基于图形的药物设计的生成人工智能:最近的进展和前进的道路
1Aalto University and YaiYai Ltd, Finland.
Current opinion in structural biology
|January 10, 2024
概括
人工智能 (AI) 生成模型提供了一种有希望的方法,通过导航广的分子空间来加速药物发现. 这篇评论探讨了分子图形生成和未来方向的最先进的人工智能.
科学领域:
- 药物的发现和开发.
- 计算化学是一种计算化学.
- 医学中的人工智能
背景情况:
- 药物发现是一个复杂的过程,因分子搜索空间的巨大规模而受到挑战.
- 识别有前途的候选药物需要创新的计算方法.
- 人工智能 (AI) 为简化药物设计管道提供了一个潜在的解决方案.
研究的目的:
- 对分子图表表示的AI驱动生成模型的当前最先进状态进行审查.
- 确定现有的AI药物设计方法的局限性.
- 概述未来的研究方向,利用人工智能发现新药候选药物.
主要方法:
- 复习近期应用到分子图形的生成模型的进展.
- 从分子数据结构中学习方法的分析.
- 探索人工智能技术,以导航离散和广的化学空间.
主要成果:
- 在分子图表上运行的生成模型显示了药物设计的重大前景.
- 目前的方法在有效地探索整个分子搜索空间方面面临局限性.
- 人工智能为克服识别新型治疗分子的传统挑战提供了一条途径.
结论:
- 人工智能驱动的生成模型对于推动药物发现至关重要.
- 需要进一步的研究来解决当前人工智能方法的局限性.
- 利用人工智能的潜力可以显著加快有效药物候选者的识别.
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