从直觉到人工智能:在药物发现中小分子表示的演变
Miles McGibbon1, Steven Shave1, Jie Dong2
1Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, United Kingdom.
Briefings in bioinformatics
|November 30, 2023
概括
药物发现中的人工智能 (AI) 使用机器可读的分子表示来预测药物特性,旨在降低成本和失败率. 这些方法从简单的格式演变为复杂的学习表征,提高了新药候选者的识别.
科学领域:
- 化学信息学 化学信息学
- 人工智能在药物发现中的作用
- 计算化学的计算化学
背景情况:
- 药物发现的主要目标是有效地识别安全有效的候选药物.
- 机器可读的分子表示对于准确的属性预测和知情决策至关重要.
- 分子表示已经从人类可读的格式发展到复杂的学习表示.
研究的目的:
- 审查人工智能驱动药物发现中的分子表示的演变和当前景观.
- 突出基于序列和基于图形的分子表示的优缺点.
- 识别分子表示中创新的新机遇.
主要方法:
- 对化学信息学和药物发现中的分子表示现有文献的审查.
- 基于序列和基于图形的分子表示技术的分析.
- 讨论分子表示的关键性质:一般性,计算成本,可解释性和可逆性.
主要成果:
- 分子表示已经从简单的描述器过渡到复杂的学习模型.
- 基于序列和基于图表的表示方式很受欢迎,但具有明显的优势和局限性.
- 关键的挑战包括数据稀缺和需要可解释的模型.
结论:
- 分子表示的选择会影响药物发现的效率和成功.
- 未来的研究应该专注于用于低数据场景的新型表示,并整合更广泛的化学知识.
- 分子表示的进步对于下一代治疗方式至关重要.
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