2026-05-27T23:52:54Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/463312026-04-10T23:46:12Zcom_10630_2254col_10630_37953
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Romero Caparrós, Felipe
author
Romero-Gómez, Luis Felipe
author
López Redondo, Juana
author
Martínez Ortigosa, Pilar
2026-04-10T10:24:06Z
2025-01-23
Romero, F., Romero, L.F., Redondo, J.L. et al. Drugs Discovery by Shape Similarity Using Deep Learning. J Optim Theory Appl 204, 37 (2025). https://doi.org/10.1007/s10957-024-02589-x
https://hdl.handle.net/10630/46331
10.1007/s10957-024-02589-x
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.
eng
Aprendizaje automático (Inteligencia artificial)
Medicamentos – Diseño
Reconocimiento de formas (Informática)
Unidades de procesamiento gráfico
Computación heterogénea
Drugs Discovery by Shape Similarity Using Deep Learning.
journal article
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