1
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Gorostiola González M, van den Broek RL, Braun TGM, Chatzopoulou M, Jespers W, IJzerman AP, Heitman LH, van Westen GJP. 3DDPDs: describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors. J Cheminform 2023; 15:74. [PMID: 37641107 PMCID: PMC10463931 DOI: 10.1186/s13321-023-00745-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 08/10/2023] [Indexed: 08/31/2023] Open
Abstract
Proteochemometric (PCM) modelling is a powerful computational drug discovery tool used in bioactivity prediction of potential drug candidates relying on both chemical and protein information. In PCM features are computed to describe small molecules and proteins, which directly impact the quality of the predictive models. State-of-the-art protein descriptors, however, are calculated from the protein sequence and neglect the dynamic nature of proteins. This dynamic nature can be computationally simulated with molecular dynamics (MD). Here, novel 3D dynamic protein descriptors (3DDPDs) were designed to be applied in bioactivity prediction tasks with PCM models. As a test case, publicly available G protein-coupled receptor (GPCR) MD data from GPCRmd was used. GPCRs are membrane-bound proteins, which are activated by hormones and neurotransmitters, and constitute an important target family for drug discovery. GPCRs exist in different conformational states that allow the transmission of diverse signals and that can be modified by ligand interactions, among other factors. To translate the MD-encoded protein dynamics two types of 3DDPDs were considered: one-hot encoded residue-specific (rs) and embedding-like protein-specific (ps) 3DDPDs. The descriptors were developed by calculating distributions of trajectory coordinates and partial charges, applying dimensionality reduction, and subsequently condensing them into vectors per residue or protein, respectively. 3DDPDs were benchmarked on several PCM tasks against state-of-the-art non-dynamic protein descriptors. Our rs- and ps3DDPDs outperformed non-dynamic descriptors in regression tasks using a temporal split and showed comparable performance with a random split and in all classification tasks. Combinations of non-dynamic descriptors with 3DDPDs did not result in increased performance. Finally, the power of 3DDPDs to capture dynamic fluctuations in mutant GPCRs was explored. The results presented here show the potential of including protein dynamic information on machine learning tasks, specifically bioactivity prediction, and open opportunities for applications in drug discovery, including oncology.
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Affiliation(s)
- Marina Gorostiola González
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
- ONCODE Institute, Leiden, The Netherlands
| | - Remco L van den Broek
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Thomas G M Braun
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Magdalini Chatzopoulou
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Willem Jespers
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Adriaan P IJzerman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Laura H Heitman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
- ONCODE Institute, Leiden, The Netherlands
| | - Gerard J P van Westen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.
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Parks C, Gaieb Z, Amaro RE. An Analysis of Proteochemometric and Conformal Prediction Machine Learning Protein-Ligand Binding Affinity Models. Front Mol Biosci 2020; 7:93. [PMID: 32671093 PMCID: PMC7328444 DOI: 10.3389/fmolb.2020.00093] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 04/22/2020] [Indexed: 11/13/2022] Open
Abstract
Protein-ligand binding affinity is a key pharmacodynamic endpoint in drug discovery. Sole reliance on experimental design, make, and test cycles is costly and time consuming, providing an opportunity for computational methods to assist. Herein, we present results comparing random forest and feed-forward neural network proteochemometric models for their ability to predict pIC50 measurements for held out generic Bemis-Murcko scaffolds. In addition, we assess the ability of conformal prediction to provide calibrated prediction intervals in both a retrospective and semi-prospective test using the recently released Grand Challenge 4 data set as an external test set. In total, random forest and deep neural network proteochemometric models show quality retrospective performance but suffer in the semi-prospective setting. However, the conformal predictor prediction intervals prove to be well-calibrated both retrospectively and semi-prospectively showing that they can be used to guide hit discovery and lead optimization campaigns.
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Affiliation(s)
| | | | - Rommie E. Amaro
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, United States
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3
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Bongers BJ, IJzerman AP, Van Westen GJP. Proteochemometrics - recent developments in bioactivity and selectivity modeling. DRUG DISCOVERY TODAY. TECHNOLOGIES 2019; 32-33:89-98. [PMID: 33386099 DOI: 10.1016/j.ddtec.2020.08.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 08/18/2020] [Accepted: 08/28/2020] [Indexed: 06/12/2023]
Abstract
Proteochemometrics is a machine learning based modeling approach relying on a combination of ligand and protein descriptors. With ongoing developments in machine learning and increases in public data the technique is more frequently applied in early drug discovery, typically in ligand-target binding prediction. Common applications include improvements to single target quantitative structure-activity relationship models, protein selectivity and promiscuity modeling, and large-scale deep learning approaches. The increase in predictive power using proteochemometrics is observed in multi-target bioactivity modeling, opening the door to more extensive studies covering whole protein families. On top of that, with deep learning fueling more complex and larger scale models, proteochemometrics allows faster and higher quality computational models supporting the design, make, test cycle.
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Affiliation(s)
- Brandon J Bongers
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Adriaan P IJzerman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Gerard J P Van Westen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands.
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4
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Giblin KA, Hughes SJ, Boyd H, Hansson P, Bender A. Prospectively Validated Proteochemometric Models for the Prediction of Small-Molecule Binding to Bromodomain Proteins. J Chem Inf Model 2018; 58:1870-1888. [DOI: 10.1021/acs.jcim.8b00400] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Kathryn A. Giblin
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Samantha J. Hughes
- Computational Chemistry, Oncology, IMED Biotech Unit, AstraZeneca, Cambridge CB10 1XL, U.K
| | - Helen Boyd
- Discovery Biology, Discovery Sciences, IMED Biotech Unit, AstraZeneca, Gothenburg 431 50 SE, Sweden
| | - Pia Hansson
- Discovery Biology, Discovery Sciences, IMED Biotech Unit, AstraZeneca, Gothenburg 431 50 SE, Sweden
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
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5
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Qiu T, Wu D, Qiu J, Cao Z. Finding the molecular scaffold of nuclear receptor inhibitors through high-throughput screening based on proteochemometric modelling. J Cheminform 2018; 10:21. [PMID: 29651663 PMCID: PMC5897275 DOI: 10.1186/s13321-018-0275-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Accepted: 04/02/2018] [Indexed: 02/10/2023] Open
Abstract
Nuclear receptors (NR) are a class of proteins that are responsible for sensing steroid and thyroid hormones and certain other molecules. In that case, NR have the ability to regulate the expression of specific genes and associated with various diseases, which make it essential drug targets. Approaches which can predict the inhibition ability of compounds for different NR target should be particularly helpful for drug development. In this study, proteochemometric modelling was introduced to analysis the bioactivity between chemical compounds and NR targets. Results illustrated the ability of our PCM model for high-throughput NR-inhibitor screening after evaluated on both internal (AUC > 0.870) and external (AUC > 0.746) validation set. Moreover, in-silico predicted bioactive compounds were clustered according to structure similarity and a series of representative molecular scaffolds can be derived for five major NR targets. Through scaffolds analysis, those essential bioactive scaffolds of different NR target can be detected and compared. Generally, the methods and molecular scaffolds proposed in this article can not only help the screening of potential therapeutic NR-inhibitors but also able to guide the future NR-related drug discovery.
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Affiliation(s)
- Tianyi Qiu
- School of Life Sciences and Technology, Shanghai 10th People's Hospital, Tongji University, No. 1239 SiPing Road, Shanghai, China.,The Institute of Biomedical Sciences, Fudan University, No. 138 Medical College Road, Shanghai, China
| | - Dingfeng Wu
- School of Life Sciences and Technology, Shanghai 10th People's Hospital, Tongji University, No. 1239 SiPing Road, Shanghai, China
| | - Jingxuan Qiu
- School of Life Sciences and Technology, Shanghai 10th People's Hospital, Tongji University, No. 1239 SiPing Road, Shanghai, China.,School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, No. 516 JunGong Road, Shanghai, China
| | - Zhiwei Cao
- School of Life Sciences and Technology, Shanghai 10th People's Hospital, Tongji University, No. 1239 SiPing Road, Shanghai, China.
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Basith S, Cui M, Macalino SJY, Park J, Clavio NAB, Kang S, Choi S. Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design. Front Pharmacol 2018; 9:128. [PMID: 29593527 PMCID: PMC5854945 DOI: 10.3389/fphar.2018.00128] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Accepted: 02/06/2018] [Indexed: 01/14/2023] Open
Abstract
The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the "golden age for GPCR structural biology." Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand- and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.
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Affiliation(s)
| | | | | | | | | | - Soosung Kang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
| | - Sun Choi
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
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7
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Sorgenfrei FA, Fulle S, Merget B. Kinome-Wide Profiling Prediction of Small Molecules. ChemMedChem 2017; 13:495-499. [PMID: 28544552 DOI: 10.1002/cmdc.201700180] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Revised: 05/20/2017] [Indexed: 12/21/2022]
Abstract
Extensive kinase profiling data, covering more than half of the human kinome, are available nowadays and allow the construction of activity prediction models of high practical utility. Proteochemometric (PCM) approaches use compound and protein descriptors, which enables the extrapolation of bioactivity values to thus far unexplored kinases. In this study, the potential of PCM to make large-scale predictions on the entire kinome is explored, considering the applicability on novel compounds and kinases, including clinically relevant mutants. A rigorous validation indicates high predictive power on left-out kinases and superiority over individual kinase QSAR models for new compounds. Furthermore, external validation on clinically relevant mutant kinases reveals an excellent predictive power for mutations spread across the ATP binding site.
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Affiliation(s)
- Frieda A Sorgenfrei
- BioMed X Innovation Center, Im Neuenheimer Feld 515, 69120, Heidelberg, Germany
| | - Simone Fulle
- BioMed X Innovation Center, Im Neuenheimer Feld 515, 69120, Heidelberg, Germany
| | - Benjamin Merget
- BioMed X Innovation Center, Im Neuenheimer Feld 515, 69120, Heidelberg, Germany
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8
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Qiu T, Qiu J, Feng J, Wu D, Yang Y, Tang K, Cao Z, Zhu R. The recent progress in proteochemometric modelling: focusing on target descriptors, cross-term descriptors and application scope. Brief Bioinform 2016; 18:125-136. [PMID: 26873661 DOI: 10.1093/bib/bbw004] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2015] [Revised: 12/09/2015] [Indexed: 12/17/2022] Open
Abstract
As an extension of the conventional quantitative structure activity relationship models, proteochemometric (PCM) modelling is a computational method that can predict the bioactivity relations between multiple ligands and multiple targets. Traditional PCM modelling includes three essential elements: descriptors (including target descriptors, ligand descriptors and cross-term descriptors), bioactivity data and appropriate learning functions that link the descriptors to the bioactivity data. Since its appearance, PCM modelling has developed rapidly over the past decade by taking advantage of the progress of different descriptors and machine learning techniques, along with the increasing amounts of available bioactivity data. Specifically, the new emerging target descriptors and cross-term descriptors not only significantly increased the performance of PCM modelling but also expanded its application scope from traditional protein-ligand interaction to more abundant interactions, including protein-peptide, protein-DNA and even protein-protein interactions. In this review, target descriptors and cross-term descriptors, as well as the corresponding application scope, are intensively summarized. Additionally, we look forward to seeing PCM modelling extend into new application scopes, such as Target-Catalyst-Ligand systems, with the further development of descriptors, machine learning techniques and increasing amounts of available bioactivity data.
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9
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Dong J, Cao DS, Miao HY, Liu S, Deng BC, Yun YH, Wang NN, Lu AP, Zeng WB, Chen AF. ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation. J Cheminform 2015; 7:60. [PMID: 26664458 PMCID: PMC4674923 DOI: 10.1186/s13321-015-0109-z] [Citation(s) in RCA: 169] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2015] [Accepted: 11/26/2015] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Molecular descriptors and fingerprints have been routinely used in QSAR/SAR analysis, virtual drug screening, compound search/ranking, drug ADME/T prediction and other drug discovery processes. Since the calculation of such quantitative representations of molecules may require substantial computational skills and efforts, several tools have been previously developed to make an attempt to ease the process. However, there are still several hurdles for users to overcome to fully harness the power of these tools. First, most of the tools are distributed as standalone software or packages that require necessary configuration or programming efforts of users. Second, many of the tools can only calculate a subset of molecular descriptors, and the results from multiple tools need to be manually merged to generate a comprehensive set of descriptors. Third, some packages only provide application programming interfaces and are implemented in different computer languages, which pose additional challenges to the integration of these tools. RESULTS A freely available web-based platform, named ChemDes, is developed in this study. It integrates multiple state-of-the-art packages (i.e., Pybel, CDK, RDKit, BlueDesc, Chemopy, PaDEL and jCompoundMapper) for computing molecular descriptors and fingerprints. ChemDes not only provides friendly web interfaces to relieve users from burdensome programming work, but also offers three useful and convenient auxiliary tools for format converting, MOPAC optimization and fingerprint similarity calculation. Currently, ChemDes has the capability of computing 3679 molecular descriptors and 59 types of molecular fingerprints. CONCLUSION ChemDes provides users an integrated and friendly tool to calculate various molecular descriptors and fingerprints. It is freely available at http://www.scbdd.com/chemdes. The source code of the project is also available as a supplementary file. Graphical abstract:An overview of ChemDes. A platform for computing various molecular descriptors and fingerprints.
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Affiliation(s)
- Jie Dong
- School of Pharmaceutical Sciences, Central South University, Changsha, 410013 Hunan People's Republic of China
| | - Dong-Sheng Cao
- School of Pharmaceutical Sciences, Central South University, Changsha, 410013 Hunan People's Republic of China
| | - Hong-Yu Miao
- Department of Biostatistics, School of Public Health, University of Texas Health Science Center at Houston, Houston, USA
| | - Shao Liu
- Xiangya Hospital, Central South University, Changsha, 410008 Hunan People's Republic of China
| | - Bai-Chuan Deng
- College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083 People's Republic of China
| | - Yong-Huan Yun
- College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083 People's Republic of China
| | - Ning-Ning Wang
- School of Pharmaceutical Sciences, Central South University, Changsha, 410013 Hunan People's Republic of China
| | - Ai-Ping Lu
- Institute of Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR People's Republic of China
| | - Wen-Bin Zeng
- School of Pharmaceutical Sciences, Central South University, Changsha, 410013 Hunan People's Republic of China
| | - Alex F Chen
- School of Pharmaceutical Sciences, Central South University, Changsha, 410013 Hunan People's Republic of China
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Ain QU, Méndez-Lucio O, Ciriano IC, Malliavin T, van Westen GJP, Bender A. Modelling ligand selectivity of serine proteases using integrative proteochemometric approaches improves model performance and allows the multi-target dependent interpretation of features. Integr Biol (Camb) 2015; 6:1023-33. [PMID: 25255469 DOI: 10.1039/c4ib00175c] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Serine proteases, implicated in important physiological functions, have a high intra-family similarity, which leads to unwanted off-target effects of inhibitors with insufficient selectivity. However, the availability of sequence and structure data has now made it possible to develop approaches to design pharmacological agents that can discriminate successfully between their related binding sites. In this study, we have quantified the relationship between 12,625 distinct protease inhibitors and their bioactivity against 67 targets of the serine protease family (20,213 data points) in an integrative manner, using proteochemometric modelling (PCM). The benchmarking of 21 different target descriptors motivated the usage of specific binding pocket amino acid descriptors, which helped in the identification of active site residues and selective compound chemotypes affecting compound affinity and selectivity. PCM models performed better than alternative approaches (models trained using exclusively compound descriptors on all available data, QSAR) employed for comparison with R(2)/RMSE values of 0.64 ± 0.23/0.66 ± 0.20 vs. 0.35 ± 0.27/1.05 ± 0.27 log units, respectively. Moreover, the interpretation of the PCM model singled out various chemical substructures responsible for bioactivity and selectivity towards particular proteases (thrombin, trypsin and coagulation factor 10) in agreement with the literature. For instance, absence of a tertiary sulphonamide was identified to be responsible for decreased selective activity (by on average 0.27 ± 0.65 pChEMBL units) on FA10. Among the binding pocket residues, the amino acids (arginine, leucine and tyrosine) at positions 35, 39, 60, 93, 140 and 207 were observed as key contributing residues for selective affinity on these three targets.
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Affiliation(s)
- Qurrat U Ain
- Centre for Molecular Informatics, Department of Chemistry, Lensfield Road, CB2 1EW, University of Cambridge, UK.
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11
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Zhang W, Ji L, Chen Y, Tang K, Wang H, Zhu R, Jia W, Cao Z, Liu Q. When drug discovery meets web search: Learning to Rank for ligand-based virtual screening. J Cheminform 2015; 7:5. [PMID: 25705262 PMCID: PMC4333300 DOI: 10.1186/s13321-015-0052-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Accepted: 01/07/2015] [Indexed: 11/30/2022] Open
Abstract
Background The rapid increase in the emergence of novel chemical substances presents a substantial demands for more sophisticated computational methodologies for drug discovery. In this study, the idea of Learning to Rank in web search was presented in drug virtual screening, which has the following unique capabilities of 1). Applicable of identifying compounds on novel targets when there is not enough training data available for these targets, and 2). Integration of heterogeneous data when compound affinities are measured in different platforms. Results A standard pipeline was designed to carry out Learning to Rank in virtual screening. Six Learning to Rank algorithms were investigated based on two public datasets collected from Binding Database and the newly-published Community Structure-Activity Resource benchmark dataset. The results have demonstrated that Learning to rank is an efficient computational strategy for drug virtual screening, particularly due to its novel use in cross-target virtual screening and heterogeneous data integration. Conclusions To the best of our knowledge, we have introduced here the first application of Learning to Rank in virtual screening. The experiment workflow and algorithm assessment designed in this study will provide a standard protocol for other similar studies. All the datasets as well as the implementations of Learning to Rank algorithms are available at http://www.tongji.edu.cn/~qiliu/lor_vs.html. The analogy between web search and ligand-based drug discovery ![]()
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Affiliation(s)
- Wei Zhang
- Department of Central Laboratory, Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Lijuan Ji
- Huai'an Second People's Hospital affiliated to Xuzhou Medical College, Huai'an, China
| | - Yanan Chen
- Department of Central Laboratory, Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Kailin Tang
- Department of Central Laboratory, Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Haiping Wang
- Department of Central Laboratory, Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China ; Department of Computer Science, Hefei University of Technology, Hefei, 230009 China
| | - Ruixin Zhu
- Department of Central Laboratory, Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Wei Jia
- R & D Information, AstraZeneca, Shanghai, China
| | - Zhiwei Cao
- Department of Central Laboratory, Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Qi Liu
- Department of Central Laboratory, Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
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Cortés-Ciriano I, Ain QU, Subramanian V, Lenselink EB, Méndez-Lucio O, IJzerman AP, Wohlfahrt G, Prusis P, Malliavin TE, van Westen GJP, Bender A. Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects. MEDCHEMCOMM 2015. [DOI: 10.1039/c4md00216d] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Proteochemometric (PCM) modelling is a computational method to model the bioactivity of multiple ligands against multiple related protein targets simultaneously.
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Affiliation(s)
- Isidro Cortés-Ciriano
- Unité de Bioinformatique Structurale
- Institut Pasteur and CNRS UMR 3825
- Structural Biology and Chemistry Department
- 75 724 Paris
- France
| | - Qurrat Ul Ain
- Unilever Centre for Molecular Informatics
- Department of Chemistry
- CB2 1EW Cambridge
- UK
| | | | - Eelke B. Lenselink
- Division of Medicinal Chemistry
- Leiden Academic Centre for Drug Research
- Leiden
- The Netherlands
| | - Oscar Méndez-Lucio
- Unilever Centre for Molecular Informatics
- Department of Chemistry
- CB2 1EW Cambridge
- UK
| | - Adriaan P. IJzerman
- Division of Medicinal Chemistry
- Leiden Academic Centre for Drug Research
- Leiden
- The Netherlands
| | - Gerd Wohlfahrt
- Computer-Aided Drug Design
- Orion Pharma
- FIN-02101 Espoo
- Finland
| | - Peteris Prusis
- Computer-Aided Drug Design
- Orion Pharma
- FIN-02101 Espoo
- Finland
| | - Thérèse E. Malliavin
- Unité de Bioinformatique Structurale
- Institut Pasteur and CNRS UMR 3825
- Structural Biology and Chemistry Department
- 75 724 Paris
- France
| | - Gerard J. P. van Westen
- European Molecular Biology Laboratory
- European Bioinformatics Institute
- Wellcome Trust Genome Campus
- Hinxton
- UK
| | - Andreas Bender
- Unilever Centre for Molecular Informatics
- Department of Chemistry
- CB2 1EW Cambridge
- UK
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13
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Cortes-Ciriano I, van Westen GJ, Lenselink EB, Murrell DS, Bender A, Malliavin T. Proteochemometric modeling in a Bayesian framework. J Cheminform 2014; 6:35. [PMID: 25045403 PMCID: PMC4083135 DOI: 10.1186/1758-2946-6-35] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2014] [Accepted: 06/18/2014] [Indexed: 11/10/2022] Open
Abstract
Proteochemometrics (PCM) is an approach for bioactivity predictive modeling which models the relationship between protein and chemical information. Gaussian Processes (GP), based on Bayesian inference, provide the most objective estimation of the uncertainty of the predictions, thus permitting the evaluation of the applicability domain (AD) of the model. Furthermore, the experimental error on bioactivity measurements can be used as input for this probabilistic model. In this study, we apply GP implemented with a panel of kernels on three various (and multispecies) PCM datasets. The first dataset consisted of information from 8 human and rat adenosine receptors with 10,999 small molecule ligands and their binding affinity. The second consisted of the catalytic activity of four dengue virus NS3 proteases on 56 small peptides. Finally, we have gathered bioactivity information of small molecule ligands on 91 aminergic GPCRs from 9 different species, leading to a dataset of 24,593 datapoints with a matrix completeness of only 2.43%. GP models trained on these datasets are statistically sound, at the same level of statistical significance as Support Vector Machines (SVM), with R02 values on the external dataset ranging from 0.68 to 0.92, and RMSEP values close to the experimental error. Furthermore, the best GP models obtained with the normalized polynomial and radial kernels provide intervals of confidence for the predictions in agreement with the cumulative Gaussian distribution. GP models were also interpreted on the basis of individual targets and of ligand descriptors. In the dengue dataset, the model interpretation in terms of the amino-acid positions in the tetra-peptide ligands gave biologically meaningful results.
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Affiliation(s)
- Isidro Cortes-Ciriano
- Institut Pasteur, Unité de Bioinformatique Structurale; CNRS UMR 3825; Département de Biologie Structurale et Chimie
| | - Gerard Jp van Westen
- ChEMBL Group, European Molecular Biology Laboratory European Bioinformatics Institute, Wellcome Trust Genome Campus, CB10 1SD, Hinxton, Cambridge, UK
| | - Eelke Bart Lenselink
- Division of Medicinal Chemistry, Leiden Academic Center for Drug Research, Leiden, The Netherlands
| | - Daniel S Murrell
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Andreas Bender
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Thérèse Malliavin
- Institut Pasteur, Unité de Bioinformatique Structurale; CNRS UMR 3825; Département de Biologie Structurale et Chimie
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