Pattern recognitiion and structure-activity relationship studies. Computer-assisted prediction of antitumor activity in structurally diverse drugs in an experimental mouse brain tumor system.
This paper reports the application of pattern recognition and substructural analysis to the problem of predicting the antineoplastic activity of 24 test compounds in an experimental mouse brain tumor system based on 138 structurally diverse compounds tested in this tumor system. The molecules were represented by three types of substructural fragments, the augmented atom, the heteropath, and the ring fragments. Of the two pattern recognition methods used to predict the activity of the test compounds the nearest neighbor method predicted 83% correctly while the learning machine method predicted 92% correctly. The test structures and the important substructural fragments used in this study are given and the implications of these results are discussed.
D000818:Animals; D000970:Antineoplastic Agents / Q000627:therapeutic use*; D001932:Brain Neoplasms / Q000188:drug therapy*; D003936:Diagnosis, Computer-Assisted; D004806:Ependymoma / Q000188:drug therapy; D008433:Mathematics; D008722:Methods; D051379:Mice; D009374:Neoplasms, Experimental / Q000188:drug therapy; D010363:Pattern Recognition, Automated; D013329:Structure-Activity Relationship
D000970:Antineoplastic Agents