通过精细调整蛋白质和连接体语言模型来学习结合性亲和关系
Rohan Gorantla1,2, Aryo Pradipta Gema1, Ian Xi Yang2
1School of Informatics, University of Edinburgh, Crichton Street, Edinburgh EH8 9AB, U.K.
Journal of chemical information and modeling
|October 31, 2025
概括
我们开发了BALM,这是一个深度学习框架,用于预测蛋白质 - 配体结合亲和力. BALM可以将其推广到新的标和药物,为药物发现查提供一种高效和可适应的工具.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 机器学习 机器学习
背景情况:
- 准确的in silico预测蛋白质 - 配体结合亲和力对于药物发现至关重要.
- 像对接这样的传统方法往往是无效的,而自由能源方法在计算上昂贵.
- 现有的深度学习模型缺乏概括能力,目前的评估方法不足.
研究的目的:
- 引入BALM,这是一个新的深度学习框架,用于预测绑定亲和力.
- 提出改进的评估策略,以评估模型对新目标的性能.
- 提高计算机工具在早期药物发现中的效率和适应性.
主要方法:
- 在BALM框架内使用预训练的蛋白质和连接体语言模型.
- 使用各种数据集和指标开发了改进的评估策略.
- 策划的绑定DB用于模型培训和验证.
主要成果:
- BALM展示了对未见的药物,支架和目标的概括.
- 优于传统的机器学习和对接方法,在一些针对USP7和Mpro.ro等目标的短暂学习场景中表现出色.
- 展示了计算效率和高适应性.
结论:
- BALM提供了一个准确,高效和适应性的解决方案,用于结合亲和力预测.
- 拟议的评估方法可以更严格地评估机器学习评分工具.
- 在早期药物发现阶段,BALM对于大规模查很实用.
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