人工智能算法包含機(jī)器學(xué)習(xí)算法和深度學(xué)習(xí)算法，可應(yīng)用于藥物靶標(biāo)發(fā)現(xiàn)、先導(dǎo)化合物的發(fā)現(xiàn)與優(yōu)化、候選藥物的確定、成藥性優(yōu)化。人工智能算法通過(guò)豐富的大數(shù)據(jù)系統(tǒng)學(xué)習(xí)可以實(shí)現(xiàn)模型的建立和高通量虛擬計(jì)算，應(yīng)用于藥物研發(fā)中能夠在一定程度上縮短研發(fā)周期、降低投入成本，進(jìn)而提高研發(fā)成功率。對(duì)機(jī)器學(xué)習(xí)算法、深度學(xué)習(xí)算法應(yīng)用于藥物研發(fā)中的研究進(jìn)展進(jìn)行闡述，以期為人工智能技術(shù)與藥物研發(fā)相結(jié)合的進(jìn)一步發(fā)展提供參考。

Artificial intelligence algorithms include machine learning algorithms and deep learning algorithms, which can be applied to discover drug targets, discover and optimize lead compounds, determine candidate drugs, and optimize druggability. Artificial intelligence algorithms can achieve model building and high-throughput virtual computing through complex big data system learning. When applied in drug research and development, it can shorten the research and development cycle to a certain extent, reduce input costs, and thereby improve the success rate of research and development. In this article, the research progress of machine learning algorithms and deep learning algorithms applied in drug development was reviewed, in order to provide reference for the further development of the combination of artificial intelligence technology and drug development.

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國(guó)家自然科學(xué)基金項(xiàng)目資助（82104012，82202950，82303681）