FEP-SPell-ABFE: An Open-Source Automated Alchemical Absolute Binding Free-Energy Calculation Workflow for Drug Discovery
Bibliographic record
Abstract
The binding affinity between a drug molecule and its target, measured by the absolute binding free energy (ABFE), is a crucial factor in the lead discovery phase of drug development. Recent research has highlighted the potential of in silico ABFE predictions to directly aid drug development by allowing for the ranking and prioritization of promising candidates. This work introduces an open-source Python workflow called FEP-SPell-ABFE, designed to automate ABFE calculations with minimal user involvement. The workflow requires only three key inputs: a receptor protein structure in PDB format, candidate ligands in SDF format, and a configuration file (config.yaml) that governs both the workflow and molecular dynamics simulation parameters. It produces a ranked list of ligands along with their binding free energies in the comma-separated values (CSV) format. The workflow leverages SLURM (Simple Linux Utility for Resource Management) for automating task execution and resource allocation across the modules. A usage example and several benchmark systems for validation are provided. The FEP-SPell-ABFE workflow, along with a practical example, is publicly accessible on GitHub at https://github.com/freeenergylab/FEP-SPell-ABFE, distributed under the MIT License.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".