近年来，随着系统生物学的快速发展，网络药理学出现并迅速成为新的药物发现模式。各种基于网络的系统性分析和预测方法，使得人们可以快速、有效地针对癌症等复杂疾病，构建预测药物－靶标（基因）－疾病多重网络关系，发现疾病的发生机制，为相关药物发现工作提供新思路。本课题将针对癌症这一复杂疾病，尤其是乳腺癌和肺癌，进行以下几个方面的研究：首先从开放数据库和文献中收集、整合多层次生物医学数据，发展系统性分析方法，构建基因－癌症关系网络，阐明癌症的发生机制，预测潜在的抗癌新靶标；然后开发基于网络的药物－靶标相互作用预测方法，改进现有方法的不足，编制单机软件并构建在线预测服务系统；同时挖掘针对癌症靶标的关键化学子结构片段，进行基于片段的药物设计；最后利用我们发展的方法，发现具有潜在抗癌活性的化合物，进行生物活性测试，阐明其作用机制，并根据实验验证结果进一步优化预测方法。

As the rapid development of systems biology in recent years, network pharmacology has emerged as a new paradigm of drug discovery. Various network-based methods for systematic analysis and prediction have made it possible to construct and predict drug-target (gene)-disease multi-layered networks for complex diseases such as cancer, rapidly and effectively, which provides a new avenue for the drug discovery of related diseases. In this project, the following aspects will be studied for cancer, especially breast cancer and lung cancer. At first, multi-scale experimental data of biology and medicine will be collected and integrated from open access databases and recent publications, to develop systematic analysis methods in order to construct gene-cancer association networks, elucidate the pathogenesis of cancer, and predict new potential anti-cancer targets. Then, new network-based methods for the prediction of drug-target interactions will be developed to improve existing methods. The developed methods will be programmed as a stand-alone software, and integrated into a web server free available online. Meanwhile, fragment-based drug design will be implemented using crucial chemical substructure fragments. At last, biological assays will be performed for new potential anti-cancer compounds predicted via these computational methods, and the mechanisms of action of the active compounds will be investigated. In comparison with the prediction results, the experimental results will be used to improve the methods.