通过基于神经指纹的深度学习技术虚拟筛选分子

Rivaaj Monsia, Sudeep Bhattacharyya
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摘要

摘要 利用卷积神经网络衍生指纹,开发并优化了一种基于机器学习的药物筛选技术。将基于神经网络的指纹图谱技术中权重的优化与固定摩根指纹图谱在药物-靶标结合亲和力的二元分类方面进行了比较。评估使用了 ZINC15 数据库中随机选择的小分子进行训练,使用了六种不同的靶蛋白。事实证明,这种新架构能更有效地筛选出与特定靶标结合力较弱的分子,并保留与之结合力较强的分子。科学贡献 我们开发了一种新的基于神经指纹的筛选模型,该模型捕获命中的能力很强。尽管使用的数据集较小,但该模型能够绘制化学空间图,与其他当代分子筛选算法类似。本算法的新颖之处在于在测试其预测能力之前对模型进行训练和调整的速度,因此在机器学习嵌入式计算药物发现领域迈出了重要一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Virtual Screening of Molecules via Neural Fingerprint-based Deep Learning Technique
Abstract A machine learning-based drug screening technique has been developed and optimized using convolutional neural network-derived fingerprints. The optimization of weights in the neural network-based fingerprinting technique was compared with fixed Morgan fingerprints in regard to binary classification on drug-target binding affinity. The assessment was carried out using six different target proteins using randomly chosen small molecules from the ZINC15 database for training. This new architecture proved to be more efficient in screening molecules that less favorably bind to specific targets and retaining molecules that favorably bind to it. Scientific contribution We have developed a new neural fingerprint-based screening model that has a significant ability to capture hits. Despite using a smaller dataset, this model is capable of mapping chemical space similar to other contemporary algorithms designed for molecular screening. The novelty of the present algorithm lies in the speed with which the models are trained and tuned before testing its predictive capabilities and hence is a significant step forward in the field of machine learning-embedded computational drug discovery.
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