| 摘要: |
| 新药研发是一个系统工程，周期长、成功率低。传统药物研发在于发现疾病相关的有效靶点，借助各种技术进行小分子（或大分子）的筛选与设计。人工智能技术在医学多个领域已取得显著进展，其在新药研发领域能整合大量高通量组学数据、网络药理学数据和图像等高维表型数据，进行有效靶点的筛选和药物设计，节省药物研发成本，缩短药物研发时间。本文探讨了在新一代人工智能技术驱动下的药物发现过程，旨在为新药研发提供参考。 |
| 关键词: 人工智能 新药 药物研发 靶点 表型 |
| DOI：10.16781/j.0258-879x.2018.08.0869 |
| 投稿时间：2018-06-21修订日期：2018-08-01 |
| 基金项目: |
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| Artificial intelligence and drug discovery |
| LIU Qi* |
(Department of Bioinformatics, School of Life Science and Technology, the Tenth People's Hospital, Tongji University, Shanghai 200092, China
*Corresponding author) |
| Abstract: |
| The discovery of new drugs is a systematic project with long cycle and low success rate. The traditional drug discovery is to find effective targets related to diseases, and then to screen and design effective small molecules (or large molecules) using various technologies. Artificial intelligence technology has made significant progress in the medical field. In the field of new drug discovery, artificial intelligence technology can integrate a large number of high-dimensional phenotype data, including high-throughput omics data, network pharmacology data and images, so as to effectively screen therapeutic targets and design drugs, saving the costs of drug discovery and shortening the time required for drug discovery. In this article, we explored the drug discovery process driven by new generation of artificial intelligence technology, hoping to provide a reference for the development of new drugs. |
| Key words: artificial intelligence new drug drug discovery target phenotype |
