论文标题
基于流的生成模型的创新药物样分子产生
Innovative Drug-like Molecule Generation from Flow-based Generative Model
论文作者
论文摘要
为了通过使用深度学习方法来设计给定生物分子的药物，最近发布了许多成功的模型。人们通常使用生成模型来设计新分子，并给定某些蛋白质。 Ligan被认为是在卷积神经网络上开发的深度学习模型的基线。最近，GraphBP表明了它可以通过使用具有图神经网络和多层感知的基于流的生成模型来预测结合亲和力优于传统分子对接方法的创新“真实”化学物质的能力。但是，所有这些方法都将蛋白质视为刚体，仅包括与结合有关的一小部分蛋白质。但是，蛋白质的动力学对于药物结合至关重要。根据GraphBP，我们建议生成源自蛋白质数据库的更多固体工作。结果将通过使用计算化学算法来通过有效性和结合亲和力进行评估。
To design a drug given a biological molecule by using deep learning methods, there are many successful models published recently. People commonly used generative models to design new molecules given certain protein. LiGAN was regarded as the baseline of deep learning model which was developed on convolutional neural networks. Recently, GraphBP showed its ability to predict innovative "real" chemicals that the binding affinity outperformed with traditional molecular docking methods by using a flow-based generative model with a graph neural network and multilayer perception. However, all those methods regarded proteins as rigid bodies and only include a very small part of proteins related to binding. However, the dynamics of proteins are essential for drug binding. Based on GraphBP, we proposed to generate more solid work derived from protein data bank. The results will be evaluated by validity and binding affinity by using a computational chemistry algorithm.
