DRUG DESIGN BY MACHINE LEARNING - THE USE OF INDUCTIVE LOGIC PROGRAMMING TO MODEL THE STRUCTURE-ACTIVITY-RELATIONSHIPS OF TRIMETHOPRIM ANALOGS BINDING TO DIHYDROFOLATE-REDUCTASE
DRUG DESIGN BY MACHINE LEARNING - THE USE OF INDUCTIVE LOGIC PROGRAMMING TO MODEL THE STRUCTURE-ACTIVITY-RELATIONSHIPS OF TRIMETHOPRIM ANALOGS BINDING TO DIHYDROFOLATE-REDUCTASE
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DOI：
10.1073/pnas.89.23.11322
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发表时间：
1992-12-01
影响因子：
11.1
通讯作者：
STERNBERG, MJE
中科院分区：
文献类型：
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作者：
KING, RD;MUGGLETON, S;STERNBERG, MJE
将归纳逻辑编程领域的机器学习程序GOEM应用于构效关系建模的药物设计问题。该计划的训练数据是44个甲氧苄啶类似物，以及它们对大肠杆菌二氢叶酸还原酶的抑制作用。另有11种化合物被用作看不见的测试数据。Golem得到的规则在统计上对训练数据更准确，在测试数据上也比Hansch线性回归模型更好。重要的是，机器学习产生了可以理解的规则，这些规则在极性、灵活性和氢键特征方面表征了受青睐的抑制剂的化学特征。这些规则与在结晶学上观察到的相互作用的立体化学相一致。
The machine learning program GOLEM from the field of inductive logic programming was applied to the drug design problem of modeling structure-activity relationships. The training data for the program were 44 trimethoprim analogues and their observed inhibition of Escherichia coli dihydrofolate reductase. A further 11 compounds were used as unseen test data. GOLEM obtained rules that were statistically more accurate on the training data and also better on the test data than a Hansch linear regression model. Importantly machine learning yields understandable rules that characterized the chemistry of favored inhibitors in terms of polarity, flexibility, and hydrogen-bonding character. These rules agree with the stereochemistry of the interaction observed crystallographically.