Main Article Content

Abstract

Drug Likeness prediction is a time-consuming and tedious process. An in-vitro method the drug development takes a long time to come to market. The failure rate is also another one to think about in this method. There are many in-silico methods currently available and developing to help the drug discovery and development process. Many online tools are available for predicting and classifying a drug after analyzing the drug-likeness properties of compounds. But most tools have their advantages and disadvantages. In this study, a tool is developed to predict the drug-likeness of compounds given as input to this software. This may help the chemists in analyzing a compound before actually preparing a compound for the drug discovery process. The tool includes both descriptor-based calculation and fingerprint-based calculation of the particular compounds. The descriptor-calculation also includes a set of rules and filters like Lipinski’s rule, Ghose filter, Veber filter and BBB likeness. The previous studies proved that the fingerprint-based prediction is more accurate than descriptor-based prediction. So, in the current study, the drug-likeness prediction tool incorporated the molecular descriptors and fingerprint-based calculations based on five different fingerprint types. The current study incorporated five different machine learning algorithms for prediction of drug-likeness and selected the algorithm, which has a high accuracy rate. When a chemist inputs a particular compound in SMILES format, the drug-likeness prediction tool predicts whether the given candidate compound is drug or non-drug.

Keywords

Drug likeness prediction ligand-based virtual screening QSAR molecular fingerprints machine learning prediction accuracy

Article Details

How to Cite
Ani R, Anand P S, Sreenath B, & Deepa O S. (2020). In Silico Prediction Tool for Drug-likeness of Compounds based on Ligand Based Screening. International Journal of Research in Pharmaceutical Sciences, 11(4), 6273-6281. https://doi.org/10.26452/ijrps.v11i4.3310