Sternberg M J, Lewis R A, King R D, Muggleton S
Biomolecular Modelling Laboratory, Imperial Cancer Research Fund, London, UK.
Faraday Discuss. 1992(93):269-80. doi: 10.1039/fd9929300269.
A machine learning program, GOLEM, has been applied to two problems: (1) the prediction of protein secondary structure from sequence and (2) modelling a quantitative structure-activity relationship in drug design. GOLEM takes as input observations and combines them with background knowledge of chemistry to yield rules expressed as stereochemical principles for prediction. The secondary structure prediction was explored on the alpha/alpha class of proteins; on an unrelated test set it yielded 81% accuracy. The rules from GOLEM defined patterns of residues forming alpha-helices. The system studied for drug design was the activities of trimethoprim analogues binding to E. coli dihydrofolate reductase. The GOLEM rules were a better model than standard regression approaches. More importantly, these rules described the chemical properties of the enzyme-binding site that were in broad agreement with the crystallographic structure.
一个名为GOLEM的机器学习程序已被应用于两个问题：（1）从序列预测蛋白质二级结构，以及（2）在药物设计中建立定量构效关系模型。GOLEM将观测值作为输入，并将它们与化学背景知识相结合，以产生表示为立体化学原理的规则用于预测。在α/α类蛋白质上探索了二级结构预测；在一个不相关的测试集上，其准确率达到了81%。GOLEM得出的规则定义了形成α螺旋的残基模式。针对药物设计研究的系统是甲氧苄啶类似物与大肠杆菌二氢叶酸还原酶结合的活性。GOLEM规则比标准回归方法是更好的模型。更重要的是，这些规则描述了与晶体结构广泛一致的酶结合位点的化学性质。
