RT Journal Article
T1 Drugs Discovery by Shape Similarity Using Deep Learning.
A1 Romero Caparrós, Felipe
A1 Romero-Gómez, Luis Felipe
A1 López Redondo, Juana
A1 Martínez Ortigosa, Pilar
K1 Aprendizaje automático (Inteligencia artificial)
K1 Medicamentos – Diseño
K1 Reconocimiento de formas (Informática)
K1 Unidades de procesamiento gráfico
K1 Computación heterogénea
AB 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.
PB Springer Nature
YR 2025
FD 2025-01-23
LK https://hdl.handle.net/10630/46331
UL https://hdl.handle.net/10630/46331
LA eng
NO 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
NO https://openpolicyfinder.jisc.ac.uk/publication/16439?from=single_hit
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 4 may 2026