Abstract
Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors.
Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors.
Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.
Keywords: Thrombin inhibitors, inhibitory constant, machine learning, model evaluation, descriptor selection, regression model.
Combinatorial Chemistry & High Throughput Screening
Title:In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning
Volume: 21 Issue: 9
Author(s): Junnan Zhao, Lu Zhu, Weineng Zhou, Lingfeng Yin, Yuchen Wang, Yuanrong Fan, Yadong Chen*Haichun Liu*
Affiliation:
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198 Jiangsu,China
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198 Jiangsu,China
Keywords: Thrombin inhibitors, inhibitory constant, machine learning, model evaluation, descriptor selection, regression model.
Abstract: Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors.
Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors.
Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.
Export Options
About this article
Cite this article as:
Zhao Junnan , Zhu Lu , Zhou Weineng , Yin Lingfeng , Wang Yuchen , Fan Yuanrong , Chen Yadong *, Liu Haichun *, In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning, Combinatorial Chemistry & High Throughput Screening 2018; 21 (9) . https://dx.doi.org/10.2174/1386207322666181220130232
DOI https://dx.doi.org/10.2174/1386207322666181220130232 |
Print ISSN 1386-2073 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5402 |
Call for Papers in Thematic Issues
Artificial Intelligence Methods for Biomedical, Biochemical and Bioinformatics Problems
Recently, a large number of technologies based on artificial intelligence have been developed and applied to solve a diverse range of problems in the areas of biomedical, biochemical and bioinformatics problems. By utilizing powerful computing resources and massive amounts of data, methods based on artificial intelligence can significantly improve the ...read more
Eco-friendly Agents for Biological Control of Pathogenic Diseases
The discovery of an alternative biological approach to disease management includes work on medicinal products derived from natural sources as a starting point for the development of eco-friendly agents for these diseases and the injuries they cause, as well as reducing human contact with hazardous chemicals and their residues. We ...read more
Emerging trends in diseases mechanisms, noble drug targets and therapeutic strategies: focus on immunological and inflammatory disorders
Recently infectious and inflammatory diseases have been a key concern worldwide due to tremendous morbidity and mortality world Wide. Recent, nCOVID-9 pandemic is a good example for the emerging infectious disease outbreak. The world is facing many emerging and re-emerging diseases out breaks at present however, there is huge lack ...read more
Exploring Spectral Graph Theory in Combinatorial Chemistry
Scope of the Thematic Issue: Combinatorial chemistry involves the synthesis and analysis of a large number of diverse compounds simultaneously. Traditional methods rely on brute force experimentation, which can be time-consuming and resource-intensive. Spectral Graph Theory, a branch of mathematics dealing with the properties of graphs in relation to the ...read more
- Author Guidelines
- Graphical Abstracts
- Fabricating and Stating False Information
- Research Misconduct
- Post Publication Discussions and Corrections
- Publishing Ethics and Rectitude
- Increase Visibility of Your Article
- Archiving Policies
- Peer Review Workflow
- Order Your Article Before Print
- Promote Your Article
- Manuscript Transfer Facility
- Editorial Policies
- Allegations from Whistleblowers
Related Articles
-
Meet Our Associate Editorial Board Member
Cardiovascular & Hematological Disorders-Drug Targets The Translocator Protein 18 kDa (TSPO) and Its Role in Mitochondrial Biology and Psychiatric Disorders
Mini-Reviews in Medicinal Chemistry Vulnerable Plaque Versus Vulnerable Patient: Emerging Blood Biomarkers for Risk Stratification
Endocrine, Metabolic & Immune Disorders - Drug Targets LncRNAs as Architects in Cancer Biomarkers with Interface of Epitranscriptomics- Incipient Targets in Cancer Therapy
Current Cancer Drug Targets Therapeutic Targeting of TRP Channels - The TR(i)P to Pain Relief
Current Topics in Medicinal Chemistry Nanotechnology Advances in Brain Tumors: The State of the Art
Recent Patents on Anti-Cancer Drug Discovery Chemopreventive Properties and Molecular Mechanisms of the Bioactive Compounds in Hibiscus Sabdariffa Linne
Current Medicinal Chemistry Recent Patents on Cardiovascular Stem Cells
Recent Patents on Cardiovascular Drug Discovery Pharmacokinetic and Pharmacodynamic Properties of Anti-VEGF Drugs After Intravitreal Injection
Current Drug Metabolism Connexins in Renal Endothelial Function and Dysfunction
Cardiovascular & Hematological Disorders-Drug Targets What have Genetically Engineered Mice Taught Us About Ischemic Injury?
Current Molecular Medicine Humanized SCID Mice Models of SLE
Current Pharmaceutical Design Synthesis and Evaluation of Thiazolidinedione-Coumarin Adducts as Antidiabetic, Anti-Inflammatory and Antioxidant Agents
Letters in Organic Chemistry Effects of Estrogens on Atherogenesis
Current Vascular Pharmacology Silencing the Brain May be Better than Stimulating it. The GABA Effect
Current Pharmaceutical Design Myocardial Perfusion SPECT Imaging in Patients after Percutaneous Coronary Intervention
Current Cardiology Reviews Perspectives on Development and Regulation of Therapeutic Products for CED-Based Therapy of Neurodegenerative Diseases
Current Pharmaceutical Biotechnology VEGF, a Mediator of the Effect of Experience on Hippocampal Neurogenesis
Current Alzheimer Research Protein-Protein Interactions: Recent Progress in the Development of Selective PDZ Inhibitors
Current Chemical Biology Pathophysiology of Sepsis in the Elderly: Clinical Impact and Therapeutic Considerations
Current Drug Targets