Parallelizing Drug Discovery: HPC Pipelines for Alzheimer's Molecular Docking and Simulation
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911132026929152 |
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| author | Alliata, Paul Ruiz Rubaga, Diana Kumlin, Daniel Puliga, Alberto |
| author_facet | Alliata, Paul Ruiz Rubaga, Diana Kumlin, Daniel Puliga, Alberto |
| contents | High-performance computing (HPC) is reshaping computational drug discovery by enabling large-scale, time-efficient molecular simulations. In this work, we explore HPC-driven pipelines for Alzheimer's disease drug discovery, focusing on virtual screening, molecular docking, and molecular dynamics simulations. We implemented a parallelised workflow using GROMACS with hybrid MPI-OpenMP strategies, benchmarking scaling performance across energy minimisation, equilibration, and production stages. Additionally, we developed a docking prototype that demonstrates significant runtime gains when moving from sequential execution to process-based parallelism using Python's multiprocessing library. Case studies on prolinamide derivatives and baicalein highlight the biological relevance of these workflows in targeting amyloid-beta and tau proteins. While limitations remain in data management, computational costs, and scaling efficiency, our results underline the potential of HPC to accelerate neurodegenerative drug discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00937 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Parallelizing Drug Discovery: HPC Pipelines for Alzheimer's Molecular Docking and Simulation Alliata, Paul Ruiz Rubaga, Diana Kumlin, Daniel Puliga, Alberto Distributed, Parallel, and Cluster Computing High-performance computing (HPC) is reshaping computational drug discovery by enabling large-scale, time-efficient molecular simulations. In this work, we explore HPC-driven pipelines for Alzheimer's disease drug discovery, focusing on virtual screening, molecular docking, and molecular dynamics simulations. We implemented a parallelised workflow using GROMACS with hybrid MPI-OpenMP strategies, benchmarking scaling performance across energy minimisation, equilibration, and production stages. Additionally, we developed a docking prototype that demonstrates significant runtime gains when moving from sequential execution to process-based parallelism using Python's multiprocessing library. Case studies on prolinamide derivatives and baicalein highlight the biological relevance of these workflows in targeting amyloid-beta and tau proteins. While limitations remain in data management, computational costs, and scaling efficiency, our results underline the potential of HPC to accelerate neurodegenerative drug discovery. |
| title | Parallelizing Drug Discovery: HPC Pipelines for Alzheimer's Molecular Docking and Simulation |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2509.00937 |