To assess the overall quality of our GB-FFs model, we compared its performance to the original GAFF parameterization using the highly parallel Tinker-HP GPU package. Our model is freely available (MIT Licence) on GitHub at , and a tutorial is included for generating GAFF parameters from a given SMILES, as well as for fine-tuning the model on a newer database. This paper introduces several significant contributions. First, it improves the existing GAFF by refining its parameters, thereby improving its accuracy. Second, it treats molecules as directed molecular graphs and employs a self-attention mechanism to effectively aggregate information, enhancing the model's performance. Third, it incorporates an efficient charge transfer procedure to enhance the prediction of fixed atomic charges with an O(N ) complexity. Furthermore, the model's validation spans across various properties, including both quantum-mechanical and experimental ones, such as hydration free energies, across diverse molecular systems. Additionally, the versatility and ease of use of the model allow its extension to other force fields. Lastly, the paper explores the limits of GAFF by modifying its functional forms, shedding light on its strengths and weaknesses.