZJ, Li P, Ding JJ, Yun YH, Lu AP, Hou TJ, Cao DS. Systematic Modeling of log D7.4 Based on Ensemble Machine Learning, Group Contribution, and Matched Molecular Pair Analysis. J Chem Inf Model 2019; 60:63-76. [DOI: 10.1021/acs.jcim.9b00718] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Li Fu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Lu Liu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Zhi-Jiang Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Pan Li
- Beijing Institute of Pharmaceutical Chemistry, Beijing 102205, P. R. China
| | - Jun-Jie Ding
- Beijing Institute of Pharmaceutical Chemistry, Beijing 102205, P. R. China
| | - Yong-Huan Yun
- College of Food Science and Engineering, Hainan University, Haikou 570228, P. R. China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR 999077, P. R. China
| | - Ting-Jun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR 999077, P. R. China
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