一个全面的框架,用于光学化学分子识别.
Yan Wang1, Ruochi Zhang2, Shengde Zhang3
1Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China; School of Artificial Intelligence, Jilin University, Changchun, 130012, China; College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China.
Computers in biology and medicine
|July 2, 2023
概括
光学化学分子识别 (OCMR) 通过增强化学结构识别来改善人工智能驱动的药物发现. 这种新的框架整合了分子图形拓,以便在复杂的场景中更好地提取数据.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
背景情况:
- 人工智能 (AI) 在药物发现中越来越重要,它有助于从目标识别到化学结构识别的任务.
- 目前用于化学结构识别的AI模型在处理复杂或非标准的分子表示方面存在局限性.
研究的目的:
- 引入光学化学分子识别 (OCMR) 框架,用于先进的化学结构识别.
- 增强实际药物发现设置中的数据提取能力.
主要方法:
- 开发了OCMR框架,整合了分子图形拓学的本地信息.
- 将OCMR性能与基于规则和端到端深度学习模型进行比较.
- 在公共基准和内部数据集上对OCMR进行评估.
主要成果:
- 与现有方法相比,OCMR在化学结构识别方面表现优越.
- 该框架有效地处理复杂的任务,如非正规图纸和原子组缩写.
- 在多个数据集上实现了与最新技术相比的显著改进.
结论:
- 在药物发现中,OCMR框架为化学结构识别提供了一个强大的解决方案.
- 整合分子图形拓学可以提高复杂化学数据的AI模型性能.
- OCMR代表了人工智能驱动的药物发现管道的重大进步.
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