Author/Editor		Mladenić, D; Bratko, I; Karalič, A
Title		Avtomatsko učenje v snovanju zdravil
Translated title		Machine learning in drug design
Type		članek
Source		In: Solina F, Zajc B, eds. Računalništvo in informatika, umetna inteligenca, razpoznavanje vzorcev, biomedicinska tehnika, robotika. 2. elektrotehniška in računalniška konferenca ERK'93: zbornik. Zvezek B. Ljubljana: Slovenska sekcija IEEE,
Publication year		1993
Volume		str. 185-8
Language		slo
Abstract		This paper describes an approach to modelling drug activity using machine learning tools. Some experiments in modelling the quantitative structure-activity relationship (QAAR) using a standard, Hansch, method and a machine learning system GOLEM were already reported in literature. The paper describes the results of applying two other machine learning systems, MAGNUS ASSISTANT and RETIS, on the same data. The results achieved by the machine learning systems are better than the results of the Hansch method; therefore, machine learning tools can be considered as very promising for solving that kind of problems. Obtained results also illustrate the variations of performance of the different machine learning systems applied to this drug design problem.
Descriptors		DRUG DESIGN TRIMETHOPRIM MODELS, CHEMICAL ARTIFICIAL INTELLIGENCE