针对性多药学的AI:药物发现的下一个前沿
Anna Cichońska1, Balaguru Ravikumar1, Rayees Rahman1
1Harmonic Discovery Inc., New York, United States.
Current opinion in structural biology
|January 12, 2024
概括
人工智能 (AI) 正在通过实现向多药学来彻底改变药物发现,这涉及设计单一药物来打击复杂疾病的多个目标,提高疗效和减少副作用.
科学领域:
- 药物的发现和开发.
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
背景情况:
- 针对性多药理学,使用单一药物对多个分子标,是治疗复杂疾病的关键策略.
- 从历史上看,多药理学是偶然的;人工智能现在为药物设计提供了一种系统的方法.
- 人工智能通过预测多目标相互作用来提高药物疗效和降低毒性.
研究的目的:
- 审查人工智能 (AI) 在有针对性的多药学中的应用.
- 探索AI在识别协同协同的共同目标和区分它们与反目标中的作用.
- 讨论人工智能驱动的多目标疗法药物设计方面的进展.
主要方法:
- 利用机器学习和深度学习进行蛋白质结构建模和新型化合物生成.
- 利用人工智能解码多药物效应并预测化合物相互作用.
- 审查人工智能支持的对接,生成化学和蛋白质化学建模.
主要成果:
- 人工智能有助于系统和预测性的多目标药物设计.
- 人工智能有助于识别对治疗疗效至关重要的协同目标.
- 人工智能有助于划分对负面影响负责的反目标.
结论:
- 人工智能正在将多药理学从偶然的发现转变为预测科学.
- 人工智能驱动的方法有望为复杂疾病提供更有效,更安全的多向药物.
- 未来的研究应该解决人工智能支持的多药理学方面的挑战.
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