# QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials

arXiv: https://arxiv.org/abs/2501.01811 · HTML: https://mlsanity.com/paper/2501.01811/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 51 references that are in the map.
- First posted on arXiv 3 Jan 2025.

## Builds on (most cited first, 6 of 6)

- [TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials](https://mlsanity.com/paper/2202.02541/) (2022)
- [A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems](https://mlsanity.com/paper/2312.07511/) (2023)
- [TorchMD: A deep learning framework for molecular simulations](https://mlsanity.com/paper/2012.12106/) (2020)
- [TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials](https://mlsanity.com/paper/2306.06482/) (2023)
- [NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic](https://mlsanity.com/paper/2201.08110/) (2022)
- [Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations](https://mlsanity.com/paper/2408.12625/) (2024)

## Built on by (most cited first, 1 of 1)

- [AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules](https://mlsanity.com/paper/2601.00581/) (2026)

## Abstract (arXiv)

Accurate prediction of protein-ligand binding affinities is crucial in drug discovery, particularly during hit-to-lead and lead optimization phases, however, limitations in ligand force fields continue to impact prediction accuracy. In this work, we validate relative binding free energy (RBFE) accuracy using neural network potentials (NNPs) for the ligands. We utilize a novel NNP model, AceFF 1.0, based on the TensorNet architecture for small molecules that broadens the applicability to diverse drug-like compounds, including all important chemical elements and supporting charged molecules. Using established benchmarks, we show overall improved accuracy and correlation in binding affinity predictions compared with GAFF2 for molecular mechanics and ANI2-x for NNPs. Slightly less accuracy but comparable correlations with OPLS4. We also show that we can run the NNP simulations at 2 fs timestep, at least two times larger than previous NNP models, providing significant speed gains. The results show promise for further evolutions of free energy calculations using NNPs while demonstrating its practical use already with the current generation. The code and NNP model are publicly available for research use.