ADMET and adverse effects predictive modeling based on FDA-approved drugs Data

Abstract

Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties and adverse effects determine the success of each drug. These factors play an important role in the late-stage failure of drug candidates and withdrawal of approved drugs from the market. In early drug discovery research, computational methods along with the integration of large, clean and safe compound data are effective approaches to minimizing the risk of late-stage attrition and reducing the number of safety issues. This thesis describes a relational database called Integrated Database of ADMET and Adverse effects for Predictive Modeling based on FDA approved drug data (IDAAPM). This database is designed to integrate approved drug data, including drug approval information, ADMET, adverse effects, chemical structures and molecular descriptors, targets and binding affinity data including their associated scientific literature references. Moreover, the database is connected to a responsive website interface, and coupled with a modern data analytic platform (KNIME). Currently, IDAAPM contains FDA approval applications (19,226), products (31,815), active ingredients (2,505), ADMET properties (1,076), drug adverse effect pairs (2,472,770), molecular structures (1,629), drug targets (2,220) and drug target interactions (36,963). Therefore, IDAAPM is a unique and comprehensive platform that provides safe compound data and enables the researcher to run a predictive analysis of their compounds of interest in terms of ADMET and adverse effect properties. IDAAPM can be accessed through a web browser at http://idaapm.helsinki.fi or a downloaded KNIME workflow at http://idaapm.helsinki.fi/Download.Siirretty Doriast