协同药物研发中的关键数学问题

It is not very easy for the development of new drugs due to the huge investment from scientific research, expensive clinical trials and 8 to 10 years of test cycle. In 2000 the United States attacked AIDS with a 'Cocktail' Therapy and opened a new pattern of developing new drugs-- synergistic drug combinations. Now, it become an important research direction with its many advantages such as its available to treat complex diseases controlled by many factors, efficacy enhancement, low cost, many alternative drugs and insignificant or no side effect and so on. Mathematics would be significant in the development of synergistic drug such as theory predicts and mechanism revealing. This project will develop new methods and algorithms or models to forecast new synergistic drug combinations and to reveal their mechanism by integrating known synergistic drug combination, drug-target interaction, and drug chemical structure on a large scale, It is anticipated that models developed here don’t need to implement biological experiments for potential synergistic combination prediction, only information from databases and literatures is needed. This project will be mainly conducted by holding a series of seminars, workshops so that we can refine the key mathematical problems in the study of synergistic drugs.

由于前期研发的巨额科研投入、昂贵的临床实验和长达8-10年的试验周期，新药的研发非常不容易。2000年美国诞生了一种新药开发的模式--“协同药物组合式”新型治疗剂。协同药物以其能够针对多因素控制的复杂疾病、功效增强、毒副作用小、研发成本低以及备选药物多等优势而成为新药研发的重要发展方向。从数学方面对协同药物的研发具有重要的理论意义和实际指导意义。本项目将主要基于高通量数据和科研文献，利用数学的方法和理论，结合已知的多种信息（如：药物组合信息、药物靶点信息和药物化学结构信息等）期望建立数学模型可以不依赖于生物实验来进行协同药物的前期预测以及协同机制的研究，提炼出适用的协同药物的前期预测以及理解协同机制的方法和算法，主要研究形式是召开研讨会、组织调研等，凝练出协同药物研发中的关键数学问题。

本项目将主要基于高通量数据和科研文献，利用数学的方法和理论，开展协同药物的预测结果研究。针对抗真菌药物，通过结合已知的多种信息（如：药物组合信息、药物靶点信息和药物化学结构信息等），我们建立了世界上第一个抗真菌组合药物的数据库，利用数学模型给出了抗真菌协同药物组合的可能排序，并有超过50%的预测结果得到了生物实验的有力验证，结果已经获得国家发明专利；还撰写了数篇论文。该研究的部分工作获得国际同行高度评价。