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Title: Database development and machine learning prediction of pharmaceutical agents
Keywords: Machine learning, Support vector machine, pharmaceutical agents, virtual screening, acute toxicity, database
Issue Date: 8-Mar-2010
Citation: LIU XIANGHUI (2010-03-08). Database development and machine learning prediction of pharmaceutical agents. ScholarBank@NUS Repository.
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 extremely large compound libraries. The last part was devoted to an acute toxicity classification system based on statistical machine learning methods. Evaluation of acute toxicity is one of the big challenges faced by pharmaceutical companies and many administrative organizations now because acute toxicity study is widely needed but very costly. Legislation calls for the use of information from alternative non-animal approaches like in vitro methods and in silico computational methods. QSAR based approaches remain the current main in silico solutions to prediction of acute toxicities but the performance is not satisfactory. SVM was explored as a new computational method to address the current issues and make a breakthrough in prediction of diverse classes of chemicals. Studies show that SVM models have better prediction accuracies (overall ~85% and independent testing ~70%) than previous studies in classification of acute and non acute toxic chemicals.
Appears in Collections:Ph.D Theses (Open)

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