Abstract
As part of the intensive efforts in facilitating drug discovery, computational methods have been explored as low-cost and efficient tools for predicting various toxicological properties and adverse drug reactions (ADR) of pharmaceutical agents. More recently, machine learning methods have been applied for developing tools capable of predicting diverse spectrum of compounds of different toxicological properties and ADR profiles. Based on the results of a number of studies, these methods have shown promising potential in predicting a variety of toxicological properties and ADR profiles. This article reviews the strategies, current progresses, underlying difficulties and future prospects in using machine learning methods for predicting compounds of specific toxicological property or ADR profile.
Keywords: Statistical learning methods, toxicology, molecular descriptors, structural diversity, adverse effects, risk assessment, drug design, pharmaceutical preparations
Current Drug Safety
Title: Advances in Machine Learning Prediction of Toxicological Properties and Adverse Drug Reactions of Pharmaceutical Agents
Volume: 3 Issue: 2
Author(s): Xiao Hua Ma, Rong Wang, Yin Xue, Ze Rong Li, Sheng Yong Yang, Yu Quan Wei and Yu Zong Chen
Affiliation:
Keywords: Statistical learning methods, toxicology, molecular descriptors, structural diversity, adverse effects, risk assessment, drug design, pharmaceutical preparations
Abstract: As part of the intensive efforts in facilitating drug discovery, computational methods have been explored as low-cost and efficient tools for predicting various toxicological properties and adverse drug reactions (ADR) of pharmaceutical agents. More recently, machine learning methods have been applied for developing tools capable of predicting diverse spectrum of compounds of different toxicological properties and ADR profiles. Based on the results of a number of studies, these methods have shown promising potential in predicting a variety of toxicological properties and ADR profiles. This article reviews the strategies, current progresses, underlying difficulties and future prospects in using machine learning methods for predicting compounds of specific toxicological property or ADR profile.
Export Options
About this article
Cite this article as:
Ma Hua Xiao, Wang Rong, Xue Yin, Li Rong Ze, Yang Yong Sheng, Wei Quan Yu and Chen Zong Yu, Advances in Machine Learning Prediction of Toxicological Properties and Adverse Drug Reactions of Pharmaceutical Agents, Current Drug Safety 2008; 3 (2) . https://dx.doi.org/10.2174/157488608784529224
DOI https://dx.doi.org/10.2174/157488608784529224 |
Print ISSN 1574-8863 |
Publisher Name Bentham Science Publisher |
Online ISSN 2212-3911 |
- 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
-
Computational Studies of Free Radical-Scavenging Properties of Phenolic Compounds
Current Topics in Medicinal Chemistry Potential Use of Protease Inhibitors as Vaginal and Colorectal Microbicides
Current HIV Research Biological Function and Medicinal Research Significance of G-Quadruplex Interactive Proteins
Current Topics in Medicinal Chemistry Biosensors for Antioxidant Evaluation in Biological Systems
Combinatorial Chemistry & High Throughput Screening Drug Development from Natural Resource: A Systematic Approach
Mini-Reviews in Medicinal Chemistry Apoptotic and Antiproliferative Potential of GAPDH from <i>Mallotus philippensis</i> Seed on Human Lung Carcinoma: <i>In Vitro</i> and <i>In Vivo</i> Approach
Protein & Peptide Letters Preparation of 2-(4-{[4-(Quinolin-2-ylmethoxy)phenyl]sulfanyl}phenyl) Propionic Acid (VUFB 20615) and 2-Methyl-2-(4-{[4-(quinolin-2- ylmethoxy)Phenyl]sulfanyl}phenyl)Propionic Acid (VUFB 20623) as Potential Antileukotrienic Agents
Current Organic Chemistry Purine Nucleoside Phosphorylase: A Potential Target for the Development of Drugs to Treat T-Cell- and Apicomplexan Parasite-Mediated Diseases
Current Drug Targets Coumarin-1,2,3-triazole Hybrid Molecules: An Emerging Scaffold for Combating Drug Resistance
Current Topics in Medicinal Chemistry Current Evidence Regarding Low-carb Diets for The Metabolic Control of Type-2 Diabetes
Current Diabetes Reviews Comparing the Interaction of Cyclophosphamide Monohydrate to Human Serum Albumin as Opposed to Holo-Transferrin by Spectroscopic and Molecular Modeling Methods: Evidence for Allocating the Binding Site
Protein & Peptide Letters Realizing the Potential of Health-Promoting Rosehips from Dogroses (Rosa sect. Caninae)
Current Bioactive Compounds Psilocybin-Occasioned Mystical Experiences in the Treatment of Tobacco Addiction
Current Drug Abuse Reviews Systems Medicine Approaches to Improving Understanding, Treatment, and Clinical Management of Neuroendocrine Prostate Cancer
Current Pharmaceutical Design The Gold Nanorod-Biology Interface: From Proteins to Cells to Tissue
Current Physical Chemistry Application of Bacterial Nanocellulose in Cancer Drug Delivery: A Review
Current Pharmaceutical Design Pulmonary Embolism Response Team (PERT) - A New Paradigm for the Treatment of Pulmonary Embolism
Current Pharmaceutical Design Predicting Clearance in Humans from In Vitro Data
Current Topics in Medicinal Chemistry Ceftriaxone-Vancomycin Drug Toxicity Reduction by VRP 1020 in Mus musculus Mice
Current Clinical Pharmacology Micropropagation: A Tool for the Production of High Quality Plant-based Medicines
Current Pharmaceutical Biotechnology