Identification of Drug Compound Bio-Activities Through Artificial Intelligence

Abstract

In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning algorithms such as regression and classification models to enhance the efficiency, efficacy, and quality of developed outputs. Applying machine learning model for drug discovery on different diseases that exists already, the author team fetched the datasets from the ChEMBL database that contain the bio-activity data, after preprocessing the data according to the bioactivity threshold in order to obtain a curated bio-activity data. Therefore, structural analogs of the drugs that bind to the target are selected as drug candidates. However, even though compounds are not structural analogs, they may achieve the desired response. A new drug discovery method based on drug response, which can complement the structure-based methods, is needed. Present manuscript is an effort for same.