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An Effective Solution for Drug Discovery Based on the Tangram Meta-Heuristic and Compound Filtering
Volume 34, Issue 4 (2023), pp. 743–769
Pub. online: 7 November 2023      Type: Research Article      Open accessOpen Access
Received
1 May 2023
Accepted
1 October 2023
Published
7 November 2023

Abstract

Ligand-Based Virtual Screening accelerates and cheapens the design of new drugs. However, it needs efficient optimizers because of the size of compound databases. This work proposes a new method called Tangram CW. The proposal also encloses a knowledge-based filter of compounds. Tangram CW achieves comparable results to the state-of-the-art tools OptiPharm and 2L-GO-Pharm using about a tenth of their computational budget without filtering. Activating it discards more than two thirds of the database while keeping the desired compounds. Thus, it is possible to consider molecular flexibility despite increasing the options. The implemented software package is public.

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Biographies

Cruz Nicolás C.
ncalvocruz@ugr.es

N.C. Cruz is a post-doctoral researcher at the Department of Computer Engineering, Automation, and Robotics of the University of Granada, Spain. After studying for a bachelor’s and master’s degree in computer engineering, he obtained his PhD in computer science at the University of Almería, Spain, in 2019. He is a member of the Supercomputing-Algorithms Research Group at that institution. His research focuses on numerical optimization through meta-heuristics and high-performance computing applied to different problems, such as design and control of solar power tower plants, neural model tuning, and optimization of mechanisms.

Puertas-Martín Savíns
savinspm@ual.es

S. Puertas-Martín is a post-doctoral researcher at the Department of Informatics of the University of Almería, Spain. He is also doing a research stay at the Information School of the University of Sheffield in the United Kingdom. He obtained his PhD in computer science at the University of Almería in 2020. He is a member of the Supercomputing-Algorithms Research Group at that institution. His research interests are drug discovery, global optimization and high-performance computing.

Redondo Juana L.
jlredondo@ual.es

J.L. Redondo is a full professor at the Department of Informatics of the University of Almería, Spain. She obtained her PhD in computer science from the University of Almería in 2008. She is a member of the Supercomputing-Algorithms Research Group at that institution. Her research interests include high-performance computing, global optimization and applications.

Ortigosa Pilar M.
ortigosa@ual.es

P.M. Ortigosa is a full professor of architecture and computer technology at the University of Almeriá, Spain. She received MSc degrees in physics and electronic engineering from the University of Granada in 1994 and 1996, respectively, and a PhD in computer science from the University of Málaga in 1999. She is a member of the Supercomputing-Algorithms Research Group at the University of Almería. Her research focuses on high-performance computing, metaheuristic global optimization, computational intelligence, deep learning, and the application to several real problems. Recently she has been working on the Internet of Things.


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Copyright
© 2023 Vilnius University
Open access article under the CC BY license.

Keywords
virtual screening shape similarity meta-heuristic knowledge-based filtering parallel computing

Funding
This work has been supported by Grant PID2021-123278OB-I00 funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”; and by projects PDC2022-133370-I00 and TED2021-132020B-I00 funded by MCIN/AEI/ 10.13039/5011 00011033 and by European Union Next GenerationEU/PRTR. N.C. Cruz is supported by the Ministry of Economic Transformation, Industry, Knowledge and Universities from the Andalusian government (PAIDI 2021: POSTDOC_21_00124). Savíns Puertas Martín is a fellow of the “Margarita Salas” grant (RR_A_2021_21), financed by the European Union (NextGenerationEU).

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