Kanji Rakesh, Sharma Abhinav, Bagler Ganesh
Indian Institute of Technology Jodhpur, Ratanada, Jodhpur, India.
Mol Biosyst. 2015 Nov;11(11):2900-6. doi: 10.1039/c5mb00312a.
Despite technological progresses and improved understanding of biological systems, discovery of novel drugs is an inefficient, arduous and expensive process. Research and development cost of drugs is unreasonably high, largely attributed to the high attrition rate of candidate drugs due to adverse drug reactions. Computational methods for accurate prediction of drug side effects, rooted in empirical data of drugs, have the potential to enhance the efficacy of the drug discovery process. Identification of features critical for specifying side effects would facilitate efficient computational procedures for their prediction. We devised a generalized ordinary canonical correlation model for prediction of drug side effects based on their chemical properties as well as their target profiles. While the former is based on 2D and 3D chemical features, the latter enumerates a systems-level property of drugs. We find that the model incorporating chemical features outperforms that incorporating target profiles. Furthermore we identified the 2D and 3D chemical properties that yield best results, thereby implying their relevance in specifying adverse drug reactions.
尽管技术不断进步，对生物系统的理解也有所改善，但新型药物的发现仍是一个低效、艰巨且昂贵的过程。药物的研发成本高得不合理，这在很大程度上归因于候选药物因药物不良反应而具有的高淘汰率。基于药物经验数据的准确预测药物副作用的计算方法，有可能提高药物发现过程的效率。识别对确定副作用至关重要的特征将有助于进行高效的计算程序来预测副作用。我们设计了一种广义的普通典型相关模型，用于基于药物的化学性质及其靶点特征来预测药物副作用。前者基于二维和三维化学特征，而后者列举了药物的系统级属性。我们发现纳入化学特征的模型优于纳入靶点特征的模型。此外，我们确定了能产生最佳结果的二维和三维化学性质，从而暗示了它们在确定药物不良反应方面的相关性。
