Abstract
Computational methods for predicting compounds of specific pharmacodynamic, pharmacokinetic, or toxicological property are useful for facilitating drug discovery and drug safety evaluation. The quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) methods are the most successfully used statistical learning methods for predicting compounds of specific property. More recently, other statistical learning methods such as neural networks and support vector machines have been explored for predicting compounds of higher structural diversity than those covered by QSAR and QSPR. These methods have shown promising potential in a number of studies. This article is intended to review the strategies, current progresses and underlying difficulties in using statistical learning methods for predicting compounds of specific property. It also evaluates algorithms commonly used for representing structural and physicochemical properties of compounds.
Keywords: Statistical learning methods, pharmacodynamic, pharmacokinetic, toxicology, QSAR, QSPR, molecular descriptors, structural diversity
Mini-Reviews in Medicinal Chemistry
Title: Prediction of Compounds with Specific Pharmacodynamic, Pharmacokinetic or Toxicological Property by Statistical Learning Methods
Volume: 6 Issue: 4
Author(s): C. W. Yap, Y. Xue, H. Li, Z. R. Li, C. Y. Ung, L. Y. Han, C. J. Zheng, Z. W. Cao and Y. Z. Chen
Affiliation:
Keywords: Statistical learning methods, pharmacodynamic, pharmacokinetic, toxicology, QSAR, QSPR, molecular descriptors, structural diversity
Abstract: Computational methods for predicting compounds of specific pharmacodynamic, pharmacokinetic, or toxicological property are useful for facilitating drug discovery and drug safety evaluation. The quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) methods are the most successfully used statistical learning methods for predicting compounds of specific property. More recently, other statistical learning methods such as neural networks and support vector machines have been explored for predicting compounds of higher structural diversity than those covered by QSAR and QSPR. These methods have shown promising potential in a number of studies. This article is intended to review the strategies, current progresses and underlying difficulties in using statistical learning methods for predicting compounds of specific property. It also evaluates algorithms commonly used for representing structural and physicochemical properties of compounds.
Export Options
About this article
Cite this article as:
Yap W. C., Xue Y., Li H., Li R. Z., Ung Y. C., Han Y. L., Zheng J. C., Cao W. Z. and Chen Z. Y., Prediction of Compounds with Specific Pharmacodynamic, Pharmacokinetic or Toxicological Property by Statistical Learning Methods, Mini-Reviews in Medicinal Chemistry 2006; 6 (4) . https://dx.doi.org/10.2174/138955706776361501
DOI https://dx.doi.org/10.2174/138955706776361501 |
Print ISSN 1389-5575 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5607 |
Call for Papers in Thematic Issues
Bioprospecting of Natural Products as Sources of New Multitarget Therapies
According to the Convention on Biological Diversity, bioprospecting is the exploration of biodiversity and indigenous knowledge to develop commercially valuable products for pharmaceutical and other applications. Bioprospecting involves searching for useful organic compounds in plants, fungi, marine organisms, and microorganisms. Natural products traditionally constituted the primary source of more than ...read more
Computational Frontiers in Medicinal Chemistry
The thematic issue "Computational Frontiers in Medicinal Chemistry" provides a robust platform for delving into state-of-the-art computational methodologies and technologies that significantly propel advancements in medicinal chemistry. This edition seeks to amalgamate top-tier reviews spotlighting the latest trends and breakthroughs in the fusion of computational approaches, including artificial intelligence (AI) ...read more
Mitochondria as a Therapeutic Target in Metabolic Disorders
Mitochondria are the primary site of adenosine triphosphate (ATP) production in mammalian cells. Moreover, these organelles are an important source of reactive oxygen and nitrogen species in virtually any nucleated cell type. The modulation of a myriad of cellular signaling pathways depends on the mitochondrial physiology. Mitochondrial dysfunction is observed ...read more
Natural Products and Dietary Supplements in Alleviation of Metabolic, Cardiovascular, and Neurological Disorders
Metabolic disorders like diabetes, obesity, inflammation, oxidative stress, cancer etc, cardiovascular disorders like angina, myocardial infarction, congestive heart failure etc as well as neurological disorders like Alzheimer?s, Parkinson?s, Epilepsy, Depression, etc are the global burden. They covered the major segment of the diseases and disorders from which the human community ...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
-
Neuro-psychopharmacogenetics and Neurological Antecedents of Posttraumatic Stress Disorder: Unlocking the Mysteries of Resilience and Vulnerability
Current Neuropharmacology The Structure-AChE Inhibitory Activity Relationships Study in a Series of Pyridazine Analogues
Medicinal Chemistry Translocator Protein Ligands as Promising Therapeutic Tools for Anxiety Disorders
Current Medicinal Chemistry Review of Current Chemoinformatic Tools for Modeling Important Aspects of CYPsmediated Drug Metabolism. Integrating Metabolism Data with Other Biological Profiles to Enhance Drug Discovery
Current Drug Metabolism Interference of Glycine Transporter 1: Modulation of Cognitive Functions Via Activation of Glycine-B Site of the NMDA Receptor
Central Nervous System Agents in Medicinal Chemistry Flavonoids and Linderone from Lindera oxyphylla and their Bioactivities
Combinatorial Chemistry & High Throughput Screening Serial Analysis of Gene Expression (SAGE): 13 Years of Application in Research
Current Pharmaceutical Biotechnology Systemic Therapeutic Gene Delivery for Cancer: Crafting Paris Arrow
Current Gene Therapy The Prognostic Effect of Clinical and Laboratory Findings on in-hospital Mortality in Patients with Confirmed COVID-19 Disease
Current Respiratory Medicine Reviews Low-dose COVID-19 CT Image Denoising Using CNN and its Method Noise Thresholding
Current Medical Imaging Advances in Computational Methods to Discover New NS2B-NS3 Inhibitors Useful Against Dengue and Zika Viruses
Current Topics in Medicinal Chemistry Estrogen Receptor Modulators: Relationships of Ligand Structure, Receptor Affinity and Functional Activity
Current Topics in Medicinal Chemistry Cell Penetrating Peptide Delivery of Splice Directing Oligonucleotides as a Treatment for Duchenne Muscular Dystrophy
Current Pharmaceutical Design Plasmodium Dihydroorotate Dehydrogenase: A Promising Target for Novel Anti-Malarial Chemotherapy
Infectious Disorders - Drug Targets Editorial: From Perfume Oils to Discovering and Making New Molecules: An International Chemical Biology Journey
Combinatorial Chemistry & High Throughput Screening COVID Tarnish Lung: Residual Radiological Lung Consequences of Infection with COVID-19
Current Respiratory Medicine Reviews Myofascial Temporomandibular Disorder
Current Rheumatology Reviews OPMSP: A Computational Method Integrating Protein Interaction and Sequence Information for the Identification of Novel Putative Oncogenes
Protein & Peptide Letters CypD: The Key to the Death Door
CNS & Neurological Disorders - Drug Targets Natural Products as α-Amylase and α-Glucosidase Inhibitors and their Hypoglycaemic Potential in the Treatment of Diabetes: An Update
Mini-Reviews in Medicinal Chemistry