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Yang ZY, He JH, Lu AP, Hou TJ, Cao DS. Frequent hitters: nuisance artifacts in high-throughput screening. Drug Discov Today 2020; 25:657-667. [DOI: 10.1016/j.drudis.2020.01.014] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 12/28/2019] [Accepted: 01/16/2020] [Indexed: 11/27/2022]
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Yang ZY, Dong J, Yang ZJ, Lu AP, Hou TJ, Cao DS. Structural Analysis and Identification of False Positive Hits in Luciferase-Based Assays. J Chem Inf Model 2020; 60:2031-2043. [PMID: 32202787 DOI: 10.1021/acs.jcim.9b01188] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Luciferase-based bioluminescence detection techniques are highly favored in high-throughput screening (HTS), in which the firefly luciferase (FLuc) is the most commonly used variant. However, FLuc inhibitors can interfere with the activity of luciferase, which may result in false positive signals in HTS assays. In order to reduce the unnecessary cost of time and money, an in silico prediction model for FLuc inhibitors is highly desirable. In this study, we built an extensive data set consisting of 20 888 FLuc inhibitors and 198 608 noninhibitors, and then developed a group of classification models based on the combination of three machine learning (ML) algorithms and four types of molecular representations. The best prediction model based on XGBoost and ECFP4 and MOE2d descriptors yielded a balanced accuracy (BA) of 0.878 and an area under the receiver operating characteristic curve (AUC) value of 0.958 for the validation set, and a BA of 0.886 and an AUC of 0.947 for the test set. Three external validation sets, including set 1 (3231 FLuc inhibitors and 69 783 noninhibitors), set 2 (695 FLuc inhibitors and 75 913 noninhibitors), and set 3 (1138 FLuc inhibitors and 8155 noninhibitors), were used to verify the predictive ability of our models. The BA values for the three external validation sets given by the best model are 0.864, 0.845, and 0.791, respectively. In addition, the important features or structural fragments related to FLuc inhibitors were recognized by the Shapley additive explanations (SHAP) method along with their influences on predictions, which may provide valuable clues to detecting undesirable luciferase inhibitors. Based on the important and explanatory features, 16 rules were proposed for detecting FLuc inhibitors, which can achieve a correction rate of 70% for FLuc inhibitors. Furthermore, a comparison with existing prediction rules and models for FLuc inhibitors used in virtual screening verified the high reliability of the models and rules proposed in this study. We also used the model to screen three curated chemical databases, and almost 10% of the molecules in the evaluated databases were predicted as inhibitors, highlighting the potential risk of false positives in luciferase-based assays. Finally, a public web server called ChemFLuc was developed (http://admet.scbdd.com/chemfluc/index/), and it offers a free available service to predict potential FLuc inhibitors.
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Affiliation(s)
- Zi-Yi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003, P.R. China
| | - Jie Dong
- Central South University of Forestry and Technology, Changsha, 410004, P.R. China
| | - Zhi-Jiang Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003, P.R. China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, P.R. China
| | - Ting-Jun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P.R. China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003, P.R. China.,Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, P.R. China
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Langeder J, Grienke U, Chen Y, Kirchmair J, Schmidtke M, Rollinger JM. Natural products against acute respiratory infections: Strategies and lessons learned. JOURNAL OF ETHNOPHARMACOLOGY 2020; 248:112298. [PMID: 31610260 DOI: 10.1016/j.jep.2019.112298] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2019] [Revised: 10/08/2019] [Accepted: 10/09/2019] [Indexed: 06/10/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE A wide variety of traditional herbal remedies have been used throughout history for the treatment of symptoms related to acute respiratory infections (ARIs). AIM OF THE REVIEW The present work provides a timely overview of natural products affecting the most common pathogens involved in ARIs, in particular influenza viruses and rhinoviruses as well as bacteria involved in co-infections, their molecular targets, their role in drug discovery, and the current portfolio of available naturally derived anti-ARI drugs. MATERIALS AND METHODS Literature of the last ten years was evaluated for natural products active against influenza viruses and rhinoviruses. The collected bioactive agents were further investigated for reported activities against ARI-relevant bacteria, and analysed for the chemical space they cover in relation to currently known natural products and approved drugs. RESULTS An overview of (i) natural compounds active in target-based and/or phenotypic assays relevant to ARIs, (ii) extracts, and (iii) in vivo data are provided, offering not only a starting point for further in-depth phytochemical and antimicrobial studies, but also revealing insights into the most relevant anti-ARI scaffolds and compound classes. Investigations of the chemical space of bioactive natural products based on principal component analysis show that many of these compounds are drug-like. However, some bioactive natural products are substantially larger and have more polar groups than most approved drugs. A workflow with various strategies for the discovery of novel antiviral agents is suggested, thereby evaluating the merit of in silico techniques, the use of complementary assays, and the relevance of ethnopharmacological knowledge on the exploration of the therapeutic potential of natural products. CONCLUSIONS The longstanding ethnopharmacological tradition of natural remedies against ARIs highlights their therapeutic impact and remains a highly valuable selection criterion for natural materials to be investigated in the search for novel anti-ARI acting concepts. We observe a tendency towards assaying for broad-spectrum antivirals and antibacterials mainly discovered in interdisciplinary academic settings, and ascertain a clear demand for more translational studies to strengthen efforts for the development of effective and safe therapeutic agents for patients suffering from ARIs.
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Affiliation(s)
- Julia Langeder
- Department of Pharmacognosy, Faculty of Life Sciences, University of Vienna, Althanstraße 14, 1090, Vienna, Austria
| | - Ulrike Grienke
- Department of Pharmacognosy, Faculty of Life Sciences, University of Vienna, Althanstraße 14, 1090, Vienna, Austria.
| | - Ya Chen
- University of Hamburg, Center for Bioinformatics (ZBH), Bundesstraße 43, 22763, Hamburg, Germany
| | - Johannes Kirchmair
- Department of Chemistry, University of Bergen, N-5020, Bergen, Norway; Computational Biology Unit (CBU), University of Bergen, N-5020, Bergen, Norway
| | - Michaela Schmidtke
- Section of Experimental Virology, Department of Medical Microbiology, Jena University Hospital, Hans-Knöll-Straße 2, Jena, 07745, Germany
| | - Judith M Rollinger
- Department of Pharmacognosy, Faculty of Life Sciences, University of Vienna, Althanstraße 14, 1090, Vienna, Austria
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Yang ZY, He JH, Lu AP, Hou TJ, Cao DS. Application of Negative Design To Design a More Desirable Virtual Screening Library. J Med Chem 2020; 63:4411-4429. [DOI: 10.1021/acs.jmedchem.9b01476] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Zi-Yi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Jun-Hong He
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, SAR, 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 and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, SAR, P. R. China
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Wang J, Zhang Y, Liu YM, Yang XC, Chen YY, Wu GJ, He XH, Duan L, Dong Y, Ma RF. Uncovering the protective mechanism of Huoxue Anxin Recipe against coronary heart disease by network analysis and experimental validation. Biomed Pharmacother 2019; 121:109655. [PMID: 31734577 DOI: 10.1016/j.biopha.2019.109655] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 10/21/2019] [Accepted: 11/06/2019] [Indexed: 12/12/2022] Open
Abstract
Coronary heart disease (CHD) is a leading cause of death and disability worldwide. Huoxue Anxin Recipe (HAR) is a novel Chinese Herbal Medicine formula of that has been used to treat CHD for several decades. Our previous study found that HAR had anti-oxidative effects, and could promote myocardial angiogenesis and improve cardiac function following myocardial infarction (MI) in rats. However, the active compounds, potential targets, and biological processes related to HAR have not been systematically investigated. Here, network pharmacology and experimental validation were used to study the protective mechanisms of HAR against CHD. We identified 124 active components, 124 verified targets, and 111 predictive targets. A total of 1192 genes related to CHD were identified by cDNA microarray and database analysis. A total of 47 putative targets of HAR against CHD were identified, including 32 verified targets and 15 predictive targets. ClueGo enrichment analysis identified 49 biological processes involved in the anti-CHD effects of HAR. Among them, the negative regulation of blood coagulation and regulation of collagen biosynthetic process were experimentally validated. After constructing a protein-protein interaction network and clustering with MECODE and ClusterONE, 162 key proteins (from ClueGo and clustering) were used to construct an internal interaction network. Complement C3 (C3), Fibrinogen alpha (FGA), Fibrinogen gamma (FGG), interleukin-6 (IL6), and Apolipoprotein A1 (APOA1) were the top 5 hub proteins identified by cytoHubber analysis. HAR limited the concentrations of C3, FGA, FGG, and IL6 and increased APOA1 levels. The results indicated that HAR could down-regulate blood coagulation, regulate collagen biosynthesis, inhibit peroxidation and inflammation injury, and promote cholesterol efflux. HAR could be a potential source of novel and effective drugs for CHD.
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Affiliation(s)
- Jie Wang
- Department of Cardiology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China
| | - Yun Zhang
- Immunology Research Department, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China.
| | - Yong-Mei Liu
- Department of Cardiology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China.
| | - Xiao-Chen Yang
- Department of Cardiology & Health Care, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China
| | - Yin-Ying Chen
- Department of Research Office, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China
| | - Guang-Jun Wu
- Immunology Research Department, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China
| | - Xuan-Hui He
- Immunology Research Department, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China
| | - Lian Duan
- Department of Cardiology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China
| | - Yan Dong
- Department of Cardiology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China
| | - Ru-Feng Ma
- Department of Cardiology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China
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David L, Arús-Pous J, Karlsson J, Engkvist O, Bjerrum EJ, Kogej T, Kriegl JM, Beck B, Chen H. Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research. Front Pharmacol 2019; 10:1303. [PMID: 31749705 PMCID: PMC6848277 DOI: 10.3389/fphar.2019.01303] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 10/14/2019] [Indexed: 12/21/2022] Open
Abstract
In recent years, the development of high-throughput screening (HTS) technologies and their establishment in an industrialized environment have given scientists the possibility to test millions of molecules and profile them against a multitude of biological targets in a short period of time, generating data in a much faster pace and with a higher quality than before. Besides the structure activity data from traditional bioassays, more complex assays such as transcriptomics profiling or imaging have also been established as routine profiling experiments thanks to the advancement of Next Generation Sequencing or automated microscopy technologies. In industrial pharmaceutical research, these technologies are typically established in conjunction with automated platforms in order to enable efficient handling of screening collections of thousands to millions of compounds. To exploit the ever-growing amount of data that are generated by these approaches, computational techniques are constantly evolving. In this regard, artificial intelligence technologies such as deep learning and machine learning methods play a key role in cheminformatics and bio-image analytics fields to address activity prediction, scaffold hopping, de novo molecule design, reaction/retrosynthesis predictions, or high content screening analysis. Herein we summarize the current state of analyzing large-scale compound data in industrial pharmaceutical research and describe the impact it has had on the drug discovery process over the last two decades, with a specific focus on deep-learning technologies.
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Affiliation(s)
- Laurianne David
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
- Department of Life Science Informatics, B-IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Josep Arús-Pous
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
- Department of Chemistry and Biochemistry, University of Bern, Bern, Switzerland
| | - Johan Karlsson
- Quantitative Biology, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
| | - Ola Engkvist
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
| | - Esben Jannik Bjerrum
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
| | - Thierry Kogej
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
| | - Jan M. Kriegl
- Department of Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Bernd Beck
- Department of Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Hongming Chen
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
- Chemistry and Chemical Biology Centre, Guangzhou Regenerative Medicine and Health – Guangdong Laboratory, Guangzhou, China
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David L, Walsh J, Sturm N, Feierberg I, Nissink JWM, Chen H, Bajorath J, Engkvist O. Identification of Compounds That Interfere with High-Throughput Screening Assay Technologies. ChemMedChem 2019; 14:1795-1802. [PMID: 31479198 PMCID: PMC6856845 DOI: 10.1002/cmdc.201900395] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 08/21/2019] [Indexed: 01/23/2023]
Abstract
A significant challenge in high-throughput screening (HTS) campaigns is the identification of assay technology interference compounds. A Compound Interfering with an Assay Technology (CIAT) gives false readouts in many assays. CIATs are often considered viable hits and investigated in follow-up studies, thus impeding research and wasting resources. In this study, we developed a machine-learning (ML) model to predict CIATs for three assay technologies. The model was trained on known CIATs and non-CIATs (NCIATs) identified in artefact assays and described by their 2D structural descriptors. Usual methods identifying CIATs are based on statistical analysis of historical primary screening data and do not consider experimental assays identifying CIATs. Our results show successful prediction of CIATs for existing and novel compounds and provide a complementary and wider set of predicted CIATs compared to BSF, a published structure-independent model, and to the PAINS substructural filters. Our analysis is an example of how well-curated datasets can provide powerful predictive models despite their relatively small size.
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Affiliation(s)
- Laurianne David
- Hit Discovery, Discovery Sciences, R&D BioPharmaceuticalsAstraZeneca GoteborgPepparedsleden 1431 83MölndalSweden
- Department of Life Science Informatics, B-ITLIMES Program Unit Chemical Biology and Medicinal ChemistryRheinische Friedrich-Wilhelms-Universität BonnEndenicher Allee 19c53115BonnGermany
| | - Jarrod Walsh
- Hit Discovery, Discovery Sciences, R&D BioPharmaceuticalsAstraZeneca CambridgeAlderley ParkMacclesfieldSK10 4TGUK
| | - Noé Sturm
- Data Science and AI, Drug Safety & Metabolism, R&D BioPharmaceuticalsAstraZeneca GothenburgPepparedsleden 1431 83MölndalSweden
| | - Isabella Feierberg
- Hit Discovery, Discovery Sciences, R&D BioPharmaceuticalsAstraZeneca Boston35 Gatehouse DriveWalthamMA02451USA
| | - J. Willem M. Nissink
- Computational Chemistry, Oncology R&DAstraZenecaCambridge Science Park, Milton RoadCambridgeCB4 0WGUK
| | - Hongming Chen
- Hit Discovery, Discovery Sciences, R&D BioPharmaceuticalsAstraZeneca GoteborgPepparedsleden 1431 83MölndalSweden
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-ITLIMES Program Unit Chemical Biology and Medicinal ChemistryRheinische Friedrich-Wilhelms-Universität BonnEndenicher Allee 19c53115BonnGermany
| | - Ola Engkvist
- Hit Discovery, Discovery Sciences, R&D BioPharmaceuticalsAstraZeneca GoteborgPepparedsleden 1431 83MölndalSweden
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Yang ZY, Yang ZJ, Dong J, Wang LL, Zhang LX, Ding JJ, Ding XQ, Lu AP, Hou TJ, Cao DS. Structural Analysis and Identification of Colloidal Aggregators in Drug Discovery. J Chem Inf Model 2019; 59:3714-3726. [DOI: 10.1021/acs.jcim.9b00541] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Zi-Yi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410003, People’s Republic of China
| | - Zhi-Jiang Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410003, People’s Republic of China
| | - Jie Dong
- Central South University of Forestry and Technology, Changsha 410004, People’s Republic of China
| | - Liang-Liang Wang
- Beijing Institute of Pharmaceutical Chemistry, Beijing 102205, People’s Republic of China
| | - Liu-Xia Zhang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410003, People’s Republic of China
| | - Jun-Jie Ding
- Beijing Institute of Pharmaceutical Chemistry, Beijing 102205, People’s Republic of China
| | - Xiao-Qin Ding
- Beijing Institute of Pharmaceutical Chemistry, Beijing 102205, People’s Republic of China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong Special Administrative Region, People’s Republic of China
| | - Ting-Jun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, People’s Republic of China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410003, People’s Republic of China
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong Special Administrative Region, People’s Republic of China
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Dantas RF, Evangelista TCS, Neves BJ, Senger MR, Andrade CH, Ferreira SB, Silva-Junior FP. Dealing with frequent hitters in drug discovery: a multidisciplinary view on the issue of filtering compounds on biological screenings. Expert Opin Drug Discov 2019; 14:1269-1282. [DOI: 10.1080/17460441.2019.1654453] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Rafael Ferreira Dantas
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Tereza Cristina Santos Evangelista
- LaSOPB – Laboratório de Síntese Orgânica e Prospecção Biológica, Instituto de Química, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Bruno Junior Neves
- LabChem – Laboratory of Cheminformatics, Centro Universitário de Anápolis, UniEVANGÉLICA, Anápolis, Brazil
| | - Mario Roberto Senger
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Carolina Horta Andrade
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, Brazil
| | - Sabrina Baptista Ferreira
- LaSOPB – Laboratório de Síntese Orgânica e Prospecção Biológica, Instituto de Química, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Floriano Paes Silva-Junior
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
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Advancing Drug Discovery via Artificial Intelligence. Trends Pharmacol Sci 2019; 40:592-604. [DOI: 10.1016/j.tips.2019.06.004] [Citation(s) in RCA: 164] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 05/23/2019] [Accepted: 06/11/2019] [Indexed: 01/15/2023]
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Ferreira LL, Andricopulo AD. ADMET modeling approaches in drug discovery. Drug Discov Today 2019; 24:1157-1165. [DOI: 10.1016/j.drudis.2019.03.015] [Citation(s) in RCA: 102] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 02/08/2019] [Accepted: 03/14/2019] [Indexed: 12/31/2022]
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