Das Pranab, Mazumder Dilwar Hussain
Department of Computer Science and Engineering, National Institute of Technology Nagaland, Chumukedima, Dimapur, Nagaland 797103 India.
Artif Intell Rev. 2023 Feb 15:1-28. doi: 10.1007/s10462-023-10413-7.
Approved drugs for sale must be effective and safe, implying that the drug's advantages outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common reasons for drug failure that may halt the whole drug discovery pipeline. The side effects might vary from minor concerns like a runny nose to potentially life-threatening issues like liver damage, heart attack, and death. Therefore, predicting the side effects of the drug is vital in drug development, discovery, and design. Supervised machine learning-based side effects prediction task has recently received much attention since it reduces time, chemical waste, design complexity, risk of failure, and cost. The advancement of supervised learning approaches for predicting side effects have emerged as essential computational tools. Supervised machine learning technique provides early information on drug side effects to develop an effective drug based on drug properties. Still, there are several challenges to predicting drug side effects. Thus, a near-exhaustive survey is carried out in this paper on the use of supervised machine learning approaches employed in drug side effects prediction tasks in the past two decades. In addition, this paper also summarized the drug descriptor required for the side effects prediction task, commonly utilized drug properties sources, computational models, and their performances. Finally, the research gap, open problems, and challenges for the further supervised learning-based side effects prediction task have been discussed.
获批销售的药物必须有效且安全，这意味着药物的益处大于其已知的有害副作用。药物的副作用是导致药物研发失败的常见原因之一，可能会使整个药物研发流程停滞。副作用可能从诸如流鼻涕等小问题到像肝损伤、心脏病发作和死亡等潜在的危及生命的问题不等。因此，预测药物的副作用在药物开发、发现和设计中至关重要。基于监督式机器学习的副作用预测任务近来备受关注，因为它能减少时间、化学废物、设计复杂性、失败风险和成本。用于预测副作用的监督式学习方法的进步已成为重要的计算工具。监督式机器学习技术能提供有关药物副作用的早期信息，以便根据药物特性开发出有效的药物。然而，预测药物副作用仍存在若干挑战。因此，本文对过去二十年中用于药物副作用预测任务的监督式机器学习方法的使用情况进行了近乎详尽的调查。此外，本文还总结了副作用预测任务所需的药物描述符、常用的药物特性来源、计算模型及其性能。最后，讨论了基于监督式学习的副作用预测任务的研究差距、未解决的问题和挑战。
