药物发现:In silico干数据可以绕过生物湿数据?
Massimiliano Tognolini1, Alessio Lodola1, Carmine Giorgio1
1Department of Food and Drug, University of Parma, Parma, Italy.
British journal of pharmacology
|October 23, 2023
概括
人工智能在药物发现中的兴起可能会忽视重要的生物数据. 成功的药物开发需要整合计算机辅助药物设计 (CADD) 和实验 (湿) 数据.
科学领域:
- 计算化学和药理学计算化学和药理学
- 药物的发现和开发.
- 生物信息学和人工智能的人工智能
背景情况:
- 计算能力和人工智能算法的快速进步促进了计算机辅助药物设计 (CADD).
- 越来越多的科学家正在利用CADD进行药物发现,潜在的经验有限.
- 由于CADD工具的可访问性,可能导致过度依赖in silico方法.
研究的目的:
- 检查药物发现中过度强调计算方法的潜在风险.
- 突出实验 (湿) 数据在in silico方法之外的不可或缺的作用.
- 强调在临床前药物开发中计算和生物数据之间的协同关系.
主要方法:
- 审查和分析计算机辅助药物设计 (CADD) 的当前趋势.
- 讨论仅仅依赖in silico数据的局限性.
- 探索展示CADD和实验数据的整合的案例研究.
主要成果:
- "干燥数据的黄金时代"可能会掩盖湿数据的关键重要性.
- 仅仅依赖于CADD可以导致不完整或不准确的候选药物识别.
- 成功的临床前药物发现需要平衡地整合in silico和生物数据.
结论:
- 药物发现计划必须平衡计算预测与实验验证.
- 在和湿数据之间的协同作用对于高效和有效的临床前研究至关重要.
- 未来的药物发现工作应该强调结合CADD和生物实验的综合方法.
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