生物打印迎来了人工智能时代:为药物发现平台SAFIRE开发基于人工智能的ADMET模型
Sarah E Biehn1, Luis Miguel Goncalves1, Juerg Lehmann1
1Eurofins DiscoveryAI, Eurofins Panlabs, Inc., Saint Charles, MO 63304, USA.
Future medicinal chemistry
|February 19, 2024
概括
人工智能模型可以高准确地预测药物的特性,如吸收,分布,新陈代谢,分泌和毒性 (ADMET). 这种人工智能方法通过有效优先考虑有前途的化合物来增强药物发现.
科学领域:
- 计算化学是一种计算化学.
- 药物的发现和开发.
- 药理动力学和毒理学
背景情况:
- 预测吸收,分布,新陈代谢,分泌和毒性 (ADMET) 属性对于优先考虑候选药物至关重要.
- 人工智能 (AI) 为ADMET财产预测提供了一种快速有效的方法.
- SAFIRE平台利用人工智能来简化药物开发的早期阶段.
研究的目的:
- 开发和验证用于预测ADMET属性的AI模型.
- 评估在各种数据集上训练的AI模型的性能.
- 将预测性ADMET建模集成到一个用户友好的药物发现平台中.
主要方法:
- 使用专有BioPrint数据库数据和公共数据集训练AI模型.
- 预测小分子的多个ADMET终点.
- 使用准确性和马修的相关系数等既定指标验证模型性能.
主要成果:
- 在验证集中,SAFIRE AI 模型实现了超过 75% 的准确性和 0.4 的 Matthew 相对应系数.
- 结合专有和公共数据提高了模型性能,并扩大了化学空间覆盖范围.
- 该平台提供评分,以帮助用户在复合优先级的决策.
结论:
- 高质量,多样化的数据集对于开发强大的ADMET预测模型至关重要.
- 人工智能驱动的ADMET预测模型在加速药物发现管道方面具有显著的实用性.
- SAFIRE平台为研究人员提供了一种有价值的工具,通过提供可靠的复合物属性预测.
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