Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions.
Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions.
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DOI：
10.1093/bib/bbab476
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发表时间：
2022-01-17
影响因子：
9.5
通讯作者：
Cheng J
中科院分区：
文献类型：
--
作者：
Dhakal A;McKay C;Tanner JJ;Cheng J
从目标识别到营销批准的新药生产需要12年的成本，而COVID-19的大流行病已经迫切需要在这里进行更强大的计算方法。为了在药物发现的背景下预测蛋白质 - 配体的相互作用，以人工智能（AI）进行方法，我们以蛋白质（靶标），配体（例如药物）及其相互作用进行简要介绍最终，我们在蛋白质 - 配体相互作用的结构域中使用的数据库进行了调查和分析机器学习（ML）方法，以预测蛋白质 - 配体结合位点，配体结合亲和力（构象）算法和最近的深度学习方法。研究蛋白质 - 配体相互作用的策略。
New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. Here, we review the computational approaches to predicting protein–ligand interactions in the context of drug discovery, focusing on methods using artificial intelligence (AI). We begin with a brief introduction to proteins (targets), ligands (e.g. drugs) and their interactions for nonexperts. Next, we review databases that are commonly used in the domain of protein–ligand interactions. Finally, we survey and analyze the machine learning (ML) approaches implemented to predict protein–ligand binding sites, ligand-binding affinity and binding pose (conformation) including both classical ML algorithms and recent deep learning methods. After exploring the correlation between these three aspects of protein–ligand interaction, it has been proposed that they should be studied in unison. We anticipate that our review will aid exploration and development of more accurate ML-based prediction strategies for studying protein–ligand interactions.
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