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Automated Absolute Binding Free Energy Calculation Workflow for Drug Discovery
Benjamin Ries1, Irfan Alibay2, Nithishwer Mouroug Anand2
1Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str 65, 88397 Biberach an der Riss, Germany.
Journal of Chemical Information and Modeling
|July 2, 2024
Summary
This study presents an automated Python workflow for calculating absolute binding free energies (ABFEs) to accelerate drug discovery. The system ranks potential drug candidates, streamlining the selection of favorable compounds.
Area of Science:
- Computational chemistry
- Drug discovery and development
Background:
- Absolute binding free energies (ABFEs) are critical for lead discovery in drug development.
- Previous work demonstrated the utility of in silico predictions for ranking drug candidates.
Purpose of the Study:
- To develop an automated Python workflow for efficient ABFE calculations.
- To minimize manual effort in the drug discovery pipeline.
Main Methods:
- Utilized Python and Snakemake for workflow automation and task management.
- Input: receptor PDB and ligand SDF files.
- Output: .tsv file with ranked ligands and their ABFEs.
Main Results:
- Demonstrated an automated workflow for calculating ABFEs with minimal user input.
- Successfully ranked ligands based on their binding free energies.
- Validated the workflow's effectiveness using a benchmark system.
Conclusions:
- The automated workflow significantly reduces manual effort in ABFE calculations.
- This approach supports efficient drug discovery by ranking and recommending favorable drug candidates.
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