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首页> 外文期刊>International journal of computational biology and drug design >In-silico study of computational modelling and GLP-1 receptor inverse agonist compounds on a cancer cell line inhibitory bioassay dataset
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In-silico study of computational modelling and GLP-1 receptor inverse agonist compounds on a cancer cell line inhibitory bioassay dataset

机译：在癌细胞系抑制性生物测定数据集上进行计算机建模和GLP-1受体反向激动剂化合物的计算机模拟研究

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摘要

The introduction of technologies such as high-throughput screening (HTS) along with combinatorial chemistry has resulted in vast amounts of data generated from various experimental results. The existing automation technology such as machine learning tends to overcome the flaws of traditional mechanisms; hence, traditional analysis techniques tend to be inefficient, time consuming and costly when dealing with the complexity of large data. The objective of target-based modelling is prediction of the activity and relationships among different compounds from a large database with unknown activity and thus reducing the cost and time for discovery and development of a new drug. To deal with current objectives, we have discussed the comparative study for different machine learning algorithms such as naive Bayes, random forest, sequential minimal optimisation (SMO) and J48 for generating predictive models. The approach was extended and evaluated to measure the accuracy of targeted models with statistical techniques to increase accuracy.
机译：高通量筛选（HTS）以及组合化学等技术的引入，导致了来自各种实验结果的大量数据。诸如机器学习之类的现有自动化技术趋于克服传统机制的缺陷。因此，传统的分析技术在处理大数据的复杂性时往往效率低下，耗时且昂贵。基于靶标的建模的目的是从具有未知活性的大型数据库中预测不同化合物之间的活性和关系，从而减少发现和开发新药的成本和时间。为了满足当前的目标，我们讨论了针对不同机器学习算法（例如朴素贝叶斯，随机森林，顺序最小优化（SMO）和用于生成预测模型的J48）的比较研究。该方法得到了扩展和评估，以使用统计技术来提高目标模型的准确性。

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