An Integrative Approach to Drug Repositioning Using Decision Tree Based Machine Learning

项目总结/摘要 尽管最近在生命科学和技术方面取得了进展，但用于开发 单一药物仍然非常昂贵。总体效率导致药物开发 保持不变，平均成本为26亿美元，开发一种药物需要15年。考虑 面对这些挑战，对药物重新定位的需求增加，其中新的适应症 发现现有或未经批准的药物。在这里，我们介绍一种方法，只整合药物 相似性度量，如副作用、结构和靶相似性，以识别新的适应症， 毒品通过专注于药物相似性度量，我们提出的方法允许应用于 孤儿分子目前没有主要适应症。为了改进目前的方法， 药物再利用，我们建议开发一种利用多种数据的计算方法， 类型，以便预测药物可能治疗的适应症。基于 类似药物用于类似适应症的观察结果，该方法公开使用 现有的数据库，以确定药物之间的关联，并整合药物相似性数据， 以及药物靶向特定信息，纳入机器学习框架，以便准确地 预测这些药物的适应症。总之，我们的方法提供了一种新颖的，广泛适用的 可以识别新适应症的策略，允许加速和更有效的方法， 未来的药物开发和重新定位工作。

PROJECT SUMMARY/ABSTRACT Despite recent advances in life sciences and technology, the amount of money spent developing a single drug has stayed drastically expensive. Overall efficiencies have caused drug development to stay the same, with an average cost of $2.6 billion and 15 years to develop a single drug. Considering these challenges, there is an increased need for drug repositioning, in which new indications are found for existing or unapproved drugs. Here we introduce an approach that integrates only drug similarity metrics, such as side effect, structure, and target similarities, to identify novel indications for drugs. By focusing on drug similarity metrics, our proposed method allows for applications towards orphan molecules that presently have no primary indication. To improve upon the current methods of drug repurposing, we propose the developing of a computational approach that utilizes multiple data types within a machine-learning framework in order to predict indications a drug may treat. Based on the observations that similar drugs are used for similar indications, this method utilizes publicly available databases to identify associations between drugs, and integrates drug similarity data, as well as drug-target specific information, into a machine-learning framework in order to accurately predict indications for these drugs. Altogether, our method provides a novel, broadly applicable strategy that can identify novel indications, allowing for an accelerated and more efficient method for future drug development and repositioning efforts.