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Yi J, Shi S, Fu L, Yang Z, Nie P, Lu A, Wu C, Deng Y, Hsieh C, Zeng X, Hou T, Cao D. OptADMET: a web-based tool for substructure modifications to improve ADMET properties of lead compounds. Nat Protoc 2024; 19:1105-1121. [PMID: 38263521 DOI: 10.1038/s41596-023-00942-4] [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] [Received: 11/16/2022] [Accepted: 10/27/2023] [Indexed: 01/25/2024]
Abstract
Lead optimization is a crucial step in the drug discovery process, which aims to design potential drug candidates from biologically active hits. During lead optimization, active hits undergo modifications to improve their absorption, distribution, metabolism, excretion and toxicity (ADMET) profiles. Medicinal chemists face key questions regarding which compound(s) should be synthesized next and how to balance multiple ADMET properties. Reliable transformation rules from multiple experimental analyses are critical to improve this decision-making process. We developed OptADMET ( https://cadd.nscc-tj.cn/deploy/optadmet/ ), an integrated web-based platform that provides chemical transformation rules for 32 ADMET properties and leverages prior experimental data for lead optimization. The multiproperty transformation rule database contains a total of 41,779 validated transformation rules generated from the analysis of 177,191 reliable experimental datasets. Additionally, 146,450 rules were generated by analyzing 239,194 molecular data predictions. OptADMET provides the ADMET profiles of all optimized molecules from the queried molecule and enables the prediction of desirable substructure transformations and subsequent validation of drug candidates. OptADMET is based on matched molecular pairs analysis derived from synthetic chemistry, thus providing improved practicality over other methods. OptADMET is designed for use by both experimental and computational scientists.
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Affiliation(s)
- Jiacai Yi
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China
- School of Computer Science, National University of Defense Technology, Changsha, China
| | - Shaohua Shi
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
| | - Li Fu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, China
| | - Ziyi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China
| | - Pengfei Nie
- National Supercomputer Center in Tianjin, Tianjin, China
| | - Aiping Lu
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
- Guangdong-Hong Kong-Macau Joint Lab on Chinese Medicine and Immune Disease Research, Guangzhou, China
| | - Chengkun Wu
- School of Computer Science, National University of Defense Technology, Changsha, China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, China
| | - Changyu Hsieh
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, China
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Xiangxiang Zeng
- Deparment of Computer Science, Hunan University, Changsha, China
| | - Tingjun Hou
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, China.
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China.
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China.
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2
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Kuralt V, Frlan R. Navigating the Chemical Space of ENR Inhibitors: A Comprehensive Analysis. Antibiotics (Basel) 2024; 13:252. [PMID: 38534687 DOI: 10.3390/antibiotics13030252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 02/29/2024] [Accepted: 03/07/2024] [Indexed: 03/28/2024] Open
Abstract
Antimicrobial resistance is a global health threat that requires innovative strategies against drug-resistant bacteria. Our study focuses on enoyl-acyl carrier protein reductases (ENRs), in particular FabI, FabK, FabV, and InhA, as potential antimicrobial agents. Despite their promising potential, the lack of clinical approvals for inhibitors such as triclosan and isoniazid underscores the challenges in achieving preclinical success. In our study, we curated and analyzed a dataset of 1412 small molecules recognized as ENR inhibitors, investigating different structural variants. Using advanced cheminformatic tools, we mapped the physicochemical landscape and identified specific structural features as key determinants of bioactivity. Furthermore, we investigated whether the compounds conform to Lipinski rules, PAINS, and Brenk filters, which are crucial for the advancement of compounds in development pipelines. Furthermore, we investigated structural diversity using four different representations: Chemotype diversity, molecular similarity, t-SNE visualization, molecular complexity, and cluster analysis. By using advanced bioinformatics tools such as matched molecular pairs (MMP) analysis, machine learning, and SHAP analysis, we were able to improve our understanding of the activity cliques and the precise effects of the functional groups. In summary, this chemoinformatic investigation has unraveled the FAB inhibitors and provided insights into rational antimicrobial design, seamlessly integrating computation into the discovery of new antimicrobial agents.
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Affiliation(s)
- Vid Kuralt
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Ljubljana, 1000 Ljubljana, Slovenia
| | - Rok Frlan
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Ljubljana, 1000 Ljubljana, Slovenia
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Yang ZY, Fu L, Lu AP, Liu S, Hou TJ, Cao DS. Semi-automated workflow for molecular pair analysis and QSAR-assisted transformation space expansion. J Cheminform 2021; 13:86. [PMID: 34774096 PMCID: PMC8590336 DOI: 10.1186/s13321-021-00564-6] [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: 07/08/2021] [Accepted: 10/30/2021] [Indexed: 12/01/2022] Open
Abstract
In the process of drug discovery, the optimization of lead compounds has always been a challenge faced by pharmaceutical chemists. Matched molecular pair analysis (MMPA), a promising tool to efficiently extract and summarize the relationship between structural transformation and property change, is suitable for local structural optimization tasks. Especially, the integration of MMPA with QSAR modeling can further strengthen the utility of MMPA in molecular optimization navigation. In this study, a new semi-automated procedure based on KNIME was developed to support MMPA on both large- and small-scale datasets, including molecular preparation, QSAR model construction, applicability domain evaluation, and MMP calculation and application. Two examples covering regression and classification tasks were provided to gain a better understanding of the importance of MMPA, which has also shown the reliability and utility of this MMPA-by-QSAR pipeline. ![]()
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Affiliation(s)
- Zi-Yi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, People's Republic of China.,Hunan Key Laboratory of Diagnostic and Therapeutic Drug Research for Chronic Diseases, Changsha, 410013, Hunan, China
| | - Li Fu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, People's Republic of China.,Hunan Key Laboratory of Diagnostic and Therapeutic Drug Research for Chronic Diseases, Changsha, 410013, Hunan, China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, 999077, SAR, People's Republic of China
| | - Shao Liu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China
| | - Ting-Jun Hou
- Hangzhou Institute of Innovative Medicine, 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, 410013, Hunan, People's Republic of China. .,Hunan Key Laboratory of Diagnostic and Therapeutic Drug Research for Chronic Diseases, Changsha, 410013, Hunan, China. .,Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, 999077, SAR, People's Republic of China.
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5
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Kovalishyn V, Zyabrev V, Kachaeva M, Ziabrev K, Keith K, Harden E, Hartline C, James SH, Brovarets V. Design of new imidazole derivatives with anti-HCMV activity: QSAR modeling, synthesis and biological testing. J Comput Aided Mol Des 2021; 35:1177-1187. [PMID: 34766232 DOI: 10.1007/s10822-021-00428-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 10/26/2021] [Indexed: 11/29/2022]
Abstract
The problem of designing new antiviral drugs against Human Cytomegalovirus (HCMV) was addressed using the Online Chemical Modeling Environment (OCHEM). Data on compound antiviral activity to human organisms were collected from the literature and uploaded in the OCHEM database. The predictive ability of the regression models was tested through cross-validation, giving coefficient of determination q2 = 0.71-0.76. The validation of the models using an external test set proved that the models can be used to predict the activity of newly designed compounds with reasonable accuracy within the applicability domain (q2 = 0.70-0.74). The models were applied to screen a virtual chemical library of imidazole derivatives, which was designed to have antiviral activity. The six most promising compounds were identified, synthesized and their antiviral activities against HCMV were evaluated in vitro. However, only two of them showed some activity against the HCMV AD169 strain.
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Affiliation(s)
- Vasyl Kovalishyn
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Science of Ukraine, 1 Murmanska Str, Kyiv, 02094, Ukraine.
| | - Volodymyr Zyabrev
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Science of Ukraine, 1 Murmanska Str, Kyiv, 02094, Ukraine
| | - Maryna Kachaeva
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Science of Ukraine, 1 Murmanska Str, Kyiv, 02094, Ukraine
| | - Kostiantyn Ziabrev
- Institute of Organic Chemistry, National Academy of Sciences, 5, Murmanska Str, Kyiv, 02660, Ukraine.,Click Chemistry Tools, East Gelging Dr, Scottsdale, AZ, 834185260, USA
| | - Kathy Keith
- Department of Pediatrics, Division of Pediatric Infectious Diseases, University of Alabama at Birmingham, Birmingham, Alabama, 35233, USA
| | - Emma Harden
- Department of Pediatrics, Division of Pediatric Infectious Diseases, University of Alabama at Birmingham, Birmingham, Alabama, 35233, USA
| | - Caroll Hartline
- Department of Pediatrics, Division of Pediatric Infectious Diseases, University of Alabama at Birmingham, Birmingham, Alabama, 35233, USA
| | - Scott H James
- Department of Pediatrics, Division of Pediatric Infectious Diseases, University of Alabama at Birmingham, Birmingham, Alabama, 35233, USA
| | - Volodymyr Brovarets
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Science of Ukraine, 1 Murmanska Str, Kyiv, 02094, Ukraine
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Tinkov OV, Grigorev VY, Grigoreva LD. Prediction of an Organic Compound’s Biotransformation Time: A Study Using Avermectins. MOSCOW UNIVERSITY CHEMISTRY BULLETIN 2021. [PMCID: PMC8382113 DOI: 10.3103/s0027131421040088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The current spread of the SARS-CoV-2 coronavirus is a challenge for the entire world. Ivermectin is a promising agent, which could be used to combat the SARS-CoV-2 coronavirus. It represents a complex of semisynthetic derivatives of natural avermectins that have been taken advantage of for a long time in medicine and agriculture as antiparasitic drugs. However, the experimental ecotoxicology assessment data for individual avermectins are still scarce. In relation to this, the aim of this study is to develop a mathematical model that would allow reliably predicting the biotransformation ability of natural and semisynthetic avermectins and identifying the structural fragments of avermectin molecules that have the largest impact on this biological activity. The base for the model construction was a structurally heterogeneous set including organic compounds with experimentally determined biotransformation half-life periods (KmHL). Using the OCHEM web platform (https://ochem.eu) with the implemented PyDescriptor plugin for the descriptor calculation and Random Forest and Transformer-CNN algorithms, a satisfactory (\documentclass[12pt]{minimal}
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\begin{document}$$R_{{{\text{test}}}}^{2}$$\end{document} = 0.81) Quantitative Relationship Structure—Activity (QSAR) model was developed. The subsequent calculations have shown that natural avermectins undergo on average faster biotransformation in fish than the semisynthetic ones. In addition, structural fragments that increase and decrease the biotransformation rate are identified.
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Tinkov OV, Grigorev VY, Grigoreva LD. QSAR analysis of the acute toxicity of avermectins towards Tetrahymena pyriformis. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:541-571. [PMID: 34157880 DOI: 10.1080/1062936x.2021.1932583] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 05/17/2021] [Indexed: 06/13/2023]
Abstract
Avermectins have been effectively used in medicine, veterinary medicine, and agriculture as antiparasitic agents for many years. However, there are still no reliable data on the main ecotoxicological characteristics of most individual avermectins. Although many QSAR models have been proposed to describe the acute toxicity of organic compounds towards Tetrahymena pyriformis (T. pyriformis), avermectins are outside the applicability domain of these models. The influence of the molecular structures of various organic compounds on the acute toxicity towards T. pyriformis was studied using the OCHEM web platform (https://ochem.eu). A data set of 1792 toxicants was used to create models. The QSAR (Quantitative Structure-Activity Relationship) models were developed using the molecular descriptors Dragon, ISIDA, CDK, PyDescriptor, alvaDesc, and SIRMS and machine learning methods, such as Least Squares Support Vector Machine and Transformer Convolutional Neural Network. The HYBOT descriptors and Random Forest were used for a comparative QSAR investigation. Since the best predictive ability was demonstrated by the Transformer Convolutional Neural Network model, it was used to predict the toxicity of individual avermectins towards T. pyriformis. During a structural interpretation of the developed QSAR model, we determined the significant molecular transformations that increase and decrease the acute toxicity of organic compounds.
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Affiliation(s)
- O V Tinkov
- Department of Pharmacology and Pharmaceutical Chemistry, Medical Faculty, Shevchenko Transnistria State University, Tiraspol, Moldova
- Department of Computer Science, Military Institute of the Ministry of Defense, Tiraspol, Moldova
| | - V Y Grigorev
- Department of Computer-aided Molecular Design, Institute of Physiologically Active Compounds of the Russian Academy of Science, Chernogolovka, Russia
| | - L D Grigoreva
- Department of Fundamental Physicochemical Engineering, Moscow State University, Moscow, Russia
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8
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Tinkov OV, Grigorev VY, Grigoreva LD. Virtual Screening and Molecular Design of Potential SARS-COV-2 Inhibitors. MOSCOW UNIVERSITY CHEMISTRY BULLETIN 2021. [PMCID: PMC8207500 DOI: 10.3103/s0027131421020127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
According to recent studies, the main Mpro protease of the SARS-CoV-2 virus, which is the most important target in the development of promising drugs for the treatment of COVID-19, is evolutionarily conservative and has not undergone significant changes compared with the main Mpro protease of the SARS-CoV virus. Many researchers note the similarity between the binding sites of the main Mpro protease of SARS-CoV and SARS-CoV-2 viruses; thus, with the spreading epidemic, further studies on inhibitors of the main Mpro protease of the SARS-CoV virus to fight COVID-19 seems logical. In the course of the study, satisfactory QSAR models are built using simplex, fractal, and HYBOT descriptors; the Partial Least Squares (PLS), Random Forest (RF), Support Vectors, Gradient Boosting (GBM) methods; and the OCHEM Internet platform (https://ochem.eu), in which different types of molecular descriptors and machine learning methods are implemented. The structural interpretation, which allowed us to identify molecular fragments that increase and decrease the activity of SARS-CoV inhibitors, is performed for the obtained models. The results of the structural interpretation are used for the rational molecular design of potential SARS-CoV-2 inhibitors. The resulting QSAR models are used for the virtual screening of 2087 FDA-approved drugs.
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9
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Tinkov O, Polishchuk P, Matveieva M, Grigorev V, Grigoreva L, Porozov Y. The Influence of Structural Patterns on Acute Aquatic Toxicity of Organic Compounds. Mol Inform 2020; 40:e2000209. [PMID: 33029954 DOI: 10.1002/minf.202000209] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 10/01/2020] [Indexed: 12/28/2022]
Abstract
Investigation of the influence of molecular structure of different organic compounds on acute toxicity towards Fathead minnow, Daphnia magna, and Tetrahymena pyriformis has been carried out using 2D simplex representation of molecular structure and two modelling methods: Random Forest (RF) and Gradient Boosting Machine (GBM). Suitable QSAR (Quantitative Structure - Activity Relationships) models were obtained. The study was focused on QSAR models interpretation. The aim of the study was to develop a set of structural fragments that simultaneously consistently increase toxicity toward Fathead minnow, Daphnia magna, Tetrahymena pyriformis. The interpretation allowed to gain more details about known toxicophores and to propose new fragments. The results obtained made it possible to rank the contributions of molecular fragments to various types of toxicity to aquatic organisms. This information can be used for molecular optimization of chemicals. According to the results of structural interpretation, the most significant common mechanisms of the toxic effect of organic compounds on Fathead minnow, Daphnia magna and Tetrahymena pyriformis are reactions of nucleophilic substitution and inhibition of oxidative phosphorylation in mitochondria. In addition acetylcholinesterase and voltage-gated ion channel of Fathead minnow and Daphnia magna are important targets for toxicants. The on-line version of the OCHEM expert system (https://ochem.eu) were used for a comparative QSAR investigation. The proposed QSAR models comply with the OECD principles and can be used to reliably predict acute toxicity of organic compounds towards Fathead minnow, Daphnia magna and Tetrahymena pyriformis with allowance for applicability domain estimation.
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Affiliation(s)
- Oleg Tinkov
- Department of Computer Science, Military Institute of the Ministry of Defense, 3300, Gogol str. 2"B", Tiraspol, Transdniestria, Moldova.,Department of Pharmacology and Pharmaceutical Chemistry, Medical Faculty, Transnistrian State University, 3300, October 25 str. 128, Tiraspol, Transdniestria, Moldova
| | - Pavel Polishchuk
- Institute of Molecular and Translational Medicine Faculty of Medicine and Dentistry Palacký University and University Hospital in Olomouc, Hnevotinska 5, 77900, Olomouc, Czech Republic
| | - Mariia Matveieva
- Institute of Molecular and Translational Medicine Faculty of Medicine and Dentistry Palacký University and University Hospital in Olomouc, Hnevotinska 5, 77900, Olomouc, Czech Republic
| | - Veniamin Grigorev
- Institute of Physiologically Active Compounds, Russian Academy of Sciences, 142432, Severniy proezd 1, Chernogolovka, Moscow region, Russia
| | - Ludmila Grigoreva
- Department of Fundamental Physical and Chemical Engineering, Moscow State University, 119991, Leninskiye Gory 1/51, Moscow, Russia
| | - Yuri Porozov
- World-Class Research Center "Digital biodesign and personalized healthcare", I.M. Sechenov First Moscow State Medical University, Moscow, Russia.,Department of Computational Biology, Sirius University of Science and Technology, 354340, Olympic Ave 1, Sochi, Russia
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Pennington LD, Aquila BM, Choi Y, Valiulin RA, Muegge I. Positional Analogue Scanning: An Effective Strategy for Multiparameter Optimization in Drug Design. J Med Chem 2020; 63:8956-8976. [PMID: 32330036 DOI: 10.1021/acs.jmedchem.9b02092] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Minimizing the number and duration of design cycles needed to optimize hit or lead compounds into high-quality chemical probes or drug candidates is an ongoing challenge in biomedical research. Small structure modifications to hit or lead compounds can have meaningful impacts on pharmacological profiles due to significant effects on molecular and physicochemical properties and intra- and intermolecular interactions. Rapid pharmacological profiling of an efficiently prepared series of positional analogues stemming from the systematic exchange of methine groups with heteroatoms or other substituents in aromatic or heteroaromatic ring-containing hit or lead compounds is one approach toward minimizing design cycles (e.g., exchange of aromatic or heteroaromatic CH groups with N atoms or CF, CMe, or COH groups). In this Perspective, positional analogue scanning is shown to be an effective strategy for multiparameter optimization in drug design, whereby substantial improvements in a variety of pharmacological parameters can be achieved.
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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: 27] [Impact Index Per Article: 5.4] [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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12
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Tinkov O, Grigorev V, Polishchuk P, Yarkov A, Raevsky O. QSAR investigation of acute toxicity of organic compounds during oral administration to mice. ACTA ACUST UNITED AC 2019; 65:123-132. [DOI: 10.18097/pbmc20196502123] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The effect of the structure of organic compounds on the acute toxicity upon oral injection in mice was studied using 2D simplex representation of the molecular structure and Random forest (RF) methods. Satisfactory quantitative structure-activity relationship (QSAR) models were constructed (R2 test = 0,61–0,62). The interpretation of the obtained QSAR models was carried out. The contributions of known toxicophores with established mechanisms of action were calculated in order to confirm the ability of the interpretation approach to correctly rank them relative to other structural fragments. The influence of the molecular surroundings of some toxicophores was analyzed. We analyzed the contributions of other highly ranked fragments from the list of common functional groups and ring systems in order to find new potential toxicophores. The on-line version of the expert system “OCHEM” (https://ochem.eu) and Arithmetic Mean Toxicity (AMT) approach were used for a comparative QSAR study.
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Affiliation(s)
- O.V. Tinkov
- Military Institute of the Ministry of Defense, Tiraspol, Moldova
| | - V.Yu. Grigorev
- Institute of Physiologically Active Compounds, Russian Academy of Sciences, Chernogolovka, Russia
| | - P.G. Polishchuk
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacký University, Olomouc, Czech Republic
| | - A.V. Yarkov
- Institute of Physiologically Active Compounds, Russian Academy of Sciences, Chernogolovka, Russia
| | - O.A. Raevsky
- Institute of Physiologically Active Compounds, Russian Academy of Sciences, Chernogolovka, Russia
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Koutsoukas A, Chang G, Keefer CE. In-Silico Extraction of Design Ideas Using MMPA-by-QSAR and its Application on ADME Endpoints. J Chem Inf Model 2018; 59:477-485. [DOI: 10.1021/acs.jcim.8b00520] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Alexios Koutsoukas
- Computational ADME Group, Department of Pharmacokinetics, Dynamics, and Metabolism, Pfizer Worldwide Research & Development, Groton, Connecticut 06340, United States
| | - George Chang
- Computational ADME Group, Department of Pharmacokinetics, Dynamics, and Metabolism, Pfizer Worldwide Research & Development, Groton, Connecticut 06340, United States
| | - Christopher E. Keefer
- Computational ADME Group, Department of Pharmacokinetics, Dynamics, and Metabolism, Pfizer Worldwide Research & Development, Groton, Connecticut 06340, United States
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Matveieva M, Cronin MTD, Polishchuk P. Interpretation of QSAR Models: Mining Structural Patterns Taking into Account Molecular Context. Mol Inform 2018; 38:e1800084. [DOI: 10.1002/minf.201800084] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2018] [Accepted: 09/27/2018] [Indexed: 01/22/2023]
Affiliation(s)
- Mariia Matveieva
- Institute of Molecular and Translational MedicineFaculty of Medicine and DentistryPalacký University and University Hospital in Olomouc Hnevotinska 5, 77900 Olomouc Czech Republic
| | - Mark T. D. Cronin
- School of Pharmacy and Biomolecular SciencesLiverpool John Moores University Byrom Street Liverpool L3 3AF United Kingdom
| | - Pavel Polishchuk
- Institute of Molecular and Translational MedicineFaculty of Medicine and DentistryPalacký University and University Hospital in Olomouc Hnevotinska 5, 77900 Olomouc Czech Republic
- A.M. Butlerov Institute of ChemistryKazan Federal University Kremlevskaya Str. 10 Kazan Russia
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15
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Svensson F, Aniceto N, Norinder U, Cortes-Ciriano I, Spjuth O, Carlsson L, Bender A. Conformal Regression for Quantitative Structure–Activity Relationship Modeling—Quantifying Prediction Uncertainty. J Chem Inf Model 2018; 58:1132-1140. [DOI: 10.1021/acs.jcim.8b00054] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Fredrik Svensson
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
- IOTA Pharmaceuticals, St Johns Innovation Centre, Cowley Road, Cambridge CB4 0WS, U.K
| | - Natalia Aniceto
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Ulf Norinder
- Swetox, Unit of Toxicology Sciences, Karolinska Institutet, Forskargatan 20, SE-151 36 Södertälje, Sweden
- Department of Computer and Systems Sciences, Stockholm University, Box 7003, SE-164 07 Kista, Sweden
| | - Isidro Cortes-Ciriano
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, SE-75124, Uppsala Sweden
| | - Lars Carlsson
- Quantitative Biology, Discovery Sciences, IMED Biotech Unit, AstraZeneca, SE-43183, Mölndal, Sweden
- Department of Computer Science, Royal Holloway, University of London, Egham Hill, Surrey, U.K
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
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16
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Withnall M, Chen H, Tetko IV. Matched Molecular Pair Analysis on Large Melting Point Datasets: A Big Data Perspective. ChemMedChem 2018; 13:599-606. [PMID: 28650584 PMCID: PMC5900986 DOI: 10.1002/cmdc.201700303] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Revised: 06/26/2017] [Indexed: 11/11/2022]
Abstract
A matched molecular pair (MMP) analysis was used to examine the change in melting point (MP) between pairs of similar molecules in a set of ∼275k compounds. We found many cases in which the change in MP (ΔMP) of compounds correlates with changes in functional groups. In line with the results of a previous study, correlations between ΔMP and simple molecular descriptors, such as the number of hydrogen bond donors, were identified. In using a larger dataset, covering a wider chemical space and range of melting points, we observed that this method remains stable and scales well with larger datasets. This MMP-based method could find use as a simple privacy-preserving technique to analyze large proprietary databases and share findings between participating research groups.
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Affiliation(s)
- Michael Withnall
- Helmholtz Zentrum München—German Research Center for Environmental Health, GmbHInstitute of Structural BiologyNeuherbergGermany
| | - Hongming Chen
- External Sciences, Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&D GothenburgMölndal43183Sweden
| | - Igor V. Tetko
- Helmholtz Zentrum München—German Research Center for Environmental Health, GmbHInstitute of Structural BiologyNeuherbergGermany
- BIGCHEM GmbHIngolstädter Landstraße 1, b. 60w85764NeuherbergGermany
- Institute of Structural Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, GmbHIngolstädter Landstraße 185764NeuherbergGermany
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17
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Polishchuk P. Interpretation of Quantitative Structure–Activity Relationship Models: Past, Present, and Future. J Chem Inf Model 2017; 57:2618-2639. [DOI: 10.1021/acs.jcim.7b00274] [Citation(s) in RCA: 120] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Pavel Polishchuk
- Institute of Molecular and
Translational Medicine, Faculty of Medicine and Dentistry, Palacký University and University Hospital in Olomouc, Hněvotínská
1333/5, 779 00 Olomouc, Czech Republic
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18
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Lukac I, Zarnecka J, Griffen EJ, Dossetter AG, St-Gallay SA, Enoch SJ, Madden JC, Leach AG. Turbocharging Matched Molecular Pair Analysis: Optimizing the Identification and Analysis of Pairs. J Chem Inf Model 2017; 57:2424-2436. [DOI: 10.1021/acs.jcim.7b00335] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Iva Lukac
- School
of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, U.K
| | - Joanna Zarnecka
- School
of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, U.K
| | | | | | | | - Steven J. Enoch
- School
of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, U.K
| | - Judith C. Madden
- School
of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, U.K
| | - Andrew G. Leach
- School
of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, U.K
- MedChemica Ltd., BioHub, Alderley
Park, Macclesfield SK10
4TG, U.K
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19
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Lombardo F, Desai PV, Arimoto R, Desino KE, Fischer H, Keefer CE, Petersson C, Winiwarter S, Broccatelli F. In Silico Absorption, Distribution, Metabolism, Excretion, and Pharmacokinetics (ADME-PK): Utility and Best Practices. An Industry Perspective from the International Consortium for Innovation through Quality in Pharmaceutical Development. J Med Chem 2017; 60:9097-9113. [DOI: 10.1021/acs.jmedchem.7b00487] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Franco Lombardo
- Alkermes Inc., 852 Winter Street, Waltham, Massachusetts 02451, United States
| | - Prashant V. Desai
- Computational
ADME, Drug Disposition, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Rieko Arimoto
- Vertex Pharmaceuticals Inc., 50 Northern Avenue, Boston, Massachusetts 02210, United States
| | | | - Holger Fischer
- Roche
Pharmaceutical Research and Early Development, Pharmaceutical Sciences,
Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
| | | | - Carl Petersson
- Discovery Drug Disposition, Biopharma, R&D Global Early Development, EMD Serono, Frankfurter Strasse 250 I Postcode D39/001, 64293 Darmstadt, Germany
| | - Susanne Winiwarter
- Drug Safety and Metabolism, AstraZeneca R&D Gothenburg, 431 83 Mölndal, Sweden
| | - Fabio Broccatelli
- Genentech Inc., South San Francisco, California 94080, United States
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20
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Aliagas I, Berger R, Goldberg K, Nishimura RT, Reilly J, Richardson P, Richter D, Sherer EC, Sparling BA, Bryan MC. Sustainable Practices in Medicinal Chemistry Part 2: Green by Design. J Med Chem 2017; 60:5955-5968. [DOI: 10.1021/acs.jmedchem.6b01837] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Ignacio Aliagas
- Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Raphaëlle Berger
- MRL, Merck & Co., Inc., 2015 Galloping Hill Road, Kenilworth, New Jersey 07033, United States
| | - Kristin Goldberg
- Innovative Medicines Unit, AstraZeneca, Building 310, Milton Science Park, Cambridge, CB4 0FZ, U.K
| | - Rachel T. Nishimura
- Janssen Research & Development, LLC, 3210 Merryfield Row, San Diego, California 92121, United States
| | - John Reilly
- Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Paul Richardson
- Pfizer Global Research and Development, 10777 Science Center Drive (CB2), San Diego, California 92121, United States
| | - Daniel Richter
- Pfizer Global Research and Development, 10777 Science Center Drive (CB2), San Diego, California 92121, United States
| | - Edward C. Sherer
- MRL, Merck & Co., Inc., P.O. Box 2000, Rahway, New Jersey 07065, United States
| | - Brian A. Sparling
- Amgen, Inc., 360 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Marian C. Bryan
- Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
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21
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Abstract
It is widely accepted that modern QSAR began in the early 1960s. However, as long ago as 1816 scientists were making predictions about physical and chemical properties. The first investigations into the correlation of biological activities with physicochemical properties such as molecular weight and aqueous solubility began in 1841, almost 60 years before the important work of Overton and Meyer linking aquatic toxicity to lipid-water partitioning. Throughout the 20th century QSAR progressed, though there were many lean years. In 1962 came the seminal work of Corwin Hansch and co-workers, which stimulated a huge interest in the prediction of biological activities. Initially that interest lay largely within medicinal chemistry and drug design, but in the 1970s and 1980s, with increasing ecotoxicological concerns, QSAR modelling of environmental toxicities began to grow, especially once regulatory authorities became involved. Since then QSAR has continued to expand, with over 1400 publications annually from 2011 onwards.
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22
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Structural, Physicochemical and Stereochemical Interpretation of QSAR Models Based on Simplex Representation of Molecular Structure. CHALLENGES AND ADVANCES IN COMPUTATIONAL CHEMISTRY AND PHYSICS 2017. [DOI: 10.1007/978-3-319-56850-8_4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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23
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Aniceto N, Freitas AA, Bender A, Ghafourian T. A novel applicability domain technique for mapping predictive reliability across the chemical space of a QSAR: reliability-density neighbourhood. J Cheminform 2016. [PMCID: PMC5395519 DOI: 10.1186/s13321-016-0182-y] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
The ability to define the regions of chemical space where a predictive model can be safely used is a necessary condition to assure the reliability of new predictions. This implies that reliability must be determined across chemical space in the attempt to localize “safe” and “unsafe” regions for prediction. As a result we devised an applicability domain technique that addresses the data locally instead of handling it as a whole—the reliability-density neighbourhood (RDN). The main novelty aspect of this method is that it characterizes each single training instance according to the density of its neighbourhood in the training set, as well as its individual bias and precision. By scanning through the chemical space (by iteratively increasing the applicability domain area), it was observed that new test compounds are successively included into the applicability domain region in such a manner that strongly correlates to their predictive performance. This allows the mapping of local reliability across different locations in the training set space, and thus allows identifying regions where the model has low reliability. This method also showed matching profiles between two external sets, which is an indication that it performs robustly with new data. Another novel aspect in this technique is that it is paired with a specific feature selection algorithm. As a result, the impact of the feature set used was studied from which the top 20 features selected by ReliefF yielded the best results, as opposed to using the model’s features or the entire feature set as commonly done. As the third novel aspect, in this work we propose a new scoring function to help evaluate the quality of an applicability domain profile (i.e., the curve of accuracy vs the applicability domain measure in question). Overall, the RDN showed to be a promising method that can correctly sort new instances according to predictive performance. As a result, this technique can be received by an end-user as proof of concept for the performance of a QSAR model in new data, thus promoting the user’s trust on the QSAR output.. ![]()
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24
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Tetko IV, Maran U, Tropsha A. Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development. Mol Inform 2016; 36. [PMID: 27778468 DOI: 10.1002/minf.201600082] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Accepted: 10/03/2016] [Indexed: 01/08/2023]
Abstract
Thousands of (Quantitative) Structure-Activity Relationships (Q)SAR models have been described in peer-reviewed publications; however, this way of sharing seldom makes models available for the use by the research community outside of the developer's laboratory. Conversely, on-line models allow broad dissemination and application representing the most effective way of sharing the scientific knowledge. Approaches for sharing and providing on-line access to models range from web services created by individual users and laboratories to integrated modeling environments and model repositories. This emerging transition from the descriptive and informative, but "static", and for the most part, non-executable print format to interactive, transparent and functional delivery of "living" models is expected to have a transformative effect on modern experimental research in areas of scientific and regulatory use of (Q)SAR models.
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Affiliation(s)
- Igor V Tetko
- Institute of Structural Biology, Helmholtz Zentrum München -, German Research Center for Environmental Health (GmbH), Institute of Structural Biology, Ingolstädter Landstraße 1, D-, 85764, Neuherberg, Germany.,BigChem GmbH, Ingolstädter Landstraße 1, b. 60w, D-, 85764, Neuherberg, Germany
| | - Uko Maran
- Institute of Chemistry, University of Tartu, Ravila 14A, Tartu, 50411, Estonia
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.,Butlerov Institute of Chemistry, Kazan Federal University, Kremlyovskaya St. 18, 420008, Kazan, Russia
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25
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Aniceto N, Freitas AA, Bender A, Ghafourian T. Simultaneous Prediction of four ATP-binding Cassette Transporters’ Substrates Using Multi-label QSAR. Mol Inform 2016; 35:514-528. [DOI: 10.1002/minf.201600036] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Accepted: 07/11/2016] [Indexed: 12/21/2022]
Affiliation(s)
- Natália Aniceto
- Medway School of Pharmacy; Universities of Kent and Greenwich; Anson Building, Central Avenue, Chatham Maritime, Chatham Kent postCode/>ME4 4TB UK
| | - Alex A. Freitas
- School of Computing; University of Kent; Canterbury CT2 7NF UK
| | - Andreas Bender
- Centre for Molecular Science Informatics, Department of Chemistry; University of Cambridge; Lensfield Road Cambridge CB2 1EW UK
| | - Taravat Ghafourian
- School of Life Sciences, JMS Building; University of Sussex; Brighton BN1 9QG UK
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26
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Polishchuk P, Tinkov O, Khristova T, Ognichenko L, Kosinskaya A, Varnek A, Kuz’min V. Structural and Physico-Chemical Interpretation (SPCI) of QSAR Models and Its Comparison with Matched Molecular Pair Analysis. J Chem Inf Model 2016; 56:1455-69. [DOI: 10.1021/acs.jcim.6b00371] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Affiliation(s)
- Pavel Polishchuk
- Institute
of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacký University and University Hospital in Olomouc, Hněvotínská
1333/5, 779 00 Olomouc, Czech Republic
- A. V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine, Lustdorfskaya
doroga 86, 65080 Odessa, Ukraine
| | - Oleg Tinkov
- T. G. Shevchenko Transdniestria State University, ul. 25 Oktyabrya 107, 3300 Tiraspol, Transdniestria, Republic of Moldova
| | - Tatiana Khristova
- A. V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine, Lustdorfskaya
doroga 86, 65080 Odessa, Ukraine
- Laboratoire
de Chémoinformatique, UMR 7140 CNRS, Université de Strasbourg, 1 rue Blaise Pascal, 67000 Strasbourg, France
| | - Ludmila Ognichenko
- A. V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine, Lustdorfskaya
doroga 86, 65080 Odessa, Ukraine
| | - Anna Kosinskaya
- A. V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine, Lustdorfskaya
doroga 86, 65080 Odessa, Ukraine
| | - Alexandre Varnek
- Laboratoire
de Chémoinformatique, UMR 7140 CNRS, Université de Strasbourg, 1 rue Blaise Pascal, 67000 Strasbourg, France
- Laboratory
of Chemoinformatics and Molecular Modeling, Butlerov Institut of Chemistry, Kazan Federal University, Kremlevskaya 18, Kazan, Russia
| | - Victor Kuz’min
- A. V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine, Lustdorfskaya
doroga 86, 65080 Odessa, Ukraine
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27
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Abstract
How to design a ligand to bind multiple targets, rather than to a single target, is the focus of this review. Rational polypharmacology draws on knowledge that is both broad ranging and hierarchical. Computer-aided multitarget ligand design methods are described according to their nested knowledge level. Ligand-only and then receptor-ligand strategies are first described; followed by the metabolic network viewpoint. Subsequently strategies that view infectious diseases as multigenomic targets are discussed, and finally the disease level interpretation of medicinal therapy is considered. As yet there is no consensus on how best to proceed in designing a multitarget ligand. The current methodologies are bought together in an attempt to give a practical overview of how polypharmacology design might be best initiated.
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28
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Subhani S, Jamil K. Molecular docking of chemotherapeutic agents to CYP3A4 in non-small cell lung cancer. Biomed Pharmacother 2015. [DOI: 10.1016/j.biopha.2015.05.018] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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29
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Osolodkin DI, Radchenko EV, Orlov AA, Voronkov AE, Palyulin VA, Zefirov NS. Progress in visual representations of chemical space. Expert Opin Drug Discov 2015; 10:959-73. [DOI: 10.1517/17460441.2015.1060216] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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30
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Warr WA. Many InChIs and quite some feat. J Comput Aided Mol Des 2015; 29:681-94. [PMID: 26081259 DOI: 10.1007/s10822-015-9854-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Accepted: 06/10/2015] [Indexed: 12/14/2022]
Affiliation(s)
- Wendy A Warr
- Wendy Warr & Associates, Holmes Chapel, Crewe, Cheshire, CW4 7HZ, UK,
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31
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Reen FJ, Gutiérrez-Barranquero JA, Dobson ADW, Adams C, O’Gara F. Emerging concepts promising new horizons for marine biodiscovery and synthetic biology. Mar Drugs 2015; 13:2924-54. [PMID: 25984990 PMCID: PMC4446613 DOI: 10.3390/md13052924] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2015] [Revised: 04/22/2015] [Accepted: 04/28/2015] [Indexed: 12/23/2022] Open
Abstract
The vast oceans of the world, which comprise a huge variety of unique ecosystems, are emerging as a rich and relatively untapped source of novel bioactive compounds with invaluable biotechnological and pharmaceutical potential. Evidence accumulated over the last decade has revealed that the diversity of marine microorganisms is enormous with many thousands of bacterial species detected that were previously unknown. Associated with this diversity is the production of diverse repertoires of bioactive compounds ranging from peptides and enzymes to more complex secondary metabolites that have significant bioactivity and thus the potential to be exploited for innovative biotechnology. Here we review the discovery and functional potential of marine bioactive peptides such as lantibiotics, nanoantibiotics and peptidomimetics, which have received particular attention in recent years in light of their broad spectrum of bioactivity. The significance of marine peptides in cell-to-cell communication and how this may be exploited in the discovery of novel bioactivity is also explored. Finally, with the recent advances in bioinformatics and synthetic biology, it is becoming clear that the integration of these disciplines with genetic and biochemical characterization of the novel marine peptides, offers the most potential in the development of the next generation of societal solutions.
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Affiliation(s)
- F. Jerry Reen
- BIOMERIT Research Centre, School of Microbiology, University College Cork—National University of Ireland, Cork, Ireland; E-Mails: (F.J.R.); (J.A.G.-B.); (C.A.)
| | - José A. Gutiérrez-Barranquero
- BIOMERIT Research Centre, School of Microbiology, University College Cork—National University of Ireland, Cork, Ireland; E-Mails: (F.J.R.); (J.A.G.-B.); (C.A.)
| | - Alan D. W. Dobson
- School of Microbiology, University College Cork—National University of Ireland, Cork, Ireland; E-Mail:
| | - Claire Adams
- BIOMERIT Research Centre, School of Microbiology, University College Cork—National University of Ireland, Cork, Ireland; E-Mails: (F.J.R.); (J.A.G.-B.); (C.A.)
| | - Fergal O’Gara
- BIOMERIT Research Centre, School of Microbiology, University College Cork—National University of Ireland, Cork, Ireland; E-Mails: (F.J.R.); (J.A.G.-B.); (C.A.)
- School of Biomedical Sciences, Curtin University, Perth WA 6845, Australia
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