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Workflow and Tools for Crystallographic Fragment Screening at the Helmholtz-Zentrum Berlin

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FEP-SPell-ABFE: An Open-Source Automated Alchemical Absolute Binding Free-Energy Calculation Workflow for Drug

Pengfei Li¹, Tingting Pu¹, Ye Mei^2,3,4

¹Single Particle, LLC Suzhou, Jiangsu 215000, China.

Journal of Chemical Information and Modeling

|March 3, 2025

Summary

FEP-SPell-ABFE is a new Python workflow that automates absolute binding free energy (ABFE) calculations for drug discovery. This tool helps rank drug candidates by predicting their binding affinity to targets, accelerating lead discovery.

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Area of Science:

Computational chemistry
Drug discovery
Molecular modeling

Background:

Accurate prediction of drug-target binding affinity is essential for efficient lead discovery.
In silico methods for absolute binding free energy (ABFE) calculations can accelerate drug development by prioritizing candidates.
Automation of complex computational workflows is needed to increase accessibility and reduce user effort.

Purpose of the Study:

To introduce FEP-SPell-ABFE, an open-source Python workflow for automating absolute binding free energy (ABFE) calculations.
To provide a user-friendly tool requiring minimal input for predicting drug-target binding affinities.
To facilitate the ranking and prioritization of drug candidates in the early stages of drug development.

Main Methods:

The FEP-SPell-ABFE workflow automates ABFE calculations using molecular dynamics simulations.
Input requirements include receptor protein structure (PDB), candidate ligands (SDF), and a configuration file (config.yaml).
The workflow utilizes SLURM for task execution and resource management, outputting a ranked list of ligands with binding free energies (CSV).

Main Results:

The FEP-SPell-ABFE workflow successfully automates ABFE predictions with minimal user input.
The workflow generates a ranked list of ligands based on their predicted binding free energies.
Validation was performed using benchmark systems, and a usage example is provided.

Conclusions:

FEP-SPell-ABFE offers an automated and accessible solution for in silico ABFE predictions in drug discovery.
The workflow streamlines the process of ranking and prioritizing drug candidates, aiding lead discovery.
The open-source nature and public availability on GitHub promote wider adoption and further development.