title: Identification of new candidate drugs for lung cancer using chemical-chemical interactions, chemical-protein interactions and a K-means clustering algorithm.
abstract: Lung cancer, characterized by uncontrolled cell growth in the lung tissue, is the leading cause of global cancer deaths. Until now, effective treatment of this disease is limited. Many synthetic compounds have emerged with the advancement of combinatorial chemistry. Identification of effective lung cancer candidate drug compounds among them is a great challenge. Thus, it is necessary to build effective computational methods that can assist us in selecting for potential lung cancer drug compounds. In this study, a computational method was proposed to tackle this problem. The chemical-chemical interactions and chemical-protein interactions were utilized to select candidate drug compounds that have close associations with approved lung cancer drugs and lung cancer-related genes. A permutation test and K-means clustering algorithm were employed to exclude candidate drugs with low possibilities to treat lung cancer. The final analysis suggests that the remaining drug compounds have potential anti-lung cancer activities and most of them have structural dissimilarity with approved drugs for lung cancer.
authors: a School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong , Yantai University , Yantai , 264005 , P.R. China.
authors: Jing
authors: J
authors: Lu
authors: b College of Information Engineering , Shanghai Maritime University , Shanghai 201306 , P.R. China.
authors: Lei
authors: L
authors: Chen
authors: b College of Information Engineering , Shanghai Maritime University , Shanghai 201306 , P.R. China.
authors: Jun
authors: J
authors: Yin
authors: c The Key Laboratory of Stem Cell Biology , Institute of Health Sciences, Shanghai Jiao Tong University School of Medicine (SJTUSM) and Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS) , Shanghai 200025 , P.R. China.
authors: Tao
authors: T
authors: Huang
authors: a School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong , Yantai University , Yantai , 264005 , P.R. China.
authors: Yi
authors: Y
authors: Bi
authors: c The Key Laboratory of Stem Cell Biology , Institute of Health Sciences, Shanghai Jiao Tong University School of Medicine (SJTUSM) and Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS) , Shanghai 200025 , P.R. China.
authors: Xiangyin
authors: X
authors: Kong
authors: d Drug Discovery and Design Center , Shanghai Institute of Materia Medica , Shanghai 201203 , P.R. China.
authors: Mingyue
authors: M
authors: Zheng
authors: e College of Life Science , Shanghai University , Shanghai 200444 , P.R. China.
authors: Yu-Dong
authors: YD
authors: Cai
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000737:chemistry* / Q000494:pharmacology; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D062313:Databases, Pharmaceutical; D055808:Drug Discovery / Q000379:methods*; D006801:Humans; D008024:Ligands; D008175:Lung Neoplasms / Q000188:drug therapy; D009154:Mutation; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D013329:Structure-Activity Relationship
chemical_list: D000970:Antineoplastic Agents; D008024:Ligands; D011506:Proteins
keywords: K-means clustering algorithm; chemical–chemical interaction; chemical–protein interaction; lung cancer