Pharmacophore discovery using the Inductive Logic Programming system PROGOL

作     者：Finn, P Muggleton, S Page, D Srinivasan, A 

作者机构：Univ Louisville Dept Engn Math & Comp Sci Speed Sci Sch Louisville KY 40292 USA Pfizer Ltd Cent Res Computat Chem Ramsgate Rd Sandwich CT13 9NJ Kent England Univ York Dept Comp Sci York YO1 5DD N Yorkshire England Univ Oxford Comp Lab Oxford OX1 3QD England 

出 版 物：《MACHINE LEARNING》 (机器学习)

年 卷 期：1998年第30卷第2-3期

页      面：241-270页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：inductive logic programming pharmacophore structure-activity prediction 

摘      要：This paper presents a case study of a machine-aided knowledge discovery process within the general area of drug design. Within drug design, the particular problem of pharmacophore discovery is isolated, and the Inductive Logic Programming (ILP) system PROGOL is applied to the problem of identifying potential pharmacophores for ACE inhibition. The case study reported in this paper supports four general lessons for machine learning and knowledge discovery, as well as more specific lessons for pharmacophore discovery, for Inductive Logic Programming, and for ACE inhibition. The general lessons for machine learning and knowledge discovery are as follows. 1. An initial rediscovery step is a useful tool when approaching a new application domain. 2. General machine learning heuristics may fail to match the derails of an application domain, but it may be possible to successfully apply a heuristic-based algorithm in spite of the mismatch. 3. A complete search for all plausible hypotheses can provide useful information to a user, although experimentation may be required to choose between competing hypotheses. 4. A declarative knowledge representation facilitates the development and debugging of background knowledge in collaboration with a domain expert, as well as the communication of final results.