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SynAI:一个人工智能驱动的癌症药物协同作用预测平台
Kuan Yan1, Runjun Jia1, Sheng Guo1
1Data Science and Bioinformatics, Crown Bioscience, Suzhou, Jiangsu Province 215000, P.R. China.
Bioinformatics advances
|November 29, 2023
概括
SynAI使用人工智能和化合物结构 (简化分子输入线输入系统序列) 预测药物协同作用. 这种方法可以降低药物开发成本,因为它可以在早期发现治疗价值.
科学领域:
- 计算化学和药理学计算化学和药理学
- 人工智能在药物发现中的作用
背景情况:
- 发现协同作用的药物组合对于有效的癌症治疗至关重要.
- 预测药物协同作用的传统方法昂贵且耗时.
研究的目的:
- 推出SynAI,一个人工智能驱动的平台,用于预测药物协同作用.
- 为了能够早期识别化合物组合的治疗潜力.
主要方法:
- SynAI使用基于简化分子输入线输入系统 (SMILE) 序列的in silico预测.
- 人工智能模型在广泛的数据集上进行了训练,包括NCI-Almanac和DrugCombDB,包括超过1,200,000次体外试验.
- 该平台预测了150个癌症细胞系中复合对的幸福分数.
主要成果:
- 在不需要化合物合成或结构分析的情况下,SynAI可以预测药物协同作用.
- 该平台在数据输入方面提供了灵活性,可以接受一个或两个复合SMILE序列.
- 性能与现有方法可比,具有更高的灵活性.
结论:
- SynAI显著降低了早期候选药物查的成本.
- 该平台为预测药物协同作用提供了灵活和高效的解决方案.
- 在线可以访问评估版本,以便更广泛地使用.
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