Database development and machine learning prediction of pharmaceutical agents
type: CreativeWork
description: Drug discovery process is typically a lengthy and costly process. Target, efficacy and safety are the three major issues. Cheminformatics and bioinformatics tools are explored to increase the efficiency and reduce the cost and time of pharmaceutical research and development. This work represents computational approaches to address these issues. In the first study, a particular focus has been given to database developing of two web accessible databases: therapeutic targets database (TTD) and Information of Drug Activity Database (IDAD). The updated TTD is intended to be a more useful resource in complement to other related databases by providing comprehensive information about the primary targets and other drug data for the approved, clinical trial, and experimental drugs. IDAD is a drug activity database of drug and clinical trial compounds. The integration of information from these two databases leads to analysis of properties of drug and clinical trials compounds. It shows that there are some differences between them in terms of properties. This could lead to a better understanding the reasons for failures of clinical trials in drug discovery and serve as guidelines for selection of drug candidates for clinical trials. The second focus was given to the use of machine learning classification method for virtual screening of pharmaceutical agents. This method was tested on several systems like Abl inhibitors and HDAC inhibitors. It is shown that Support Vector Machine (SVM) based virtual screening system combined with a novel putative negative generation method is a highly efficient virtual screening tool. SVM models showed a prediction accuracy for non-inhibitors around 50% for independent testing set, which were comparable against other results, while the prediction accuracy for non-inhibitors is >99.9%, which were substantially better than the typical values of 77%~96% of other studies. This high prediction accuracy for non-inhibitors is favorable for screening of…
abstract: Drug discovery process is typically a lengthy and costly process. Target, efficacy and safety are the three major issues. Cheminformatics and bioinformatics tools are explored to increase the efficiency and reduce the cost and time of pharmaceutical research and development. This work represents computational approaches to address these issues. In the first study, a particular focus has been given to database developing of two web accessible databases: therapeutic targets database (TTD) and Information of Drug Activity Database (IDAD). The updated TTD is intended to be a more useful resource in complement to other related databases by providing comprehensive information about the primary targets and other drug data for the approved, clinical trial, and experimental drugs. IDAD is a drug activity database of drug and clinical trial compounds. The integration of information from these two databases leads to analysis of properties of drug and clinical trials compounds. It shows that there are some differences between them in terms of properties. This could lead to a better understanding the reasons for failures of clinical trials in drug discovery and serve as guidelines for selection of drug candidates for clinical trials. The second focus was given to the use of machine learning classification method for virtual screening of pharmaceutical agents. This method was tested on several systems like Abl inhibitors and HDAC inhibitors. It is shown that Support Vector Machine (SVM) based virtual screening system combined with a novel putative negative generation method is a highly efficient virtual screening tool. SVM models showed a prediction accuracy for non-inhibitors around 50% for independent testing set, which were comparable against other results, while the prediction accuracy for non-inhibitors is >99.9%, which were substantially better than the typical values of 77%~96% of other studies. This high prediction accuracy for non-inhibitors is favorable for screening of…
author: LIU XIANGHUI
keywords: Machine learning, Support vector machine, pharmaceutical agents, virtual screening, acute toxicity, database
date published: 2010-03-08
date modified: 2026-07-24T03:30:47Z
provider: National University of Singapore
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: National University of Singapore