Classification of drug molecules considering their IC50 values using mixed-integer linear programming based hyper-boxes method

Background: A priori analysis of the activity of drugs on the target protein by computational approaches can be useful in narrowing down drug candidates for further experimental tests. Currently, there are a large number of computational methods that predict the activity of drugs on proteins. In this study, we approach the activity prediction problem as a classification problem and, we aim to improve the classification accuracy by introducing an algorithm that combines partial least squares regression with mixed-integer programming based hyper-boxes classification method, where drug molecules are classified as low active or high active regarding their binding activity (IC50 values) on target proteins. We also aim to determine the most significant molecular descriptors for the drug molecules. Results: We first apply our approach by analyzing the activities of widely known inhibitor datasets including Acetylcholinesterase (ACHE), Benzodiazepine Receptor (BZR), Dihydrofolate Reductase (D...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Pelin Armutlu, Muhittin Emre Ozdemir, Fadime Üney Yüksektepe, I. Halil Kavakli, Metin Türkay
• Keywords: BMCBI 2008, Drug, Drug Molecules, Target Proteins