TF3P: Three-Dimensional Force Fields Fingerprint Learned by Deep Capsular Network.,Journal of Chemical Information and Modeling

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TF3P: Three-Dimensional Force Fields Fingerprint Learned by Deep Capsular Network.
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2020-05-11 , DOI: 10.1021/acs.jcim.0c00005
Yanxing Wang ₁ , Jianxing Hu ₁ , Junyong Lai ₁ , Yibo Li ₂ , Hongwei Jin ₁ , Lihe Zhang ₁ , Liang-Ren Zhang ₁ , Zhen-Ming Liu ₁

Affiliation

Molecular fingerprints are the workhorse in ligand-based drug discovery. In recent years, an increasing number of research papers reported fascinating results on using deep neural networks to learn 2D molecular representations as fingerprints. It is anticipated that the integration of deep learning would also contribute to the prosperity of 3D fingerprints. Here, we unprecedentedly introduce deep learning into 3D small molecule fingerprints, presenting a new one we termed as the three-dimensional force fields fingerprint (TF3P). TF3P is learned by a deep capsular network whose training is in no need of labeled data sets for specific predictive tasks. TF3P can encode the 3D force fields information of molecules and demonstrates the stronger ability to capture 3D structural changes, to recognize molecules alike in 3D but not in 2D, and to identify similar targets inaccessible by other 2D or 3D fingerprints based on only ligands similarity. Furthermore, TF3P is compatible with both statistical models (e.g., similarity ensemble approach) and machine learning models. Altogether, we report TF3P as a new 3D small molecule fingerprint with a promising future in ligand-based drug discovery. All codes are written in Python and available at https://github.com/canisw/tf3p.

中文翻译：

TF3P：深囊网络学习的三维力场指纹。

分子指纹是基于配体的药物发现中的主力军。近年来，越来越多的研究论文报道了使用深度神经网络学习2D分子表示作为指纹的有趣结果。可以预期，深度学习的集成也将有助于3D指纹的繁荣。在这里，我们介绍了前所未有深度学习到3D小分子指纹，呈现一个新的，我们称为的牛逼重稀土维˚F奥尔塞˚F ields ˚F英格p林特（TF3P）。TF3P由深层封装网络学习，该网络的训练不需要针对特定预测任务的标记数据集。TF3P可以编码分子的3D力场信息，并展示出更强的能力来捕获3D结构变化，识别3D中的相似分子但不能识别2D中的分子，以及仅基于配体相似性来识别其他2D或3D指纹无法访问的相似目标。此外，TF3P与统计模型（例如，相似性集成方法）和机器学习模型都兼容。总之，我们将TF3P报告为一种新的3D小分子指纹图谱，在基于配体的药物发现中前景广阔。所有代码都是用Python编写的，可从https://github.com/canisw/tf3p获得。

更新日期：2020-06-23

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