ProfhEX:通过基于机器学习的目标分析和责任预测来加强早期药物发现
Filippo Lunghini1, Carmen Cerchia2, Anna Fava1
1EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123 Naples, Italy.
Journal of chemical information and modeling
|December 8, 2025
概括
增强的ProfhEX平台使用人工智能 (AI) 加快药物发现,用于准确的化合物目标预测. 它提供了一个大规模的,用户友好的解决方案,以克服当前药物开发工具的局限性.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 药物的发现和开发.
背景情况:
- 药物发现是漫长的,昂贵的,并且由于安全性,有效性和监管问题而面临高的消耗率.
- 人工智能 (AI) 方法通过预测分子活性和识别药物点来加速药物发现.
- 现有的基于联体的预测工具在数据覆盖,目标范围和可用性方面存在局限性.
研究的目的:
- 介绍ProfhEX平台的增强版本,用于全面的药物向活动概况.
- 提高计算药物发现工具的可扩展性,用户友好性和预测准确性.
主要方法:
- 开发了一个增强的ProfhEX平台,用于693个人类目标,提供969个预测模型.
- 在超过500万个精选的生物活性数据点上训练模型.
- 在现实场景中评估预测准确度,并与最先进的工具进行比较.
主要成果:
- 增强的ProfhEX平台在前性研究中显示出高预测准确度.
- 在初级目标预测基准中,ProfhEX超过了现有的工具.
- 该平台提供全面的复合目标活动概况.
结论:
- ProfhEX是用于复合目标预测的最大和最准确的平台之一.
- 增强的平台支持早期药物发现,并改善目标责任评估.
- ProfhEX解决了当前预测工具的局限性,提高了药物开发效率.
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