MREDTA:一种基于BERT和变压器的分子表示编码器,用于预测药物标结合亲和力.
Xu Sun1, Juanjuan Huang1,2, Yabo Fang1
1Department of Computational Mathematics, School of Mathematics, Jilin University, Changchun, China.
我们开发了MREDTA,一种用于预测药物标结合亲缘关系 (DTA) 的新型模型,以帮助药物重新定位. 与现有的DTA模型相比,MREDTA显示出更高的准确性和通用性.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 药物标结合亲和力 (DTA) 的预测对于识别现有药物的新疗法应用 (药物重新定位) 是至关重要的.
- 当前的DTA模型在实现高精度和通用性方面面临着挑战.
- 开发强大的模型对于高效的药物发现管道至关重要.
研究的目的:
- 引入一种新型模型,即基于分子表示编码器的DTA预测 (MREDTA),用于准确和可概括的DTA预测.
- 利用先进的深度学习架构来增强分子特征提取.
- 通过现有的最先进的方法来验证模型的性能.
主要方法:
- 在MREDTA中集成了BERT-Trans Block,Multi-Trans Block和DTI学习模块.
- 该模型同时通过跳过连接提取本地和全球分子特征.
- 多晶体块增强了对分子结构的敏感性,而BERT提高了通用性.
主要成果:
- 在KIBA和戴维斯数据集上,MREDTA表现最佳,超过了12个先进模型.
- 一个涉及2034个FDA批准的非小细胞肺癌 (NSCLC) 药物的案例研究针对突变EGFRT790M验证了MREDTA的强度.
- 分子对接结果证实了该模型在预测药物向相互作用方面的可靠性.
结论:
- 在DTA预测准确性和通用性方面,MREDTA提供了显著的进步.
- 该模型有望加速药物重新定位和个性化医疗,特别是对于像突变EGFR这样的复杂目标.
- MREDTA的架构为计算药物发现的未来发展提供了一个强大的框架.
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