Predicting fibrinogen adsorption to polymeric surfaces in silico: a combined method approach
Predicting fibrinogen adsorption to polymeric surfaces in silico: a combined method approach
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DOI：
10.1016/j.polymer.2005.03.012
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发表时间：
2005-05-26
期刊：
影响因子：
4.6
通讯作者：
Welsh, WJ
中科院分区：
文献类型：
--
作者：
Smith, JR;Kholodovych, V;Welsh, WJ
我们提出了一种改进的半经验（代理）模型预测纤维蛋白原吸附到聚合物表面的组合库。维斯我们以前的方法，该模型最重要的新功能是，它准确地预测纤维蛋白原吸附到一组20个聚合物的基础上，他们的结构单独，即不使用任何实验数据，这20个聚合物。这意味着模型预测可以在合成这些结构之前生成，并且它们的吸附亲和力可以完全通过计算机进行评估。建模是通过结合几个更传统的计算方法在一个“混合”的方法。该技术是用来系统地消除实验输入（空气-水接触角和玻璃化转变温度）从现有的人工神经网络模型，有利于输入数学上来自聚合物结构的二维表示。我们使用偏最小二乘（PLS）回归来选择基于结构的分子描述符，随后用于生成计算模型，用于随后通过人工神经网络（ANN）预测蛋白质吸附。该模型提供了准确的预测纤维蛋白原吸附到聚合物表面仅使用吸附数据从一个小的代表性子集的聚合物库。这项工作是朝着生成虚拟聚合物库的目标迈出的重要一步，用于生物医学应用相关性能的合理设计/优化。(c)2005爱思唯尔有限公司保留所有权利。
We present an improved semi-empirical (surrogate) model for the prediction of fibrinogen adsorption to the surfaces of polymers in a combinatorial library. The most important novel features of this model vis a vis our previous method is that it accurately predicts fibrinogen adsorption to a group of 20 polymers based on their structure alone, i.e. without using any experimental data for these 20 polymers. This implies that the model predictions can be generated prior to synthesis of these structures and their adsorption affinities can be evaluated entirely in silico. Modeling is accomplished by combining several more traditional computational methods in a 'hybrid' approach. The technique is used to systematically eliminate experimental inputs (air-water contact angle and glass transition temperature) from an existing artificial neural network model in favor of inputs that are derived mathematically from two-dimensional representations of polymer structure. We use partial least squares (PLS) regression to select structure-based molecular descriptors that are subsequently used to generate computational models for the subsequent prediction of protein adsorption by artificial neural networks (ANNs). The model provides accurate predictions of fibrinogen adsorption to polymeric surfaces using only adsorption data from a small representative subset of the polymer library. This work represents a major step toward the goal of generating virtual polymer libraries for rational design/optimization for properties relevant to biomedical applications. (c) 2005 Elsevier Ltd. All rights reserved.