In the current study, we have developed predictive quantitative structure–activity relationship (QSAR) models for cellular response (foetal rate lung fibroblast proliferation) and protein adsorption (fibrinogen adsorption (FA)) on the surface of tyrosine-derived biodegradable polymers designed for tissue engineering purpose using a dataset of 66 and 40 biodegradable polymers, respectively, employing two-dimensional molecular descriptors. Best four individual models have been selected for each of the endpoints. These models are developed using partial least squares regression with a unique combination of six and four descriptors for cellular response and protein adsorption, respectively. The generated models were strictly validated using internal and external metrics to determine the predictive ability and robustness of proposed models. Subsequently, the validated individual models for each response endpoints were used for the generation of ‘intelligent’ consensus models (http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) to improve the quality of predictions for the external data set. These models may help in prediction of virtual polymer libraries for rational design/optimization for properties relevant to biomedical applications prior to their synthesis.

在当前的研究中，我们已经开发了预测的定量结构-活性关系（QSAR）模型，用于设计用于组织的酪氨酸衍生的可生物降解聚合物表面上的细胞反应（以速率计的肺成纤维细胞增殖）和蛋白质吸附（纤维蛋白原吸附（FA））。使用二维分子描述子分别使用66和40种可生物降解的聚合物的数据集实现工程目的。已为每个端点选择了最佳的四个独立模型。这些模型是使用偏最小二乘回归法开发的，分别具有细胞反应和蛋白质吸附的六个和四个描述符的独特组合。使用内部和外部指标严格验证生成的模型，以确定所提出模型的预测能力和鲁棒性。随后，将用于每个响应端点的经过验证的单个模型用于生成“智能”共识模型（http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/），以提高外部数据集的预测质量。这些模型可以帮助预测虚拟聚合物库，以便在合成之前对与生物医学应用相关的特性进行合理的设计/优化。