Upload README.md

README.md

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----
-license: other
-license_name: non-profit
-license_link: LICENSE
----
+---
+license: other
+license_name: license.txt
+license_link: LICENSE
+tags:
+- chemistry
+- molecular simulations
+- machine learning potentials
+- neural network potentials
+- drug discovery
+---
+
+# AceForce 1.0
+
+**Organization(s):** Acellera Therapeutics, inc  
+**Contact:** info@acellera.com  
+**License:** Refer to the accompanying license file for usage rights.
+
+---
+
+## Overview
+
+AceForce 1.0 is a next-generation **Neural Network Potential (NNP)** designed for **Relative Binding Free Energy (RBFE)** calculations in drug discovery. 
+It addresses key limitations of traditional molecular mechanics (MM) force fields and earlier NNP models, including restricted atom types, limited charge support, and computational inefficiencies.
+
+The model leverages the TensorNet architecture[1] and the NNP software library TorchMD-Net [2] to provide accurate predictions for diverse drug-like compounds, supporting all key chemical elements and charged molecules. 
+AceForce 1.0 improves the stability of molecular dynamics simulations, supports 2 fs timesteps, and achieves state-of-the-art accuracy with fewer outliers in RBFE predictions.
+
+
+## Description
+
+AceForce 1.0 is the first version of a new family of potentials released by [Acellera](https://www.acellera.com). 
+It uses [TensorNet](https://proceedings.neurips.cc/paper_files/paper/2023/file/75c2ec5f98d7b2f50ad68033d2c07086-Paper-Conference.pdf) 1-layer trained 
+on Acellera's internal proprietary dataset of molecular forces and energies using the wB97M-V/def2-tzvppd level of theory and VV10 dispersion corrections.
+
+The training set was built on [PubChem](https://ftp.ncbi.nlm.nih.gov/pubchem/Compound/CURRENT-Full/SDF). 
+We extracted the SMILES and generated molecules filtering out molecules larger than 20 atoms. 
+We kept only molecules with the elements H, B, C, N, O, F, Si, P, S, Cl, Br, and I, and a formal charge of -1,0,1.
+Energy are in eV. Forces are in eV/A.
+
+---
+
+## Key Features
+
+- **Broad Applicability:** Supports diverse drug-like molecules, including charged species and rare chemical groups.
+- **High Accuracy:** Benchmark-tested on the JACS dataset, demonstrating performance comparable to or better than MM-based methods (e.g., GAFF2, FEP+).
+- **Improved Stability:** Enables a 2 fs timestep for NNP/MM simulations, significantly reducing computational costs.
+- **Integration-Friendly:** Available for RBFE calculations via [HTMD](https://github.com/acellera/htmd).
+- **Open Science:** The model and all benchmarking data are accessible on GitHub for not-for-profit usage.
+- **Error Metrics:** AceForce 1.0 exhibits lower RMSE and higher Kendall tau correlations compared to traditional MM force fields across various protein targets.[3]
+
+---
+
+## Usage
+
+AceForce 1.0 is designed for use alone or in a NNP/MM approach, where the ligand is treated with the neural network potential and the environment with molecular mechanics. 
+
+1. Run ML potential molecular simulations of a small molecule using ACEMD with this [tutorial](https://software.acellera.com/acemd/nnp.html) , e.g. to minimize. 
+1. For a tutorial on running mixed protein-ligand simulations, refer to [NNP/MM in ACEMD](https://software.acellera.com/acemd/nnpmm.html).
+
+---
+
+## Applications
+
+- **Drug Discovery:** Optimizing lead compounds in hit-to-lead and lead optimization stages using free energy methods.
+- **Binding Free Energy Calculations:** Accurate and efficient RBFE predictions for diverse molecular systems.
+- **Molecular dynamics:** Capturing higher-body terms than traditional MM force fields, ACEFORCE can be used for structure minimization and dynamics of small molecules.
+
+## Limitations
+
+- **Small molecules only**: ACEFORCE 1.0 is trained on specifically curated and extended PubChem data. However, proteins, water, etc are not part of the dataset. ACEFORCE 2.0 will be capable of simulations of proteins.
+- **Time step**: Use time steps of 2fs to run dynamics with hydrogen mass repartitioning.
+- **Only -1,0,1 charges**: For simplicity we have trained only on these type of charged molecules; do not use it on +2,-2, etc. Aceforce 1.1 will fix this.
+ 
+---
+
+## References
+
+[1] Simeon, Guillem, and Gianni De Fabritiis, Tensornet: Cartesian tensor representations for efficient learning of molecular potentials, Advances in Neural Information Processing Systems 36 (2024), https://arxiv.org/abs/2306.06482
+
+[2] Raul P. Pelaez, Guillem Simeon, Raimondas Galvelis, Antonio Mirarchi, Peter Eastman, Stefan Doerr, Philipp Thölke, Thomas E. Markland, Gianni De Fabritiis, TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations, J. Chem. Theory Comput. 2024, 20, 10, 4076–4087, https://arxiv.org/abs/2402.17660
+
+[3] Francesc Sabanés Zariquiey, Stephen E. Farr, Stefan Doerr, Gianni De Fabritiis, QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials, https://arxiv.org/abs/2501.01811 (2025).