化合物文库对细胞色素P450 3A4抑制能力的快速虚拟筛选过滤器

J. Zuegge, U. Fechner, O. Roche, N. Parrott, O. Engkvist, G. Schneider
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引用次数: 39

摘要

目前的虚拟筛选应用不仅关注生物活性,还关注候选药物的其他相关特性,如吸收、分布、代谢和排泄(ADME)。在首次虚拟筛选中,这些预测系统必须非常快,因为通常必须处理数百万种化合物。我们开发了一个基于pls的线性预测系统,用于细胞色素P450 3A4抑制引起的药物-药物相互作用的二元分类。从包含1152种化合物的原始数据集中精心挑选311种分子,使用ic50值对该系统进行训练。它正确预测了95%的训练数据和90%的半独立验证数据集。PLS模型由333个描述符编码一个分子计算得到。它优于利用三层前馈人工神经网络架构的方法。在单个微处理器上,预测每个分子所需的平均计算时间不到0.3秒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A fast virtual screening filter for cytochrome P450 3A4 inhibition liability of compound libraries
Current virtual screening applications focus not only on biological activity, but also on additional relevant properties of drug candidates, like absorption, distribution, metabolism, and excretion (ADME). In first-pass virtual screening, these prediction systems must be very fast because typically several millions of compounds must be processed. We have developed a linear PLS-based prediction system for binary classification of drug-drug interaction liability caused by cytochrome P450 3A4 inhibition. The system was trained using IC 5 0 values of 311 carefully selected molecules out of a raw data set containing 1152 compounds. It correctly predicts 95% of the training data and 90% of a semi-independent validation data set. The PLS model was calculated from 333 descriptors encoding a molecule. It outperforms an approach utilizing a three layered feed-forward artificial neural network architecture. The average calculation time required for a prediction is less than 0.3 seconds per molecule on a single microprocessor.
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