通过预先训练的多视图分子表示来提高药物标结合预测的概括性
Xike Ouyang1, Yannuo Feng1, Chen Cui2
1School of Information Science and Technology, Institute of Computational Biology, Northeast Normal University, Changchun, Jilin 130117, China.
本研究介绍了预训练的多视图分子表示 (PMMR),这是一种用于准确预测药物标结合的新型神经网络方法. PMMR增强了概括性,特别是在新情景中,有助于药物发现.
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:
- 药物标结合的预测对于药物开发至关重要.
- 现有的方法在分子多样性和有限的数据方面扎,影响准确性和通用性.
研究的目的:
- 开发一种高度准确和可通用的药物标结合预测方法.
- 解决当前模型在处理各种分子性质和稀缺训练数据方面的局限性.
主要方法:
- 建议使用神经网络进行预训练的多视图分子表示 (PMMR).
- 利用预先训练的模型来转移目标蛋白和药物表征.
- 使用图形神经网络和变压器从图形和SMILES字符串中学习互补的本地和全球药物特征.
主要成果:
- 与现有方法相比,PMMR在药物向亲和和相互作用数据集上表现出卓越的表现.
- 在概括性方面取得了显著的改进,特别是在冷启动场景中.
- 通过对循环林依赖激酶2的案例研究,展示了药物发现的潜力.
结论:
- PMMR为准确和可概括的药物标结合预测提供了一个强大的解决方案.
- 该方法有效地减轻了因有限的数据和分子多样性而产生的问题.
- 作为加速药物发现管道的有价值工具,PMMR显示出希望.
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