GS-DTI:一个图形结构意识的框架,利用大型语言模型来预测药物向相互作用
Qinze Yu1, Chang Zhou1, Jiyue Jiang1
1Department of Computer Science and Engineering, CUHK, Hong Kong SAR 999077, China.
通过使用图形神经网络和先进的蛋白质模型,GS-DTI通过准确预测药物向相互作用 (DTI) 来增强药物发现. 这一框架改善了对未经探索的目标和化合物的概括性.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 对药物向相互作用 (DTI) 的准确预测对于药物发现至关重要,特别是对于新的向和化合物.
- 图形神经网络和预训练模型为捕获分子特征和改进DTI预测概括提供了先进的功能.
研究的目的:
- 为预测药物向相互作用 (DTI) 开发一个强大的和可泛化的框架.
- 整合分子图形变压器,蛋白质语言模型和蛋白质三级结构,以提高DTI预测.
- 提供可解释的预测,并在具有挑战性的跨领域设置中提高性能.
主要方法:
- GS-DTI框架使用分子图形变压器从SMILES中提取药物特征.
- 蛋白质特征是从使用蛋白质语言模型的序列和预测的3D结构中得出的.
- 具有对比学习的多任务损失函数增强了概括性和可解释性.
主要成果:
- 在基准数据集和跨域设置上,GS-DTI实现了最先进的性能.
- 该模型显示,对于药物标对冷启动预测,马修斯相关系数 (MCC) 的提高超过10%.
- GS-DTI准确地识别了绑定口袋,提供了强大的解释性,并展示了虚拟选中的潜力.
结论:
- GS-DTI为DTI预测提供了一种强大且可解释的方法.
- 该框架在概括方面取得了显著的改进,特别是在新目标和新化合物方面.
- 通过高效的虚拟查,GS-DTI有望加速药物发现.
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