人工智能（AI）和機器學習不僅使藥物發(fā)現(xiàn)和開發(fā)實現(xiàn)了質(zhì)的飛躍，而且?guī)椭幬镩_發(fā)進程進入現(xiàn)代化。機器學習和深度學習算法已應用于藥物發(fā)現(xiàn)各個階段，如先導化合物的篩選、多肽合成及小分子藥物的發(fā)現(xiàn)、最佳給藥劑量的確定、類藥化合物的設計和藥物不良反應的預測、蛋白質(zhì)間相互作用的預測、虛擬篩選效率的提高、定量構(gòu)效關系（QSAR）建模和藥物重新定位、理化性質(zhì)和藥物靶標親和力的預測、化合物的結(jié)合預測和體內(nèi)安全性分析、多靶點配體藥物分子的設計以及臨床試驗的設計。簡要綜述了AI算法和傳統(tǒng)化學相結(jié)合以提高藥物發(fā)現(xiàn)的效率以及AI在藥物發(fā)現(xiàn)過程中的應用研究進展，以期為AI應用于藥物發(fā)現(xiàn)提供一定參考。

Artificial intelligence (AI) and machine learning (ML) not only has made a qualitative leap in drug discovery and development, but also has helped the process of drug development enter modernization. Algorithms of ML and deep learning (DL) have been applied to various phases of drug discovery, such as screening of lead compounds, peptide synthesis and discovery of small molecular drugs, determination of the optimal dosage, design of drug-like compounds and prediction of adverse drug reaction (ADR), prediction of protein-protein interaction, improvement of the efficiency of virtual screening, quantitative structure-activity relationship (QSAR) modeling and drug repositioning, prediction of physicochemical properties and affinity of drug targets, prediction of drug binding and in vivo safety analysis, design of multiple target ligand drug molecules, as well as design of clinical trials. The research progress in the combination of AI algorithm and traditional chemistry to improve the efficiency of drug discovery and application of AI during the process of drug discovery was briefly reviewed, in order to provide some references for using AI for drug discovery in China.

中國食品藥品檢定研究院學科帶頭人課題（2021X2）