Classification of nervous system withdrawn and approved drugs with ToxPrint features via machine learning strategies.
Journal:
Computer methods and programs in biomedicine
PMID:
28325450
Abstract
BACKGROUND AND OBJECTIVES: Early-phase virtual screening of candidate drug molecules plays a key role in pharmaceutical industry from data mining and machine learning to prevent adverse effects of the drugs. Computational classification methods can distinguish approved drugs from withdrawn ones. We focused on 6 data sets including maximum 110 approved and 110 withdrawn drugs for all and nervous system diseases to distinguish approved drugs from withdrawn ones.
Authors
Keywords
Algorithms
Area Under Curve
Benzene
Carbon
Computer Simulation
Data Mining
Drug Approval
Drug Monitoring
Ethylamines
False Positive Reactions
Humans
Linear Models
Nervous System
Patient Safety
Pattern Recognition, Automated
Pharmaceutical Preparations
Product Surveillance, Postmarketing
Risk
Safety-Based Drug Withdrawals
Sensitivity and Specificity
Support Vector Machine
Toluene