A fast virtual screening filter for cytochrome P450 3A4 inhibition liability of compound libraries

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

Abstract

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.
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
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。
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