1
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Liu XQ, Yi YJ, Kong Y, Yu P, Zhao LG, Li DD. Consensus scoring model: A novel approach to the study of EGFR kinase inhibitors. Chem Phys Lett 2022. [DOI: 10.1016/j.cplett.2022.139650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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2
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Antifungal peptides produced by actinomycetes and their biological activities against plant diseases. J Antibiot (Tokyo) 2020; 73:265-282. [PMID: 32123311 DOI: 10.1038/s41429-020-0287-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 01/11/2020] [Accepted: 01/15/2020] [Indexed: 12/23/2022]
Abstract
Antibacterial peptides are a class of naturally occurring peptides produced by eukaryotes and prokaryotes. Some of them exhibit broad-spectrum antifungal activity. Antifungal peptides (AFPs) can be developed as antibiotic to control fungal infections in agriculture due to their different antifungal mechanisms. As actinomycetes are still one of the most important sources of novel antibiotics, in this review, the mechanisms of action of AFPs are explained. Characterization of several AFPs produced by actinomycetes and their biological activities against plant diseases are summarized. Furthermore, the pathway for total synthesis of naturally occurring cyclodepsipeptide, valinomycin, is proposed. Finally, the pathway for biosynthesis of kutzneride 2 is proposed and the structure-activity relationship of kutznerides is discussed.
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3
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An Application of Fit Quality to Screen MDM2/p53 Protein-Protein Interaction Inhibitors. Molecules 2018; 23:molecules23123174. [PMID: 30513790 PMCID: PMC6321222 DOI: 10.3390/molecules23123174] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Revised: 11/28/2018] [Accepted: 11/30/2018] [Indexed: 12/17/2022] Open
Abstract
The judicious application of ligand or binding efficiency (LE) metrics, which quantify the molecular properties required to obtain binding affinity for a drug target, is gaining traction in the selection and optimization of fragments, hits and leads. Here we report for the first time the use of LE based metric, fit quality (FQ), in virtual screening (VS) of MDM2/p53 protein-protein interaction inhibitors (PPIIs). Firstly, a Receptor-Ligand pharmacophore model was constructed on multiple MDM2/ligand complex structures to screen the library. The enrichment factor (EF) for screening was calculated based on a decoy set to define the screening threshold. Finally, 1% of the library, 335 compounds, were screened and re-filtered with the FQ metric. According to the statistical results of FQ vs. activity of 156 MDM2/p53 PPIIs extracted from literatures, the cut-off was defined as FQ = 0.8. After the second round of VS, six compounds with the FQ > 0.8 were picked out for assessing their antitumor activity. At the cellular level, the six hits exhibited a good selectivity (larger than 3) against HepG2 (wt-p53) vs. Hep3B (p53 null) cell lines. On the further study, the six hits exhibited an acceptable affinity (range of Ki from 102 to 103 nM) to MDM2 when comparing to Nutlin-3a. Based on our work, FQ based VS strategy could be applied to discover other PPIIs.
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4
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Irwin JJ, Gaskins G, Sterling T, Mysinger MM, Keiser MJ. Predicted Biological Activity of Purchasable Chemical Space. J Chem Inf Model 2017; 58:148-164. [PMID: 29193970 PMCID: PMC5780839 DOI: 10.1021/acs.jcim.7b00316] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
![]()
Whereas
400 million distinct compounds are now purchasable within
the span of a few weeks, the biological activities of most are unknown.
To facilitate access to new chemistry for biology, we have combined
the Similarity Ensemble Approach (SEA) with the maximum Tanimoto similarity
to the nearest bioactive to predict activity for every commercially
available molecule in ZINC. This method, which we label SEA+TC, outperforms
both SEA and a naïve-Bayesian classifier via predictive performance
on a 5-fold cross-validation of ChEMBL’s bioactivity data set
(version 21). Using this method, predictions for over 40% of compounds
(>160 million) have either high significance (pSEA ≥ 40),
high
similarity (ECFP4MaxTc ≥ 0.4), or both, for one or more of
1382 targets well described by ligands in the literature. Using a
further 1347 less-well-described targets, we predict activities for
an additional 11 million compounds. To gauge whether these predictions
are sensible, we investigate 75 predictions for 50 drugs lacking a
binding affinity annotation in ChEMBL. The 535 million predictions
for over 171 million compounds at 2629 targets are linked to purchasing
information and evidence to support each prediction and are freely
available via https://zinc15.docking.org and https://files.docking.org.
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Affiliation(s)
- John J Irwin
- Department of Pharmaceutical Chemistry, University of California, San Francisco , Byers Hall, 1700 4th Street, San Francisco, California 94158-2330, United States
| | - Garrett Gaskins
- Department of Pharmaceutical Chemistry, University of California, San Francisco , Byers Hall, 1700 4th Street, San Francisco, California 94158-2330, United States.,Institute for Neurodegenerative Diseases, University of California, San Francisco , 675 Nelson Rising Lane, San Francisco, California 94158, United States.,Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco , Byers Hall, 1700 4th Street, San Francisco, California 94158, United States.,Institute for Computational Health Sciences, University of California, San Francisco , 550 16th Street, San Francisco, California 94158, United States
| | - Teague Sterling
- Department of Pharmaceutical Chemistry, University of California, San Francisco , Byers Hall, 1700 4th Street, San Francisco, California 94158-2330, United States
| | - Michael M Mysinger
- Department of Pharmaceutical Chemistry, University of California, San Francisco , Byers Hall, 1700 4th Street, San Francisco, California 94158-2330, United States
| | - Michael J Keiser
- Department of Pharmaceutical Chemistry, University of California, San Francisco , Byers Hall, 1700 4th Street, San Francisco, California 94158-2330, United States.,Institute for Neurodegenerative Diseases, University of California, San Francisco , 675 Nelson Rising Lane, San Francisco, California 94158, United States.,Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco , Byers Hall, 1700 4th Street, San Francisco, California 94158, United States.,Institute for Computational Health Sciences, University of California, San Francisco , 550 16th Street, San Francisco, California 94158, United States
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5
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Li DD, Meng XF, Wang Q, Yu P, Zhao LG, Zhang ZP, Wang ZZ, Xiao W. Consensus scoring model for the molecular docking study of mTOR kinase inhibitor. J Mol Graph Model 2017; 79:81-87. [PMID: 29154212 DOI: 10.1016/j.jmgm.2017.11.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Revised: 10/27/2017] [Accepted: 11/03/2017] [Indexed: 12/22/2022]
Abstract
The discovery of mammalian target of rapamycin (mTOR) kinase inhibitors has always been a research hotspot of antitumor drugs. Consensus scoring used in the docking study of mTOR kinase inhibitors usually improves hit rate of virtual screening. Herein, we attempt to build a series of consensus scoring models based on a set of the common scoring functions. In this paper, twenty-five kinds of mTOR inhibitors (16 clinical candidate compounds and 9 promising preclinical compounds) are carefully collected, and selected for the molecular docking study used by the Glide docking programs within the standard precise (SP) mode. The predicted poses of these ligands are saved, and revaluated by twenty-six available scoring functions, respectively. Subsequently, consensus scoring models are trained based on the obtained rescoring results by the partial least squares (PLS) method, and validated by Leave-one-out (LOO) method. In addition, three kinds of ligand efficiency indices (BEI, SEI, and LLE) instead of pIC50 as the activity could greatly improve the statistical quality of build models. Two best calculated models 10 and 22 using the same BEI indice have following statistical parameters, respectively: for model 10, training set R2=0.767, Q2=0.647, RMSE=0.024, and for test set R2=0.932, RMSE=0.026; for model 22, raining set R2=0.790, Q2=0.627, RMSE=0.023, and for test set R2=0.955, RMSE=0.020. These two consensus scoring model would be used for the docking virtual screening of novel mTOR inhibitors.
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Affiliation(s)
- Dong-Dong Li
- College of Chemical Engineering, Nanjing Forestry University, 159 Long Pan Road, Nanjing 210037, China.
| | - Xiang-Feng Meng
- College of Chemical Engineering, Nanjing Forestry University, 159 Long Pan Road, Nanjing 210037, China
| | - Qiang Wang
- College of Chemical Engineering, Nanjing Forestry University, 159 Long Pan Road, Nanjing 210037, China
| | - Pan Yu
- College of Chemical Engineering, Nanjing Forestry University, 159 Long Pan Road, Nanjing 210037, China
| | - Lin-Guo Zhao
- College of Chemical Engineering, Nanjing Forestry University, 159 Long Pan Road, Nanjing 210037, China
| | - Zheng-Ping Zhang
- Chia Tai Tianqing Pharmaceutical Group Co., Ltd., 369 South Yuzhou Road, Haizhou District, Lianyungang 222062, Jiangsu Province, China.
| | - Zhen-Zhong Wang
- Jiangsu Kanion Pharmaceutical Co., Ltd., 58 Haichang South Road, Lianyungang 222001, Jiangsu Province, China
| | - Wei Xiao
- Jiangsu Kanion Pharmaceutical Co., Ltd., 58 Haichang South Road, Lianyungang 222001, Jiangsu Province, China.
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6
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Cheng T, Hao M, Takeda T, Bryant SH, Wang Y. Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review. AAPS J 2017; 19:1264-1275. [PMID: 28577120 PMCID: PMC11097213 DOI: 10.1208/s12248-017-0092-6] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Accepted: 04/25/2017] [Indexed: 11/30/2022] Open
Abstract
The prediction of drug-target interactions (DTIs) is of extraordinary significance to modern drug discovery in terms of suggesting new drug candidates and repositioning old drugs. Despite technological advances, large-scale experimental determination of DTIs is still expensive and laborious. Effective and low-cost computational alternatives remain in strong need. Meanwhile, open-access resources have been rapidly growing with massive amount of bioactivity data becoming available, creating unprecedented opportunities for the development of novel in silico models for large-scale DTI prediction. In this work, we review the state-of-the-art computational approaches for identifying DTIs from a data-centric perspective: what the underlying data are and how they are utilized in each study. We also summarize popular public data resources and online tools for DTI prediction. It is found that various types of data were employed including properties of chemical structures, drug therapeutic effects and side effects, drug-target binding, drug-drug interactions, bioactivity data of drug molecules across multiple biological targets, and drug-induced gene expressions. More often, the heterogeneous data were integrated to offer better performance. However, challenges remain such as handling data imbalance, incorporating negative samples and quantitative bioactivity data, as well as maintaining cross-links among different data sources, which are essential for large-scale and automated information integration.
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Affiliation(s)
- Tiejun Cheng
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Ming Hao
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Takako Takeda
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Stephen H Bryant
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Yanli Wang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA.
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7
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Cavalluzzi MM, Mangiatordi GF, Nicolotti O, Lentini G. Ligand efficiency metrics in drug discovery: the pros and cons from a practical perspective. Expert Opin Drug Discov 2017; 12:1087-1104. [PMID: 28814111 DOI: 10.1080/17460441.2017.1365056] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Ligand efficiency metrics are almost universally accepted as a valuable indicator of compound quality and an aid to reduce attrition. Areas covered: In this review, the authors describe ligand efficiency metrics giving a balanced overview on their merits and points of weakness in order to enable the readers to gain an informed opinion. Relevant theoretical breakthroughs and drug-like properties are also illustrated. Several recent exemplary case studies are discussed in order to illustrate the main fields of application of ligand efficiency metrics. Expert opinion: As a medicinal chemist guide, ligand efficiency metrics perform in a context- and chemotype-dependent manner; thus, they should not be used as a magic box. Since the 'big bang' of efficiency metrics occurred more or less ten years ago and the average time to develop a new drug is over the same period, the next few years will give a clearer outlook on the increased rate of success, if any, gained by means of these new intriguing tools.
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Affiliation(s)
| | | | - Orazio Nicolotti
- a Department of Pharmacy - Drug Sciences , University of Bari Aldo Moro , Bari , Italy
| | - Giovanni Lentini
- a Department of Pharmacy - Drug Sciences , University of Bari Aldo Moro , Bari , Italy
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8
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Polanski J, Tkocz A. Between Descriptors and Properties: Understanding the Ligand Efficiency Trends for G Protein-Coupled Receptor and Kinase Structure-Activity Data Sets. J Chem Inf Model 2017; 57:1321-1329. [PMID: 28489365 DOI: 10.1021/acs.jcim.7b00116] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
The chemical meaning of the ligand efficiency (LE) metrics is explained in this paper using a large G protein-coupled receptor (GPCR) and kinase structure-activity (IC50, Ki) data set. Although there is a controversy in the literature regarding both the mathematical validity and the performance of LE, it is in common use as an early estimator for drug optimization. Apparently, the numerous con arguments are not convincing enough. We show here for the first time that the main misunderstanding of the chemical meaning of LE is its interpretation as a molecular descriptor connected with a single molecule. Instead, LE should be interpreted as a statistical property. We show that the LE, which is designed as a regression of a binding property on the heavy atom count (HAC), is correlated to the reciprocal of the molecular weight because of Avogadro statistics. This indicates that the hyperbolic model of LE is basically a consequence of a nonbinding effect, an increase in the number of ligands that are available to a receptor for smaller molecules, and not a real increase in the binding potency for a single HAC as interpreted in the literature. Accordingly, we need to revisit and carefully reevaluate LE-based molecular comparisons.
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Affiliation(s)
- Jaroslaw Polanski
- Institute of Chemistry, University of Silesia , 9 Szkolna Street, 40-006 Katowice, Poland
| | - Aleksandra Tkocz
- Institute of Chemistry, University of Silesia , 9 Szkolna Street, 40-006 Katowice, Poland
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9
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Sheridan RP. Debunking the Idea that Ligand Efficiency Indices Are Superior to pIC50 as QSAR Activities. J Chem Inf Model 2016; 56:2253-2262. [DOI: 10.1021/acs.jcim.6b00431] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Robert P. Sheridan
- Modeling and Informatics Department, Merck & Co. Inc., Rahway, New Jersey 07065, United States
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10
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He B, Lu C, Zheng G, He X, Wang M, Chen G, Zhang G, Lu A. Combination therapeutics in complex diseases. J Cell Mol Med 2016; 20:2231-2240. [PMID: 27605177 PMCID: PMC5134672 DOI: 10.1111/jcmm.12930] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2016] [Accepted: 06/16/2016] [Indexed: 12/22/2022] Open
Abstract
The biological redundancies in molecular networks of complex diseases limit the efficacy of many single drug therapies. Combination therapeutics, as a common therapeutic method, involve pharmacological intervention using several drugs that interact with multiple targets in the molecular networks of diseases and may achieve better efficacy and/or less toxicity than monotherapy in practice. The development of combination therapeutics is complicated by several critical issues, including identifying multiple targets, targeting strategies and the drug combination. This review summarizes the current achievements in combination therapeutics, with a particular emphasis on the efforts to develop combination therapeutics for complex diseases.
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Affiliation(s)
- Bing He
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.,Institute of Integrated Bioinformedicine & Translational Science, HKBU Shenzhen Research Institute and Continuing Education, Shenzhen, China
| | - Cheng Lu
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.,Institute of Integrated Bioinformedicine & Translational Science, HKBU Shenzhen Research Institute and Continuing Education, Shenzhen, China.,Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Guang Zheng
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.,Institute of Integrated Bioinformedicine & Translational Science, HKBU Shenzhen Research Institute and Continuing Education, Shenzhen, China
| | - Xiaojuan He
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.,Institute of Integrated Bioinformedicine & Translational Science, HKBU Shenzhen Research Institute and Continuing Education, Shenzhen, China.,Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Maolin Wang
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.,Institute of Integrated Bioinformedicine & Translational Science, HKBU Shenzhen Research Institute and Continuing Education, Shenzhen, China
| | - Gao Chen
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.,Institute of Integrated Bioinformedicine & Translational Science, HKBU Shenzhen Research Institute and Continuing Education, Shenzhen, China
| | - Ge Zhang
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.,Institute of Integrated Bioinformedicine & Translational Science, HKBU Shenzhen Research Institute and Continuing Education, Shenzhen, China
| | - Aiping Lu
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.,Institute of Integrated Bioinformedicine & Translational Science, HKBU Shenzhen Research Institute and Continuing Education, Shenzhen, China.,Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
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11
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Cortes-Ciriano I. Benchmarking the Predictive Power of Ligand Efficiency Indices in QSAR. J Chem Inf Model 2016; 56:1576-87. [DOI: 10.1021/acs.jcim.6b00136] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Affiliation(s)
- Isidro Cortes-Ciriano
- Département de Biologie
Structurale et Chimie, Institut Pasteur, Unité de Bioinformatique Structurale, CNRS UMR 3825, 25, rue du Dr Roux, 75015 Paris, France
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12
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Accurate and efficient target prediction using a potency-sensitive influence-relevance voter. J Cheminform 2015; 7:63. [PMID: 26719774 PMCID: PMC4696267 DOI: 10.1186/s13321-015-0110-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Accepted: 12/02/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A number of algorithms have been proposed to predict the biological targets of diverse molecules. Some are structure-based, but the most common are ligand-based and use chemical fingerprints and the notion of chemical similarity. These methods tend to be computationally faster than others, making them particularly attractive tools as the amount of available data grows. RESULTS Using a ChEMBL-derived database covering 490,760 molecule-protein interactions and 3236 protein targets, we conduct a large-scale assessment of the performance of several target-prediction algorithms at predicting drug-target activity. We assess algorithm performance using three validation procedures: standard tenfold cross-validation, tenfold cross-validation in a simulated screen that includes random inactive molecules, and validation on an external test set composed of molecules not present in our database. CONCLUSIONS We present two improvements over current practice. First, using a modified version of the influence-relevance voter (IRV), we show that using molecule potency data can improve target prediction. Second, we demonstrate that random inactive molecules added during training can boost the accuracy of several algorithms in realistic target-prediction experiments. Our potency-sensitive version of the IRV (PS-IRV) obtains the best results on large test sets in most of the experiments. Models and software are publicly accessible through the chemoinformatics portal at http://chemdb.ics.uci.edu/.
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13
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Chiba S, Ikeda K, Ishida T, Gromiha MM, Taguchi YH, Iwadate M, Umeyama H, Hsin KY, Kitano H, Yamamoto K, Sugaya N, Kato K, Okuno T, Chikenji G, Mochizuki M, Yasuo N, Yoshino R, Yanagisawa K, Ban T, Teramoto R, Ramakrishnan C, Thangakani AM, Velmurugan D, Prathipati P, Ito J, Tsuchiya Y, Mizuguchi K, Honma T, Hirokawa T, Akiyama Y, Sekijima M. Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target. Sci Rep 2015; 5:17209. [PMID: 26607293 PMCID: PMC4660442 DOI: 10.1038/srep17209] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2015] [Accepted: 10/27/2015] [Indexed: 12/14/2022] Open
Abstract
A search of broader range of chemical space is important for drug discovery. Different methods of computer-aided drug discovery (CADD) are known to propose compounds in different chemical spaces as hit molecules for the same target protein. This study aimed at using multiple CADD methods through open innovation to achieve a level of hit molecule diversity that is not achievable with any particular single method. We held a compound proposal contest, in which multiple research groups participated and predicted inhibitors of tyrosine-protein kinase Yes. This showed whether collective knowledge based on individual approaches helped to obtain hit compounds from a broad range of chemical space and whether the contest-based approach was effective.
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Affiliation(s)
- Shuntaro Chiba
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama 226-8501 Japan
| | - Kazuyoshi Ikeda
- Level Five Co. Ltd., Shiodome Shibarikyu Bldg., 1-2-3 Kaigan, Minato-ku, Tokyo 105-0022, Japan
| | - Takashi Ishida
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama 226-8501 Japan.,Department of Computer Science, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550 Japan
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India
| | - Y-H Taguchi
- Department of Physics, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan
| | - Mitsuo Iwadate
- Department of Biological Sciences, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan
| | - Hideaki Umeyama
- Department of Biological Sciences, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan
| | - Kun-Yi Hsin
- Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Onna-son, Kunigami, Okinawa 904-0495 Japan
| | - Hiroaki Kitano
- Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Onna-son, Kunigami, Okinawa 904-0495 Japan.,The Systems Biology Research Institute, Falcon Building 5F, 5-6-9 Shirokanedai, Minato-ku, Tokyo 108-0071 Japan.,Center for Integrative Medical Sciences, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan
| | - Kazuki Yamamoto
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904 Japan
| | - Nobuyoshi Sugaya
- PharmaDesign Inc., 2-19-8, Hatchobori, Chuo-ku, Tokyo 104-0032 Japan
| | - Koya Kato
- Department of Computational Science and Engineering, Nagoya University, Furocho, Chikusa, Nagoya 464-8603, Japan
| | - Tatsuya Okuno
- Division of Neurogenetics, Nagoya University Graduate School of Medicine, 65 Tsurumai, Showa-ku, Nagoya 466-8550, Japan
| | - George Chikenji
- Department of Computational Science and Engineering, Nagoya University, Furocho, Chikusa, Nagoya 464-8603, Japan
| | - Masahiro Mochizuki
- Information and Mathematical Science and Bioinformatics Co., Ltd., Level 6 OWL TOWER, 4-21-1 Higashi-Ikebukuro, Toshima-ku, Tokyo 170-0013 Japan
| | - Nobuaki Yasuo
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama 226-8501 Japan.,Department of Computer Science, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550 Japan
| | - Ryunosuke Yoshino
- Global Scientific Information and Computing Center, Tokyo Institute of Technology 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550 Japan.,Department of Biotechnology, The University of Tokyo, 1-1-1 Yayoi, Nunkyo-ku, Tokyo, 113-8657
| | - Keisuke Yanagisawa
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama 226-8501 Japan.,Department of Computer Science, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550 Japan
| | - Tomohiro Ban
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama 226-8501 Japan.,Department of Computer Science, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550 Japan
| | - Reiji Teramoto
- Forerunner Pharma Research, Co., Ltd., Yokohama Bio Industry Center, 1-6 Shuehiro-cho, Tsurumi-ku, Yokohama 230-0045 Japan
| | - Chandrasekaran Ramakrishnan
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India
| | - A Mary Thangakani
- Centre of Advanced Study in Crystallography and Biophysics and Bioinformatics Infrastructure Facility (DBT Funded), University of Madras, Chennai 600025, Tamilnadu, India
| | - D Velmurugan
- Centre of Advanced Study in Crystallography and Biophysics and Bioinformatics Infrastructure Facility (DBT Funded), University of Madras, Chennai 600025, Tamilnadu, India
| | - Philip Prathipati
- National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki, Osaka 567-0085 Japan
| | - Junichi Ito
- National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki, Osaka 567-0085 Japan
| | - Yuko Tsuchiya
- National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki, Osaka 567-0085 Japan
| | - Kenji Mizuguchi
- National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki, Osaka 567-0085 Japan
| | - Teruki Honma
- Center for Life Science Technologies, RIKEN, 6-7-3 Minatojima-minamimachi, Chuo-ku, Kobe-shi, Hyogo 650-0047 Japan
| | - Takatsugu Hirokawa
- Molecular Profiling Research Center for Drug Discovery, National Institute of Advanced Industrial Science and Technology, 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan.,Initiative for Parallel Bioinformatics, Level 14 Hibiya Central Building, 1-2-9 Nishi-Shimbashi Minato-Ku, Tokyo 105-0003 Japan
| | - Yutaka Akiyama
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama 226-8501 Japan.,Department of Computer Science, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550 Japan.,Molecular Profiling Research Center for Drug Discovery, National Institute of Advanced Industrial Science and Technology, 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan.,Initiative for Parallel Bioinformatics, Level 14 Hibiya Central Building, 1-2-9 Nishi-Shimbashi Minato-Ku, Tokyo 105-0003 Japan
| | - Masakazu Sekijima
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama 226-8501 Japan.,Department of Computer Science, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550 Japan.,Global Scientific Information and Computing Center, Tokyo Institute of Technology 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550 Japan.,Initiative for Parallel Bioinformatics, Level 14 Hibiya Central Building, 1-2-9 Nishi-Shimbashi Minato-Ku, Tokyo 105-0003 Japan
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14
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Li J, Bai F, Liu H, Gramatica P. Ligand Efficiency Outperforms pIC50on Both 2D MLR and 3D CoMFA Models: A Case Study on AR Antagonists. Chem Biol Drug Des 2015. [PMID: 26198098 DOI: 10.1111/cbdd.12619] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Jiazhong Li
- School of Pharmacy; Lanzhou University; 199 West Donggang Road 730000 Lanzhou China
- The Separating Scientific Institute of Lanzhou; 3 Weiyi Road 730000 Lanzhou China
| | - Fang Bai
- School of Pharmacy; Lanzhou University; 199 West Donggang Road 730000 Lanzhou China
| | - Huanxiang Liu
- School of Pharmacy; Lanzhou University; 199 West Donggang Road 730000 Lanzhou China
| | - Paola Gramatica
- Department of Theoretical and Applied Sciences; University of Insubria; via Dunant 3 21100 Varese Italy
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15
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Sugaya N. Ligand efficiency-based support vector regression models for predicting bioactivities of ligands to drug target proteins. J Chem Inf Model 2014; 54:2751-63. [PMID: 25220713 DOI: 10.1021/ci5003262] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
The concept of ligand efficiency (LE) indices is widely accepted throughout the drug design community and is frequently used in a retrospective manner in the process of drug development. For example, LE indices are used to investigate LE optimization processes of already-approved drugs and to re-evaluate hit compounds obtained from structure-based virtual screening methods and/or high-throughput experimental assays. However, LE indices could also be applied in a prospective manner to explore drug candidates. Here, we describe the construction of machine learning-based regression models in which LE indices are adopted as an end point and show that LE-based regression models can outperform regression models based on pIC50 values. In addition to pIC50 values traditionally used in machine learning studies based on chemogenomics data, three representative LE indices (ligand lipophilicity efficiency (LLE), binding efficiency index (BEI), and surface efficiency index (SEI)) were adopted, then used to create four types of training data. We constructed regression models by applying a support vector regression (SVR) method to the training data. In cross-validation tests of the SVR models, the LE-based SVR models showed higher correlations between the observed and predicted values than the pIC50-based models. Application tests to new data displayed that, generally, the predictive performance of SVR models follows the order SEI > BEI > LLE > pIC50. Close examination of the distributions of the activity values (pIC50, LLE, BEI, and SEI) in the training and validation data implied that the performance order of the SVR models may be ascribed to the much higher diversity of the LE-based training and validation data. In the application tests, the LE-based SVR models can offer better predictive performance of compound-protein pairs with a wider range of ligand potencies than the pIC50-based models. This finding strongly suggests that LE-based SVR models are better than pIC50-based models at predicting bioactivities of compounds that could exhibit a much higher (or lower) potency.
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Affiliation(s)
- Nobuyoshi Sugaya
- Drug Discovery Department, Research & Development Division, PharmaDesign, Inc. , Hatchobori 2-19-8, Chuo-ku, Tokyo 104-0032, Japan
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