Using Chemoinformatics and Rough Set Rule Induction for HIV Drug Discovery

This paper presents a computational approach to HIV Drug discovery using rough set based rule induction. Since conventional drug discovery is a time consuming process in which drugs are discovered either by chance or by screening the natural products, alternative methods were required to hasten the process in order to abridge the demand and supply gap. Chemoinformatics, providing novel methodologies to alleviate the problem, helps chemists to make sense of the data, attempting to predict the properties of chemical substances from a sample of data which involves lesser amount of time as compared to discovering new drugs. In this paper we make use of rough based rule induction to compare rule sets from two categories of drug databases; HIV and General. Upon comparison drugs were discovered which shared common properties with HIV drugs. These selected drugs will then be passed for clinical testing.