药物发现的大数据:医药化学家的一些历史景观,考虑和应用
João A L de Lima1, Lucas Silva Franco1, Lídia M Lima1
1Institute of Biomedical Science, Federal University of Rio de Janeiro, Avenue Carlos Chagas Filho 373, CCS, Block B Room B14 Rio de Janeiro, Rio de Janeiro 21941-902, Brazil.
ACS omega
|February 16, 2026
概括
大数据 (BD) 通过整合各种数据集来实现目标识别和头优化,彻底改变了药物发现. 虽然加速了开发,但它的使用,与AI和LLMs一起,引发了关于数据质量和科学解释的关键问题.
科学领域:
- 生物医学科学 生物医学科学
- 计算化学计算化学
- 药理学 药理学是指药理学的学科.
背景情况:
- 大数据 (BD) 集成通过结合化学,生物和临床信息来改变药物发现.
- BD 增强了成功识别,生成和药物重新定位,减少了开发时间和成本.
研究的目的:
- 审查大数据 (BD) 在药物发现和开发的所有阶段的变革性影响.
- 探索BD如何帮助识别新药学标和开发个性化疗法.
- 讨论在科学研究中使用BD,AI和LLMs的哲学含义.
主要方法:
- 对大数据在药物发现中的应用进行文献综述.
- 综合基因组,蛋白质组和代谢组数据的分析,用于目标识别.
- 检查BD与组合化学,高通量选和AI的协同作用.
主要成果:
- BD加速了具有最佳药理动力学和药理动力学概况的化合物的识别.
- BD支持理性分子设计,预测建模和数据驱动的药物发现.
- 法律法规的兴起产生了需要对科学有效性进行批判性评估的数据.
结论:
- 大数据是现代药物发现的基石,它超越了连接体的发现,扩展到个性化医学.
- 整合BD,AI和LLMs需要就生物医学科学中的数据质量和应用进行对话.
- 确保有意义的数据解释对于协调人类和人工智能在药物开发中的贡献至关重要.
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