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Improving drug discovery through parallelism

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Abstract

Compound identification in ligand-based virtual screening is limited by two key issues: the quality and the time needed to obtain predictions. In this sense, we designed OptiPharm, an algorithm that obtained excellent results in improving the sequential methods in the literature. In this work, we go a step further and propose its parallelization. Specifically, we propose a two-layer parallelization. Firstly, an automation of the molecule distribution process between the available nodes in a cluster, and secondly, a parallelization of the internal methods (initialization, reproduction, selection and optimization). This new software, called pOptiPharm, aims to improve the quality of predictions and reduce experimentation time. As the results show, the performance of the proposed methods is good. It can find better solutions than the sequential OptiPharm, all while reducing its computation time almost proportionally to the number of processing units considered.

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Funding

This work has been financed by the National R+D+i Plan Project PID2021-123278OB-I00 of the Spanish Ministry of Science and Innovation and EIE funds; the Andalusian Regional Government through the grant: Proyectos de Excelencia (P18-RT-1193); and finally, the University of Almería through the grant: “Ayudas a proyectos de investigación I+D+I en el marco del Programa Operativo FEDER 2014–20” (UAL18-TIC-A020-B). Savíns Puertas Martín is a fellow of the “Margarita Salas” grant (RR_A_2021_21), financed by the European Union (NextGenerationEU). J.J. Moreno is supported by an FPU Fellowship (FPU16/05946) from the Spanish Ministry of Education.

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Author notes

  1. Savíns Puertas-Martín, Juana L. Redondo, Juan José Moreno and Pilar M. Ortigosa have contributed equally to this work.

Authors and Affiliations

  1. Supercomputing - Algorithms Research Group (SAL), Agrifood Campus of International Excellence, University of Almería, Carretera Sacramento s/n, La Cañada de San Urbano, 04120, Almería, Spain

    Jerónimo S. García, Savíns Puertas-Martín, Juana L. Redondo, Juan José Moreno & Pilar M. Ortigosa

  2. Information School, University of Sheffield, 221, Portobello Street, Sheffield, S1 4DP, United Kingdom

    Savíns Puertas-Martín

Authors
  1. Jerónimo S. García
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  2. Savíns Puertas-Martín
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  3. Juana L. Redondo
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  4. Juan José Moreno
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Contributions

All authors contributed equally to this work. JSG, SPM, JLR, JJM and PMO conceived the experiments, conducted the experiments, analyzed the results and reviewed the manuscript.

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Correspondence to Juana L. Redondo.

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García, J.S., Puertas-Martín, S., Redondo, J.L. et al. Improving drug discovery through parallelism. J Supercomput 79, 9538–9557 (2023). https://doi.org/10.1007/s11227-022-05014-0

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