Drugs Discovery by Shape Similarity Using Deep Learning

被引：0

作者：

Romero, Felipe ^{[1
]}

Romero, Luis F. ^{[1
]}

Redondo, Juana L. ^{[2
]}

Ortigosa, Pilar M. ^{[2
]}

机构：

[1] Univ Malaga, Comp Architecture, Blvd Louis Pasteur, Malaga 29071, Spain

[2] Univ Almeria, Informat, Almeria, Spain

来源：

JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS | 2025年 / 204卷 / 03期

关键词：

Deep Learning; Drugs discovery; Hybrid Computing; 3D Object Recognition; Embarrassingly Parallel; POINTS; SPHERE;

D O I：

10.1007/s10957-024-02589-x

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

Searching for one or several molecules in a database using their shapes has great interest from a biochemical point of view, but requires a huge computational effort due to the complexity of the algorithms and the sizes of the databases in the pharmaceutical industry. This work uses Deep Learning by training neural networks with hundreds of images of each molecule, rendered by projections (using GPUs) on planes whose normal vectors are equally distributed in the 3D space (using Fibonacci spirals). The results obtained, both in accuracy and time, exceeded expectations, opening a hopeful path of research.

引用

页数：23

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