{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T21:35:58Z","timestamp":1757540158479},"reference-count":29,"publisher":"World Scientific Pub Co Pte Lt","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Comp. Intel. Appl."],"published-print":{"date-parts":[[2017,3]]},"abstract":"<jats:p> The cytochrome P450 (CYP) superfamily, exists in the human liver, is responsible for more than 90% of the metabolism of clinical drugs. So it is necessary to adopt a new kind of computer simulation methods that can predict the rejection capability of compounds for a concrete CYPs isoform. In this work, a model is presented for classification of CYP450 1A2 inhibitors and noninhibitors based on a multi-tiered deep belief network (DBN) on a large dataset. The dataset composed of more than 13,000 heterogeneous compounds was acquired from PubChem. Firstly, 139 2D and 53 3D descriptors are calculated and preprocessed. Then, the unsupervised learning method is used to train DBN model to automatically extract multiple levels of distributed representation from the descriptors of training set. Finally, by using testing set and external validation set, we evaluate the classified performance of DBN for the inhibition of CYP1A2. Meanwhile, the proposed model is compared with shallow machine learning models (support vector machine (SVM) and artificial neural network (ANN)). We also discussed the performance of DBN by comparing it with different features combination. The experimental results showed that DBN has a better prediction ability compared with SVM and ANN. And these models combined with the features of 2D and 3D obtain the best forecast accuracy. <\/jats:p>","DOI":"10.1142\/s146902681750002x","type":"journal-article","created":{"date-parts":[[2017,3,6]],"date-time":"2017-03-06T06:07:48Z","timestamp":1488780468000},"page":"1750002","source":"Crossref","is-referenced-by-count":7,"title":["Classification of Cytochrome P450 1A2 Inhibitors and Noninhibitors Based on Deep Belief Network"],"prefix":"10.1142","volume":"16","author":[{"given":"Long","family":"Yu","sequence":"first","affiliation":[{"name":"Network Center, Xinjiang University, 14 Shengli Road, Xinjiang Uygur Autonomous Region, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Software, Xinjiang University, 499 Xibei Road, Xinjiang Uygur Autonomous Region, Urumqi 830008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengwei","family":"Tian","sequence":"additional","affiliation":[{"name":"College of Software, Xinjiang University, 499 Xibei Road, Xinjiang Uygur Autonomous Region, Urumqi 830008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuangyin","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Software, Xinjiang University, 499 Xibei Road, Xinjiang Uygur Autonomous Region, Urumqi 830008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Li","sequence":"additional","affiliation":[{"name":"College of Engineering, Xinjiang Medical University, 393 Xinyi Road, Xinjiang Uygur Autonomous Region, Urumqi 830011, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2017,3,23]]},"reference":[{"key":"S146902681750002XBIB001","doi-asserted-by":"publisher","DOI":"10.1517\/17425255.2010.489550"},{"key":"S146902681750002XBIB002","doi-asserted-by":"publisher","DOI":"10.1021\/jm060333s"},{"key":"S146902681750002XBIB003","doi-asserted-by":"publisher","DOI":"10.1021\/jm040180d"},{"key":"S146902681750002XBIB004","doi-asserted-by":"publisher","DOI":"10.1021\/ci900299a"},{"key":"S146902681750002XBIB005","doi-asserted-by":"publisher","DOI":"10.1021\/ci200091h"},{"issue":"5","key":"S146902681750002XBIB006","first-page":"401","volume":"44","author":"Li M. 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