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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 (IC<jats:sub>50<\/jats:sub> values) on target proteins. We also aim to determine the most significant molecular descriptors for the drug molecules.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We first apply our approach by analyzing the activities of widely known inhibitor datasets including Acetylcholinesterase (ACHE), Benzodiazepine Receptor (BZR), Dihydrofolate Reductase (DHFR), Cyclooxygenase-2 (COX-2) with known IC<jats:sub>50<\/jats:sub> values. The results at this stage proved that our approach consistently gives better classification accuracies compared to 63 other reported classification methods such as SVM, Na\u00efve Bayes, where we were able to predict the experimentally determined IC<jats:sub>50<\/jats:sub> values with a worst case accuracy of 96%. To further test applicability of this approach we first created dataset for Cytochrome P450 C17 inhibitors and then predicted their activities with 100% accuracy.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>Our results indicate that this approach can be utilized to predict the inhibitory effects of inhibitors based on their molecular descriptors. This approach will not only enhance drug discovery process, but also save time and resources committed.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-9-411","type":"journal-article","created":{"date-parts":[[2008,10,24]],"date-time":"2008-10-24T18:13:52Z","timestamp":1224872032000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Classification of drug molecules considering their IC50 values using mixed-integer linear programming based hyper-boxes method"],"prefix":"10.1186","volume":"9","author":[{"given":"Pelin","family":"Armutlu","sequence":"first","affiliation":[]},{"given":"Muhittin E","family":"Ozdemir","sequence":"additional","affiliation":[]},{"given":"Fadime","family":"Uney-Yuksektepe","sequence":"additional","affiliation":[]},{"given":"I Halil","family":"Kavakli","sequence":"additional","affiliation":[]},{"given":"Metin","family":"Turkay","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2008,10,3]]},"reference":[{"key":"2396_CR1","first-page":"137","volume-title":"3D QSAR in Drug Design: Theory, Methods and Applications","author":"VE Golender","year":"1993","unstructured":"Golender VE, Vorpagel ER: Computer Assisted Pharmacophore Identification. 3D QSAR in Drug Design: Theory, Methods and Applications 1993, 137\u2013149."},{"key":"2396_CR2","doi-asserted-by":"publisher","first-page":"5959","DOI":"10.1021\/ja00226a005","volume":"110","author":"RD Cramer","year":"1988","unstructured":"Cramer RD, Patterson DE, Bunce JD: Comparative molecular field analysis (CoMFA). 1. 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