用智慧型基因演算法(IGA)優化藥物溶解度的預測

Using Intelligent Genetic Algorithm (IGA) to Optimize The Drug Solubility Prediction

張志銘(Chih-Ming Chang)

指導教授 ： 何信璋

國立虎尾科技大學/工程學院/自動化工程研究所/碩士(2011年)

摘要

當吃藥的時候,它需要溶解才能被傳遞到血液產生作用,所以藥物溶解度的預測可以研究藥物在人體的溶解速度,也可以減少製作藥物的實驗次數。本文使用智慧型基因演算法(IGA)配合模糊神經網路(FNN)來預測藥物溶解度。FNN具有高效率的學習模式，可以更快速的進行模糊推理工作，每一個節點都能經由學習改變其參數，達到適應性的功能。IGA是GA配合直交式實驗設計的智慧型演算法，可有效推理出近似最佳解。由本來研究結果得知，用(FNN)來架構搭配(IGA)的預測結果優於前人文獻的方法，用此方法可以更準確預測藥物溶解度，提高了開發新藥物的成功率。

關鍵字

智慧型基因演算法(IGA) ； 溶解度 ； 建模 ； 模糊神經網路(FNN) ； 直交實驗設計

並列摘要

When take the medicine, it needs to be passed to dissolve the blood and have an effect, so you can study drug solubility prediction of drug dissolution rate in the human body, can also reduce the production of drugs, the number of experiments. This article uses intelligent genetic algorithm (IGA) with the fuzzy neural network (FNN) to predict drug solubility. FNN has a high efficiency mode of learning, more quickly the fuzzy reasoning work, each node can change its parameters through learning, to achieve adaptive function. IGA is a GA-type orthogonal experimental design with intelligent algorithms, which can effectively deduce the approximate optimal solution. By the findings that have been used (FNN) to architecture with (IGA) prediction method is superior to previous literature, this method can more accurately predict drug solubility, enhance the development of new drug discovery success rate.

並列關鍵字

intelligent genetic algorithm (IGA) ； solubility ； modeling ； fuzzy neural network (FNN) ； orthogonal experimental design

參考文獻

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