机器学习在抗病毒候选物的临床前开发中:对当前景观的回顾
Hannah Hargrove1, Bei Tong2, Amr Hussein Elkabanny3
1Department of Chemical and Biomolecular Engineering, University of Massachusetts Amherst, Amherst, MA 01003, USA.
Viruses
|February 27, 2026
概括
机器学习 (ML) 通过减少选新药候选者的成本和时间来加速抗病毒 (AVP) 设计. 这种计算方法提高了安全性,并为AVP发现探索了更广泛的化学空间.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 传统的抗病毒 (AVP) 查是耗时且昂贵的.
- 目标识别和临床前测试带来了重大后勤挑战.
研究的目的:
- 审查机器学习 (ML) 在早期AVP设计中的当前应用.
- 讨论ML在AVP开发中的未来发展轨迹.
主要方法:
- 利用机器学习进行潜在的AVP相互作用的in silico选.
- 使用ML来生成和验证新的AVP候选人.
- 将基于ML的选与传统的高通量选方法进行比较.
主要成果:
- ML显著降低了AVP查所需的财务成本和时间.
- 与传统方法相比,ML可以探索更多样化的化学空间.
- 在 silico 验证将研究人员暴露于危险病毒的风险降至最低,提高了安全性.
结论:
- 机器学习是加速抗病毒发现的变革性工具.
- ML为传统的AVP查方法提供了一个具有成本效益,安全性和效率的替代方案.
- 机器学习的整合对于未来AVP设计和开发的进步至关重要.
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