In silico toxicity prediction by support vector machine and SMILES representation-based string kernel
In silico toxicity prediction by support vector machine and SMILES representation-based string kernel
复制标题
通过支持向量机和基于 SMILES 表示的字符串内核进行计算机毒性预测
DOI：
10.1080/1062936x.2011.645874
复制
发表时间：
2012-01-01
影响因子：
3
通讯作者：
Liang, Y. -Z.
中科院分区：
文献类型：
--
作者：
Cao, D. -S.;Zhao, J. -C.;Liang, Y. -Z.
非常需要评估人类接触的化学品的有害影响或毒性。在本文中，使用基于简化分子输入行输入规范（SMILES）表示的字符串内核以及最先进的支持向量机（SVM）算法，对美国环境保护局分布式结构可搜索毒性（DSSTox）数据库网络中的化学品的毒性进行分类。在这种方法中，分子结构可以直接由一系列SMILES子串编码，这些子串代表分子中某些化学元素和不同种类的化学键（双键、三键和立体化学键）的存在。因此，SMILES字符串核可以通过隐藏在分子中的一系列局部信息来准确、直接地测量分子的相似性。使用两种模型验证方法（五倍交叉验证和独立验证集）来评估我们开发的模型的预测能力。获得的结果表明，基于 SMILES 字符串核的 SVM 可以被视为化学品潜在毒性预测的一种非常有前途的替代建模方法。
There is a great need to assess the harmful effects or toxicities of chemicals to which man is exposed. In the present paper, the simplified molecular input line entry specification (SMILES) representation-based string kernel, together with the state-of-the-art support vector machine (SVM) algorithm, were used to classify the toxicity of chemicals from the US Environmental Protection Agency Distributed Structure-Searchable Toxicity (DSSTox) database network. In this method, the molecular structure can be directly encoded by a series of SMILES substrings that represent the presence of some chemical elements and different kinds of chemical bonds (double, triple and stereochemistry) in the molecules. Thus, SMILES string kernel can accurately and directly measure the similarities of molecules by a series of local information hidden in the molecules. Two model validation approaches, five-fold cross-validation and independent validation set, were used for assessing the predictive capability of our developed models. The results obtained indicate that SVM based on the SMILES string kernel can be regarded as a very promising and alternative modelling approach for potential toxicity prediction of chemicals.