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深度学习在发现抗病毒和模拟剂方面:数据库和预测工具

Maryam Nawaz¹, Yao Huiyuan¹, Fahad Akhtar¹

¹School of Life Science and Technology, China Pharmaceutical University, Nanjing, 211100, People's Republic of China.

Molecular diversity

|March 28, 2025

概括

抗病毒 (AVP) 为传统治疗提供了一个有前途的替代方案. 机器学习通过分析数据库和预测有效性来加速它们的发现,尽管临床使用仍然存在挑战.

科学领域:

生物化学生化学
计算生物学计算生物学
药物发现药物发现药物发现

背景情况:

抗病毒 (AVP) 正在成为针对寨卡病毒,埃博拉病毒和SARS-CoV-2等病毒感染的强有力的治疗方法.
人工智能 (AI) 和数据可用性的进步正在推动AVP研究的快速进展.

研究的目的:

本综述探讨了AVP数据库的发展,它们的物理化学特性,以及基于机器学习的预测工具,用于AVP发现.
它强调了人工智能在推动AVP和皮相仿药的发展中的作用.

主要方法:

专注于专门的AVP数据库 (DRAVP,AVPdb,DBAASP) 和它们在AVP表征中的实用性.
讨论机器学习和深度学习在AVP的计算预测中的应用.
分析物理化学特性 (疏水性,螺旋结构) 与抗病毒功效之间的联系.

主要成果:

机器学习显著帮助AVP发现,利用专门的数据库.
物理化学性质,如疏水性和两性结构,对于AVP功能至关重要.
目前的预测工具面临的挑战包括有限的数据,过度配备和缺乏机械洞察力.

结论:

未来的研究应该优先考虑强大的验证,体内数据的整合和可解释的AI模型.
将预测能力扩展到多目标相互作用和复杂的生物系统中,对于AVP的临床转化至关重要.

关键词:

在AVP数据库中.抗病毒 (AVP) 是一种抗病毒.计算生物学是一种计算生物学.机器学习是机器学习.预测工具预测工具治疗开发的治疗方法.

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