Development of a method for evaluating drug-likeness and ease of synthesis using a data set in which compounds are assigned scores based on chemists' intuition
Development of a method for evaluating drug-likeness and ease of synthesis using a data set in which compounds are assigned scores based on chemists' intuition
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DOI：
10.1021/ci034043l
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发表时间：
2003-07-01
期刊：
影响因子：
--
通讯作者：
Yoshikawa, K
中科院分区：
文献类型：
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作者：
Takaoka, Y;Endo, Y;Yoshikawa, K
药物相似性的概念是筛选库中任何化合物的一个重要特征，然而很难确定。基于我们相信这个概念隐含在工作化学家的集体经验中，我们设计了一个数据集来捕捉人类对这个特征和第二个关键特征合成容易性的直观理解。五位化学家给3980种不同的化合物分配了一对分数，每对分数分别对应于药物相似性和合成的容易性。使用这个数据集，我们设计了一个人工神经网络和支持向量机的二进制分类器。发现这些模型有效地消除了不是药物样和/或难以合成的衍生物的化合物，证明了这些模型用作化合物采集过滤器的适用性。
The concept of drug-likeness, an important characteristic for any compound in a screening library, is nevertheless difficult to pin down. Based on our belief that this concept is implicit within the collective experience of working chemists, we devised a data set to capture an intuitive human understanding of both this characteristic and ease of synthesis, a second key characteristic. Five chemists assigned a pair of scores to each of 3980 diverse compounds, with the component scores of each pair corresponding to drug-likeness and ease of synthesis, respectively. Using this data set, we devised binary classifiers with an artificial neural network and a support vector machine. These models were found to efficiently eliminate compounds that are not drug-like and/or hard-to-synthesize derivatives, demonstrating the suitability of these models for use as compound acquisition filters.