1
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Xiao N, Wang B, Huang JJ, Huang Z, Shi L. Aeration strategy based on numerical modelling and the response mechanism of microbial communities under various operating conditions. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 310:114752. [PMID: 35231691 DOI: 10.1016/j.jenvman.2022.114752] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 02/10/2022] [Accepted: 02/15/2022] [Indexed: 06/14/2023]
Abstract
Aeration system is the main energy consumer in a wastewater treatment process. In this paper, the Naive Bayes classification (NBC) algorithm and response surface method (RSM) were firstly used to establish a methodology to improve the aeration efficiency and estimate effluent quality. Lab-scale experiments were conducted to verify the model. The errors between experimental values and predicted values were 3.36, -0.67 and -3.78% at operating temperatures of 20, 30 and 35 °C, indicating the applicability. To further elucidate the biological mechanisms of the experimental results, the microbial community composition was investigated under various operating conditions, the results shows that aerobic heterotrophic bacteria (HET) activity and COD removal efficiency were promoted at 30 °C. AOB and NOB activity and NH4+-N removal efficiency were promoted at 30-35 °C. These findings together suggest that operating temperature is crucial for activated sludge treatment, which should be considered when regulating DO content or aeration rate in practical application.
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Affiliation(s)
- Nan Xiao
- College of Environmental Science and Engineering/Sino-Canada Joint R&D Centre on Water and Environmental Safety, Nankai University, Tianjin, 300071, PR China
| | - Bing Wang
- College of Environmental Science and Engineering/Sino-Canada Joint R&D Centre on Water and Environmental Safety, Nankai University, Tianjin, 300071, PR China
| | - Jeanne Jinhui Huang
- College of Environmental Science and Engineering/Sino-Canada Joint R&D Centre on Water and Environmental Safety, Nankai University, Tianjin, 300071, PR China.
| | - Zhiyong Huang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, PR China
| | - Liuyang Shi
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, PR China
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2
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Tinivella A, Pinzi L, Rastelli G. Prediction of activity and selectivity profiles of human Carbonic Anhydrase inhibitors using machine learning classification models. J Cheminform 2021; 13:18. [PMID: 33676550 PMCID: PMC7937250 DOI: 10.1186/s13321-021-00499-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 02/22/2021] [Indexed: 11/23/2022] Open
Abstract
The development of selective inhibitors of the clinically relevant human Carbonic Anhydrase (hCA) isoforms IX and XII has become a major topic in drug research, due to their deregulation in several types of cancer. Indeed, the selective inhibition of these two isoforms, especially with respect to the homeostatic isoform II, holds great promise to develop anticancer drugs with limited side effects. Therefore, the development of in silico models able to predict the activity and selectivity against the desired isoform(s) is of central interest. In this work, we have developed a series of machine learning classification models, trained on high confidence data extracted from ChEMBL, able to predict the activity and selectivity profiles of ligands for human Carbonic Anhydrase isoforms II, IX and XII. The training datasets were built with a procedure that made use of flexible bioactivity thresholds to obtain well-balanced active and inactive classes. We used multiple algorithms and sampling sizes to finally select activity models able to classify active or inactive molecules with excellent performances. Remarkably, the results herein reported turned out to be better than those obtained by models built with the classic approach of selecting an a priori activity threshold. The sequential application of such validated models enables virtual screening to be performed in a fast and more reliable way to predict the activity and selectivity profiles against the investigated isoforms.
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Affiliation(s)
- Annachiara Tinivella
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125, Modena, Italy.,Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Italy
| | - Luca Pinzi
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125, Modena, Italy
| | - Giulio Rastelli
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125, Modena, Italy.
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3
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Pacureanu L, Avram S, Crisan L. Comprehensive investigation of selectivity landscape of glycogen synthase kinase-3 inhibitors. J Biomol Struct Dyn 2020; 39:2318-2337. [PMID: 32216607 DOI: 10.1080/07391102.2020.1747544] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Interaction signatures of drug candidates are characteristic to off-target (neutral) and antitarget (negative) effects, inferring reduced efficiency, side-effects and high attrition rate. Today's retroactive scaled-down virtual screening (VS) experiments relying on benchmarking datasets are extensively involved to assess ligand enrichment in the real-world problem. In recent years, unbiased benchmarking sets turned into a tremendous need to assist virtual screening methodologies for emerging drug targets. To date, the benchmarking datasets are quite limited, whereas glycogen synthase kinase-3 (GSK-3) is not included into directories of benchmarking datasets such as DUD-e, MUV, etc. Herein we introduced our in-house algorithm to build an unbiased benchmarking dataset, including highly selective, moderately selective and nonselective inhibitors for a significant therapeutic target - GSK-3, suitable for both ligand-based and structure-based VS approaches. These datasets are unbiased in terms of physico-chemical properties and topological descriptors, as resulted from mean(ROC-AUC) leave-one-out cross-validation (LOO CV). and additional 2 D similarity search. Moreover, we investigated the gradual selectivity dataset by application of multiple 2 D similarity coefficients and distances, 3 D similarity and docking. Besides the resulted links between the enrichment of selective GSK-3 inhibitors and their chemical structures, a database of compounds and their 3 D similarity signatures including cut-off thresholds for enhanced selectivity was generated. 2 D similarity space analysis revealed that selectivity problem cannot be evaluated appropriately with 2 D similarity searching alone. The current analysis provided useful, comprehensive insights, which may facilitate the knowledge-based identification of novel selective GSK-3 inhibitors.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Liliana Pacureanu
- "Coriolan Dragulescu" Institute of Chemistry, Romanian Academy, Timisoara, Romania
| | - Sorin Avram
- "Coriolan Dragulescu" Institute of Chemistry, Romanian Academy, Timisoara, Romania
| | - Luminita Crisan
- "Coriolan Dragulescu" Institute of Chemistry, Romanian Academy, Timisoara, Romania
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4
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Liu J, Ning X. Differential Compound Prioritization via Bidirectional Selectivity Push with Power. J Chem Inf Model 2017; 57:2958-2975. [PMID: 29178784 DOI: 10.1021/acs.jcim.7b00552] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Junfeng Liu
- Indiana University - Purdue University Indianapolis, 723 West Michigan Street, SL 280, Indianapolis, Indiana 46202, United States
| | - Xia Ning
- Indiana University - Purdue University Indianapolis, 723 West Michigan Street, SL 280, Indianapolis, Indiana 46202, United States
- Center
for Computational Biology and Bioinformatics, Indiana University School of Medicine, 410 West 10th Street, HITS 5000, Indianapolis, Indiana 46202, United States
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5
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Ihmaid SK, Ahmed HEA, Zayed MF, Abadleh MM. Self Organizing Map-Based Classification of Cathepsin k and S Inhibitors with Different Selectivity Profiles Using Different Structural Molecular Fingerprints: Design and Application for Discovery of Novel Hits. Molecules 2016; 21:175. [PMID: 26840291 PMCID: PMC6272978 DOI: 10.3390/molecules21020175] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Revised: 01/20/2016] [Accepted: 01/27/2016] [Indexed: 11/25/2022] Open
Abstract
The main step in a successful drug discovery pipeline is the identification of small potent compounds that selectively bind to the target of interest with high affinity. However, there is still a shortage of efficient and accurate computational methods with powerful capability to study and hence predict compound selectivity properties. In this work, we propose an affordable machine learning method to perform compound selectivity classification and prediction. For this purpose, we have collected compounds with reported activity and built a selectivity database formed of 153 cathepsin K and S inhibitors that are considered of medicinal interest. This database has three compound sets, two K/S and S/K selective ones and one non-selective KS one. We have subjected this database to the selectivity classification tool ‘Emergent Self-Organizing Maps’ for exploring its capability to differentiate selective cathepsin inhibitors for one target over the other. The method exhibited good clustering performance for selective ligands with high accuracy (up to 100 %). Among the possibilites, BAPs and MACCS molecular structural fingerprints were used for such a classification. The results exhibited the ability of the method for structure-selectivity relationship interpretation and selectivity markers were identified for the design of further novel inhibitors with high activity and target selectivity.
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Affiliation(s)
- Saleh K Ihmaid
- Pharmacognosy and Pharmaceutical Chemistry Department, College of Pharmacy, Taibah University, P. O. Box 30039, Al-Madinah Al-Munawarah 41477, Saudi Arabia.
- School of Pharmacy and Applied Science, La Trobe University, P. O. Box 199, Bendigo 3552, Australia.
| | - Hany E A Ahmed
- Pharmacognosy and Pharmaceutical Chemistry Department, College of Pharmacy, Taibah University, P. O. Box 30039, Al-Madinah Al-Munawarah 41477, Saudi Arabia.
- Pharmaceutical Organic Chemistry Department, Faculty of Pharmacy, Al-Azhar University, P. O. Box 11651, Cairo 11884, Egypt.
| | - Mohamed F Zayed
- Pharmacognosy and Pharmaceutical Chemistry Department, College of Pharmacy, Taibah University, P. O. Box 30039, Al-Madinah Al-Munawarah 41477, Saudi Arabia.
- Pharmaceutical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, P. O. Box 11651, Cairo 11884, Egypt.
| | - Mohammed M Abadleh
- Pharmacognosy and Pharmaceutical Chemistry Department, College of Pharmacy, Taibah University, P. O. Box 30039, Al-Madinah Al-Munawarah 41477, Saudi Arabia.
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6
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Li Y, Wang L, Liu Z, Li C, Xu J, Gu Q, Xu J. Predicting selective liver X receptor β agonists using multiple machine learning methods. MOLECULAR BIOSYSTEMS 2015; 11:1241-50. [DOI: 10.1039/c4mb00718b] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The classification models for predicting selective LXRβ agonists were firstly established using multiple machine learning methods. The top models can predict selective LXRβ agonists with chemical structure diversity.
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Affiliation(s)
- Yali Li
- Research Center for Drug Discovery & Institute of Human Virology
- School of Pharmaceutical Sciences
- Sun Yat-Sen University
- Guangzhou 510006
- China
| | - Ling Wang
- School of Bioscience and Bioengineering
- South China University of Technology
- Guangzhou 510006
- China
| | - Zhihong Liu
- Research Center for Drug Discovery & Institute of Human Virology
- School of Pharmaceutical Sciences
- Sun Yat-Sen University
- Guangzhou 510006
- China
| | - Chanjuan Li
- Research Center for Drug Discovery & Institute of Human Virology
- School of Pharmaceutical Sciences
- Sun Yat-Sen University
- Guangzhou 510006
- China
| | - Jiake Xu
- Centre for Orthopaedic Research
- School of Surgery
- The University of Western Australia
- Perth
- Australia
| | - Qiong Gu
- Research Center for Drug Discovery & Institute of Human Virology
- School of Pharmaceutical Sciences
- Sun Yat-Sen University
- Guangzhou 510006
- China
| | - Jun Xu
- Research Center for Drug Discovery & Institute of Human Virology
- School of Pharmaceutical Sciences
- Sun Yat-Sen University
- Guangzhou 510006
- China
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7
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Hu Y, Bajorath J. Follow up: Compound data sets and software tools for chemoinformatics and medicinal chemistry applications: update and data transfer. F1000Res 2014; 3:69. [PMID: 25520777 PMCID: PMC4264635 DOI: 10.12688/f1000research.3713.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/07/2014] [Indexed: 12/12/2022] Open
Abstract
In 2012, we reported 30 compound data sets and/or programs developed in our laboratory in a data article and made them freely available to the scientific community to support chemoinformatics and computational medicinal chemistry applications. These data sets and computational tools were provided for download from our website. Since publication of this data article, we have generated 13 new data sets with which we further extend our collection of publicly available data and tools. Due to changes in web servers and website architectures, data accessibility has recently been limited at times. Therefore, we have also transferred our data sets and tools to a public repository to ensure full and stable accessibility. To aid in data selection, we have classified the data sets according to scientific subject areas. Herein, we describe new data sets, introduce the data organization scheme, summarize the database content and provide detailed access information in ZENODO (doi: 10.5281/zenodo.8451 and doi:10.5281/zenodo.8455).
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Affiliation(s)
- Ye Hu
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms University, Bonn, D-53113, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms University, Bonn, D-53113, Germany
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8
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Yang Y, Zhang W, Cheng J, Tang Y, Peng Y, Li Z. Pharmacophore, 3D-QSAR, and Bayesian Model Analysis for Ligands Binding at the Benzodiazepine Site of GABAAReceptors: the Key Roles of Amino Group and Hydrophobic Sites. Chem Biol Drug Des 2013; 81:583-90. [DOI: 10.1111/cbdd.12100] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2012] [Revised: 12/07/2012] [Accepted: 12/12/2012] [Indexed: 01/19/2023]
Affiliation(s)
- Ying Yang
- Shanghai Key Laboratory of Chemical Biology; School of Pharmacy; East China University of Science and Technology; Shanghai; China
| | - Wei Zhang
- Shanghai Key Laboratory of Chemical Biology; School of Pharmacy; East China University of Science and Technology; Shanghai; China
| | | | - Yun Tang
- Shanghai Key Laboratory of New Drug Design; School of Pharmacy; East China University of Science and Technology; PO Box 544, 130 Meilong Road; Shanghai; 200237; China
| | - Yanqing Peng
- Shanghai Key Laboratory of Chemical Biology; School of Pharmacy; East China University of Science and Technology; Shanghai; China
| | - Zhong Li
- Shanghai Key Laboratory of Chemical Biology; School of Pharmacy; East China University of Science and Technology; Shanghai; China
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9
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Hu Y, Bajorath J. Freely available compound data sets and software tools for chemoinformatics and computational medicinal chemistry applications. F1000Res 2012; 1:11. [PMID: 24358818 PMCID: PMC3782340 DOI: 10.12688/f1000research.1-11.v1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/07/2012] [Indexed: 01/22/2023] Open
Abstract
We have generated a number of compound data sets and programs for different types of applications in pharmaceutical research. These data sets and programs were originally designed for our research projects and are made publicly available. Without consulting original literature sources, it is difficult to understand specific features of data sets and software tools, basic ideas underlying their design, and applicability domains. Currently, 30 different entries are available for download from our website. In this data article, we provide an overview of the data and tools we make available and designate the areas of research for which they should be useful. For selected data sets and methods/programs, detailed descriptions are given. This article should help interested readers to select data and tools for specific computational investigations.
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Affiliation(s)
- Ye Hu
- Department of Life Science Informatics, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr, Bonn, D-53113, Germany
| | - Jurgen Bajorath
- Department of Life Science Informatics, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr, Bonn, D-53113, Germany
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10
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Ning X, Walters M, Karypisxy G. Improved Machine Learning Models for Predicting Selective Compounds. J Chem Inf Model 2011; 52:38-50. [DOI: 10.1021/ci200346b] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Xia Ning
- Department of Computer Science & Engineering and ‡College of Pharmacy, University of Minnesota, Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Michael Walters
- Department of Computer Science & Engineering and ‡College of Pharmacy, University of Minnesota, Twin Cities, Minneapolis, Minnesota 55455, United States
| | - George Karypisxy
- Department of Computer Science & Engineering and ‡College of Pharmacy, University of Minnesota, Twin Cities, Minneapolis, Minnesota 55455, United States
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11
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Stumpfe D, Bajorath J. Applied Virtual Screening: Strategies, Recommendations, and Caveats. METHODS AND PRINCIPLES IN MEDICINAL CHEMISTRY 2011. [DOI: 10.1002/9783527633326.ch11] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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12
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Ma C, Wang L, Xie XQ. Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps (LiCABEDS) and its application on modeling ligand functionality for 5HT-subtype GPCR families. J Chem Inf Model 2011; 51:521-31. [PMID: 21381738 PMCID: PMC3065508 DOI: 10.1021/ci100399j] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Advanced high-throughput screening (HTS) technologies generate great amounts of bioactivity data, and this data needs to be analyzed and interpreted with attention to understand how these small molecules affect biological systems. As such, there is an increasing demand to develop and adapt cheminformatics algorithms and tools in order to predict molecular and pharmacological properties on the basis of these large data sets. In this manuscript, we report a novel machine-learning-based ligand classification algorithm, named Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps (LiCABEDS), for data-mining and modeling of large chemical data sets to predict pharmacological properties in an efficient and accurate manner. The performance of LiCABEDS was evaluated through predicting GPCR ligand functionality (agonist or antagonist) using four different molecular fingerprints, including Maccs, FP2, Unity, and Molprint 2D fingerprints. Our studies showed that LiCABEDS outperformed two other popular techniques, classification tree and Naive Bayes classifier, on all four types of molecular fingerprints. Parameters in LiCABEDS, including the number of boosting iterations, initialization condition, and a "reject option" boundary, were thoroughly explored and discussed to demonstrate the capability of handling imbalanced data sets, as well as its robustness and flexibility. In addition, the detailed mathematical concepts and theory are also given to address the principle behind statistical prediction models. The LiCABEDS algorithm has been implemented into a user-friendly software package that is accessible online at http://www.cbligand.org/LiCABEDS/ .
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Affiliation(s)
- Chao Ma
- Department of Computational Biology, Joint Pitt/CMU Computational Biology Program, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Pittsburgh Center for Chemical Methodologies & Library Development (PCMLD) and Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Lirong Wang
- Department of Computational Biology, Joint Pitt/CMU Computational Biology Program, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Pittsburgh Center for Chemical Methodologies & Library Development (PCMLD) and Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Xiang-Qun Xie
- Department of Computational Biology, Joint Pitt/CMU Computational Biology Program, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Pittsburgh Center for Chemical Methodologies & Library Development (PCMLD) and Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15260, USA
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13
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Abstract
The exploration of structure-activity relationships (SARs) is a major challenge in medicinal chemistry and usually focuses on compound potency for individual targets. However, selectivity of small molecules that are active against related targets is another critical parameter in chemical lead optimization. Here, an integrative approach for the systematic analysis of SARs and structure-selectivity relationships (SSRs) of small molecules is presented. The computational methodology is described and a cathepsin inhibitor set is used to discuss key aspects of the analysis. Combining a numerical scoring scheme and graphical visualization of molecular networks, the approach enables the identification of different local SAR and SSR environments. Comparative analysis of these environments reveals variable relationships between molecular structure, potency, and selectivity. Furthermore, key compounds are identified that are involved in the formation of activity and/or selectivity cliffs and often display structural features that determine compound selectivity.
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Affiliation(s)
- Lisa Peltason
- Department of Life Science Informatics, B-IT, Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany
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14
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Wassermann AM, Geppert H, Bajorath J. Application of support vector machine-based ranking strategies to search for target-selective compounds. Methods Mol Biol 2011; 672:517-530. [PMID: 20838983 DOI: 10.1007/978-1-60761-839-3_21] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Support vector machine (SVM)-based selectivity searching has recently been introduced to identify compounds in virtual screening libraries that are not only active for a target protein, but also selective for this target over a closely related member of the same protein family. In simulated virtual screening calculations, SVM-based strategies termed preference ranking and one-versus-all ranking were successfully applied to rank a database and enrich high-ranking positions with selective compounds while removing nonselective molecules from high ranks. In contrast to the original SVM approach developed for binary classification, these strategies enable learning from more than two classes, considering that distinguishing between selective, promiscuously active, and inactive compounds gives rise to a three-class prediction problem. In this chapter, we describe the extension of the one-versus-all strategy to four training classes. Furthermore, we present an adaptation of the preference ranking strategy that leads to higher recall of selective compounds than previously investigated approaches and is applicable in situations where the removal of nonselective compounds from high-ranking positions is not required.
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Affiliation(s)
- Anne Mai Wassermann
- Department of Life Science Informatics, B-IT, Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany
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15
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Stumpfe D, Lounkine E, Bajorath J. Molecular test systems for computational selectivity studies and systematic analysis of compound selectivity profiles. Methods Mol Biol 2011; 672:503-15. [PMID: 20838982 DOI: 10.1007/978-1-60761-839-3_20] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
For chemical genetics and chemical biology, an important task is the identification of small molecules that are selective against individual targets and can be used as molecular probes for specific biological functions. To aid in the development of computational methods for selectivity analysis, molecular benchmark systems have been developed that capture compound selectivity data for pairs of targets. These molecular test systems are utilized for "selectivity searching" and the analysis of structure-selectivity relationships. Going beyond binary selectivity sets focusing on target pairs, a methodological framework, Molecular Formal Concept Analysis (MolFCA), is described for the definition and systematic mining of compound selectivity profiles.
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Affiliation(s)
- Dagmar Stumpfe
- Department of Life Science Informatics, B-IT, Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany
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16
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Geppert H, Vogt M, Bajorath J. Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation. J Chem Inf Model 2010; 50:205-16. [PMID: 20088575 DOI: 10.1021/ci900419k] [Citation(s) in RCA: 231] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Hanna Geppert
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universitat, Dahlmannstrasse 2, D-53113 Bonn, Germany
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17
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Peltason L, Hu Y, Bajorath J. From structure-activity to structure-selectivity relationships: quantitative assessment, selectivity cliffs, and key compounds. ChemMedChem 2010; 4:1864-73. [PMID: 19750525 DOI: 10.1002/cmdc.200900300] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The exploration of structure-activity relationships (SARs) in chemical lead optimization is mostly focused on activity against single targets. Because many active compounds have the potential to act against multiple targets, achieving a sufficient degree of target selectivity often becomes a major issue during optimization. Herein we report a data analysis approach to explore compound selectivity in a systematic and quantitative manner. Sets of compounds that are active against multiple targets provide a basis for exploring structure-selectivity relationships (SSRs). Compound similarity and selectivity data are analyzed with the aid of network-like similarity graphs (NSGs), which organize molecular networks on the basis of similarity relationships and SAR index (SARI) values. For this purpose, the SARI framework has been adapted to quantify SSRs. Using sets of compounds with differential activity against four cathepsin thiol proteases, we show that SSRs can be quantitatively described and categorized. Furthermore, local SSR environments are identified, the analysis of which provides insight into compound selectivity determinants at the molecular level. These environments often contain "selectivity cliffs" formed by pairs or groups of similar compounds with significantly different selectivity. Moreover, key compounds are identified that determine characteristic features of single-target SARs and dual-target SSRs. The comparison of compounds involved in the formation of selectivity cliffs often reveals chemical modifications that render compounds target selective.
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Affiliation(s)
- Lisa Peltason
- Department of Life Science Informatics, B-IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Germany
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18
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van Hoorn WP, Bell AS. Searching Chemical Space with the Bayesian Idea Generator. J Chem Inf Model 2009; 49:2211-20. [DOI: 10.1021/ci900072g] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Willem P. van Hoorn
- Department of Chemistry, Pfizer Global Research and Development, Sandwich Laboratories, Sandwich, Kent CT13 9NJ, United Kingdom
| | - Andrew S. Bell
- Department of Chemistry, Pfizer Global Research and Development, Sandwich Laboratories, Sandwich, Kent CT13 9NJ, United Kingdom
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19
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Ahmed HEA, Bajorath J. Methods for Computer-Aided Chemical Biology. Part 5: Rationalizing the Selectivity of Cathepsin Inhibitors on the Basis of Molecular Fragments and Topological Feature Distributions. Chem Biol Drug Des 2009; 74:129-41. [DOI: 10.1111/j.1747-0285.2009.00848.x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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20
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Vogt M, Nisius B, Bajorath J. Predicting the similarity search performance of fingerprints and their combination with molecular property descriptors using probabilistic and information theoretic modeling. Stat Anal Data Min 2009. [DOI: 10.1002/sam.10035] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Geppert H, Humrich J, Stumpfe D, Gärtner T, Bajorath J. Ligand prediction from protein sequence and small molecule information using support vector machines and fingerprint descriptors. J Chem Inf Model 2009; 49:767-79. [PMID: 19309114 DOI: 10.1021/ci900004a] [Citation(s) in RCA: 44] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Support vector machine (SVM) database search strategies are presented that aim at the identification of small molecule ligands for targets for which no ligand information is currently available. In pharmaceutical research and chemical biology, this situation is faced, for example, when studying orphan targets or newly identified members of protein families. To investigate methods for de novo ligand identification in the absence of known three-dimensional target structures or active molecules, we have focused on combining sequence and ligand information for closely and distantly related proteins. To provide a basis for these investigations, a set of 11 protease targets from different families was assembled together with more than 2000 inhibitors directed against individual proteases. We have compared SVM approaches that combine protein sequence and ligand information in different ways and utilize 2D fingerprints as ligand descriptors. These methodologies were applied to search for inhibitors of individual proteases not taken into account during learning. A target sequence-ligand kernel and, in particular, a linear combination of multiple target-directed SVMs consistently identified inhibitors with high accuracy including test cases where homology-based similarity searching using data fusion and conventional SVM ranking nearly or completely failed. The SVM linear combination and target-ligand kernel methods described herein are intuitive and straightforward to adopt for ligand prediction against other targets.
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Affiliation(s)
- Hanna Geppert
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universitat Bonn, Dahlmannstr. 2, D-53113 Bonn, Germany
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Stumpfe D, Geppert H, Bajorath J. Analysis of structure-selectivity relationships through single- or dual step selectivity searching using 2D molecular fingerprints. Chem Cent J 2009. [DOI: 10.1186/1752-153x-3-s1-p4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Wassermann AM, Geppert H, Bajorath J. Searching for target-selective compounds using different combinations of multiclass support vector machine ranking methods, kernel functions, and fingerprint descriptors. J Chem Inf Model 2009; 49:582-92. [PMID: 19249858 DOI: 10.1021/ci800441c] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The identification of small chemical compounds that are selective for a target protein over one or more closely related members of the same family is of high relevance for applications in chemical biology. Conventional 2D similarity searching using known selective molecules as templates has recently been found to preferentially detect selective over non-selective and inactive database compounds. To improve the initially observed search performance, we have attempted to use 2D fingerprints as descriptors for support vector machine (SVM)-based selectivity searching. Different from typically applied binary SVM compound classification, SVM analysis has been adapted here for multiclass predictions and compound ranking to distinguish between selective, active but non-selective, and inactive compounds. In systematic database search calculations, we tested combinations of four alternative SVM ranking schemes, four different kernel functions, and four fingerprints and were able to further improve selectivity search performance by effectively removing non-selective molecules from high ranking positions while retaining high recall of selective compounds.
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Affiliation(s)
- Anne Mai Wassermann
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn, Germany
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Lounkine E, Stumpfe D, Bajorath J. Molecular Formal Concept Analysis for Compound Selectivity Profiling in Biologically Annotated Databases. J Chem Inf Model 2009; 49:1359-68. [DOI: 10.1021/ci900095v] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Eugen Lounkine
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität Bonn, Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Dagmar Stumpfe
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität Bonn, Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität Bonn, Dahlmannstrasse 2, D-53113 Bonn, Germany
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Ahmed HEA, Geppert H, Stumpfe D, Lounkine E, Bajorath J. Methods for Computer-Aided Chemical Biology. Part 4: Selectivity Searching for Ion Channel Ligands and Mapping of Molecular Fragments as Selectivity Markers. Chem Biol Drug Des 2009; 73:273-82. [DOI: 10.1111/j.1747-0285.2009.00784.x] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Stumpfe D, Frizler M, Sisay M, Batista J, Vogt I, Gütschow M, Bajorath J. Hit Expansion through Computational Selectivity Searching. ChemMedChem 2009; 4:52-4. [DOI: 10.1002/cmdc.200800304] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Dagmar Stumpfe
- Department of Life Science Informatics, Bonn–Aachen International Center for Information Technology, Rheinische Friedrich‐Wilhelms‐Universität Bonn, Dahlmannstr. 2, 53113 Bonn (Germany), Fax: (+49) 228‐2699‐341
| | - Maxim Frizler
- Pharmazeutisches Institut, Pharmazeutische Chemie I, Rheinische Friedrich‐Wilhelms‐Universität Bonn, An der Immenburg 4, 53121 Bonn (Germany)
| | - Mihiret T. Sisay
- Pharmazeutisches Institut, Pharmazeutische Chemie I, Rheinische Friedrich‐Wilhelms‐Universität Bonn, An der Immenburg 4, 53121 Bonn (Germany)
| | - José Batista
- Department of Life Science Informatics, Bonn–Aachen International Center for Information Technology, Rheinische Friedrich‐Wilhelms‐Universität Bonn, Dahlmannstr. 2, 53113 Bonn (Germany), Fax: (+49) 228‐2699‐341
| | - Ingo Vogt
- Department of Life Science Informatics, Bonn–Aachen International Center for Information Technology, Rheinische Friedrich‐Wilhelms‐Universität Bonn, Dahlmannstr. 2, 53113 Bonn (Germany), Fax: (+49) 228‐2699‐341
| | - Michael Gütschow
- Pharmazeutisches Institut, Pharmazeutische Chemie I, Rheinische Friedrich‐Wilhelms‐Universität Bonn, An der Immenburg 4, 53121 Bonn (Germany)
| | - Jürgen Bajorath
- Department of Life Science Informatics, Bonn–Aachen International Center for Information Technology, Rheinische Friedrich‐Wilhelms‐Universität Bonn, Dahlmannstr. 2, 53113 Bonn (Germany), Fax: (+49) 228‐2699‐341
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Bajorath J. Computational approaches in chemogenomics and chemical biology: current and future impact on drug discovery. Expert Opin Drug Discov 2008; 3:1371-6. [DOI: 10.1517/17460440802536496] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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