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2D MI-DRAGON: A new predictor for protein–ligands interactions and theoretic-experimental studies of US FDA drug-target network, oxoisoaporphine inhibitors for MAO-A and human parasite proteins

数量结构-活动关系 水准点(测量) 人工神经网络 分子描述符 人工智能 感知器 灵敏度(控制系统) 机器学习 软件 多层感知器 计算机科学 药品 药物发现 化学 计算生物学 生物化学 生物 药理学 工程类 大地测量学 电子工程 程序设计语言 地理
作者
Francisco Prado-Prado,Xerardo García‐Mera,Manuel Escobar,Eduardo Sobarzo‐Sánchez,Matilde Yáñez,Pablo Riera-Fernández,Humberto González‐Díaz
出处
期刊:European journal of medicinal chemistry [Elsevier]
卷期号:46 (12): 5838-5851 被引量:49
标识
DOI:10.1016/j.ejmech.2011.09.045
摘要

There are many pairs of possible Drug–Proteins Interactions that may take place or not (DPIs/nDPIs) between drugs with high affinity/non-affinity for different proteins. This fact makes expensive in terms of time and resources, for instance, the determination of all possible ligands–protein interactions for a single drug. In this sense, we can use Quantitative Structure–Activity Relationships (QSAR) models to carry out rational DPIs prediction. Unfortunately, almost all QSAR models predict activity against only one target. To solve this problem we can develop multi-target QSAR (mt-QSAR) models. In this work, we introduce the technique 2D MI-DRAGON a new predictor for DPIs based on two different well-known software. We use the software MARCH-INSIDE (MI) to calculate 3D structural parameters for targets and the software DRAGON was used to calculated 2D molecular descriptors all drugs showing known DPIs present in the Drug Bank (US FDA benchmark dataset). Both classes of parameters were used as input of different Artificial Neural Network (ANN) algorithms to seek an accurate non-linear mt-QSAR predictor. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 21:21-31-1:1. This MLP classifies correctly 303 out of 339 DPIs (Sensitivity = 89.38%) and 480 out of 510 nDPIs (Specificity = 94.12%), corresponding to training Accuracy = 92.23%. The validation of the model was carried out by means of external predicting series with Sensitivity = 92.18% (625/678 DPIs; Specificity = 90.12% (730/780 nDPIs) and Accuracy = 91.06%. 2D MI-DRAGON offers a good opportunity for fast-track calculation of all possible DPIs of one drug enabling us to re-construct large drug-target or DPIs Complex Networks (CNs). For instance, we reconstructed the CN of the US FDA benchmark dataset with 855 nodes 519 drugs + 336 targets). We predicted CN with similar topology (observed and predicted values of average distance are equal to 6.7 vs. 6.6). These CNs can be used to explore large DPIs databases in order to discover both new drugs and/or targets. Finally, we illustrated in one theoretic-experimental study the practical use of 2D MI-DRAGON. We reported the prediction, synthesis, and pharmacological assay of 10 different oxoisoaporphines with MAO-A inhibitory activity. The more active compound OXO5 presented IC50 = 0.00083 μM, notably better than the control drug Clorgyline.
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