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Borrel A, Mansouri K, Nolte S, Saddler T, Conway M, Schmitt C, Kleinstreuer NC. InterPred: a webtool to predict chemical autofluorescence and luminescence interference. Nucleic Acids Res 2020; 48:W586-W590. [PMID: 32421835 DOI: 10.1093/nar/gkaa378] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 04/10/2020] [Accepted: 04/29/2020] [Indexed: 02/07/2023] Open
Abstract
High-throughput screening (HTS) research programs for drug development or chemical hazard assessment are designed to screen thousands of molecules across hundreds of biological targets or pathways. Most HTS platforms use fluorescence and luminescence technologies, representing more than 70% of the assays in the US Tox21 research consortium. These technologies are subject to interferent signals largely explained by chemicals interacting with light spectrum. This phenomenon results in up to 5-10% of false positive results, depending on the chemical library used. Here, we present the InterPred webserver (version 1.0), a platform to predict such interference chemicals based on the first large-scale chemical screening effort to directly characterize chemical-assay interference, using assays in the Tox21 portfolio specifically designed to measure autofluorescence and luciferase inhibition. InterPred combines 17 quantitative structure activity relationship (QSAR) models built using optimized machine learning techniques and allows users to predict the probability that a new chemical will interfere with different combinations of cellular and technology conditions. InterPred models have been applied to the entire Distributed Structure-Searchable Toxicity (DSSTox) Database (∼800,000 chemicals). The InterPred webserver is available at https://sandbox.ntp.niehs.nih.gov/interferences/.
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Yang ZY, Dong J, Yang ZJ, Yin M, Jiang HL, Lu AP, Chen X, Hou TJ, Cao DS. ChemFLuo: a web-server for structure analysis and identification of fluorescent compounds. Brief Bioinform 2020; 22:5985287. [PMID: 33201188 DOI: 10.1093/bib/bbaa282] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 09/12/2020] [Accepted: 09/25/2020] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Fluorescent detection methods are indispensable tools for chemical biology. However, the frequent appearance of potential fluorescent compound has greatly interfered with the recognition of compounds with genuine activity. Such fluorescence interference is especially difficult to identify as it is reproducible and possesses concentration-dependent characteristic. Therefore, the development of a credible screening tool to detect fluorescent compounds from chemical libraries is urgently needed in early stages of drug discovery. RESULTS In this study, we developed a webserver ChemFLuo for fluorescent compound detection, based on two large and high-quality training datasets containing 4906 blue and 8632 green fluorescent compounds. These molecules were used to construct a group of prediction models based on the combination of three machine learning algorithms and seven types of molecular representations. The best blue fluorescence prediction model achieved with balanced accuracy (BA) = 0.858 and area under the receiver operating characteristic curve (AUC) = 0.931 for the validation set, and BA = 0.823 and AUC = 0.903 for the test set. The best green fluorescence prediction model achieved the prediction accuracy with BA = 0.810 and AUC = 0.887 for the validation set, and BA = 0.771 and AUC = 0.852 for the test set. Besides prediction model, 22 blue and 16 green representative fluorescent substructures were summarized for the screening of potential fluorescent compounds. The comparison with other fluorescence detection tools and theapplication to external validation sets and large molecule libraries have demonstrated the reliability of prediction model for fluorescent compound detection. CONCLUSION ChemFLuo is a public webserver to filter out compounds with undesirable fluorescent properties, which will benefit the design of high-quality chemical libraries for drug discovery. It is freely available at http://admet.scbdd.com/chemfluo/index/.
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Affiliation(s)
- Zi-Yi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003, P. R. China
| | - Jie Dong
- Central South University of Forestry and Technology, Changsha, 410004, P.R. China
| | - Zhi-Jiang Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003, P. R. China
| | - Mingzhu Yin
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China
| | - Hong-Li Jiang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003, P. R. China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, P.R. China
| | - Xiang Chen
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China
| | - Ting-Jun Hou
- Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003, P. R. China.,Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, P.R. China
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Renn A, Su B, Liu H, Sun J, Tseng YJ. Advances in the prediction of mouse liver microsomal studies: From machine learning to deep learning. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1479] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Alex Renn
- Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei City Taiwan
- Department of Molecular and Cellular Biology University of California‐Berkeley Berkeley California USA
| | - Bo‐Han Su
- Department of Computer Science and Information Engineering National Taiwan University Taipei City Taiwan
| | - Hsin Liu
- Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei City Taiwan
| | - Joseph Sun
- Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei City Taiwan
| | - Yufeng J. Tseng
- Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei City Taiwan
- Department of Computer Science and Information Engineering National Taiwan University Taipei City Taiwan
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Yang ZY, He JH, Lu AP, Hou TJ, Cao DS. Frequent hitters: nuisance artifacts in high-throughput screening. Drug Discov Today 2020; 25:657-667. [DOI: 10.1016/j.drudis.2020.01.014] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 12/28/2019] [Accepted: 01/16/2020] [Indexed: 11/27/2022]
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High-Throughput Screening to Predict Chemical-Assay Interference. Sci Rep 2020; 10:3986. [PMID: 32132587 PMCID: PMC7055224 DOI: 10.1038/s41598-020-60747-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 01/31/2020] [Indexed: 12/31/2022] Open
Abstract
The U.S. federal consortium on toxicology in the 21st century (Tox21) produces quantitative, high-throughput screening (HTS) data on thousands of chemicals across a wide range of assays covering critical biological targets and cellular pathways. Many of these assays, and those used in other in vitro screening programs, rely on luciferase and fluorescence-based readouts that can be susceptible to signal interference by certain chemical structures resulting in false positive outcomes. Included in the Tox21 portfolio are assays specifically designed to measure interference in the form of luciferase inhibition and autofluorescence via multiple wavelengths (red, blue, and green) and under various conditions (cell-free and cell-based, two cell types). Out of 8,305 chemicals tested in the Tox21 interference assays, percent actives ranged from 0.5% (red autofluorescence) to 9.9% (luciferase inhibition). Self-organizing maps and hierarchical clustering were used to relate chemical structural clusters to interference activity profiles. Multiple machine learning algorithms were applied to predict assay interference based on molecular descriptors and chemical properties. The best performing predictive models (accuracies of ~80%) have been included in a web-based tool called InterPred that will allow users to predict the likelihood of assay interference for any new chemical structure and thus increase confidence in HTS data by decreasing false positive testing results.
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Jain S, Kotsampasakou E, Ecker GF. Comparing the performance of meta-classifiers-a case study on selected imbalanced data sets relevant for prediction of liver toxicity. J Comput Aided Mol Des 2018; 32:583-590. [PMID: 29626291 PMCID: PMC5919997 DOI: 10.1007/s10822-018-0116-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2017] [Accepted: 03/29/2018] [Indexed: 12/28/2022]
Abstract
Abstract Cheminformatics datasets used in classification problems, especially those related to biological or physicochemical properties, are often imbalanced. This presents a major challenge in development of in silico prediction models, as the traditional machine learning algorithms are known to work best on balanced datasets. The class imbalance introduces a bias in the performance of these algorithms due to their preference towards the majority class. Here, we present a comparison of the performance of seven different meta-classifiers for their ability to handle imbalanced datasets, whereby Random Forest is used as base-classifier. Four different datasets that are directly (cholestasis) or indirectly (via inhibition of organic anion transporting polypeptide 1B1 and 1B3) related to liver toxicity were chosen for this purpose. The imbalance ratio in these datasets ranges between 4:1 and 20:1 for negative and positive classes, respectively. Three different sets of molecular descriptors for model development were used, and their performance was assessed in 10-fold cross-validation and on an independent validation set. Stratified bagging, MetaCost and CostSensitiveClassifier were found to be the best performing among all the methods. While MetaCost and CostSensitiveClassifier provided better sensitivity values, Stratified Bagging resulted in high balanced accuracies. Graphical Abstract ![]()
Electronic supplementary material The online version of this article (10.1007/s10822-018-0116-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sankalp Jain
- Department of Pharmaceutical Chemistry, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
| | - Eleni Kotsampasakou
- Department of Pharmaceutical Chemistry, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria.,Computational Toxicology Group, CMS, R&D Platform Technology & Science, GSK, Park Road, Ware, Hertfordshire, SG12 0DP, UK
| | - Gerhard F Ecker
- Department of Pharmaceutical Chemistry, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria.
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Abstract
INTRODUCTION With the emergence of the 'big data' era, the biomedical research community has great interest in exploiting publicly available chemical information for drug discovery. PubChem is an example of public databases that provide a large amount of chemical information free of charge. AREAS COVERED This article provides an overview of how PubChem's data, tools, and services can be used for virtual screening and reviews recent publications that discuss important aspects of exploiting PubChem for drug discovery. EXPERT OPINION PubChem offers comprehensive chemical information useful for drug discovery. It also provides multiple programmatic access routes, which are essential to build automated virtual screening pipelines that exploit PubChem data. In addition, PubChemRDF allows users to download PubChem data and load them into a local computing facility, facilitating data integration between PubChem and other resources. PubChem resources have been used in many studies for developing bioactivity and toxicity prediction models, discovering polypharmacologic (multi-target) ligands, and identifying new macromolecule targets of compounds (for drug-repurposing or off-target side effect prediction). These studies demonstrate the usefulness of PubChem as a key resource for computer-aided drug discovery and related area.
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Affiliation(s)
- Sunghwan Kim
- a National Center for Biotechnology Information, National Library of Medicine , National Institutes of Health , Department of Health and Human Services, Bethesda , MD , USA
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Maslakov AS, Antal TK, Riznichenko GY, Rubin AB. Modeling of primary photosynthetic processes using the kinetic Monte Carlo method. Biophysics (Nagoya-shi) 2016. [DOI: 10.1134/s000635091603009x] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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