用神经网络势能精准计算蛋白-配体结合自由能，提升药物设计效率。
QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials
- 采用基于TensorNet的新型神经网络势能模型AceFF 1.0，支持多种化学基团与带电分子。
- 相比GAFF2和ANI2-x，结合自由能预测准确率与相关性均显著提升，且可使用2 fs步长模拟。
- 模型已开源，适用于需高精度结合亲和力预测的药物研发场景。
准确预测蛋白-配体结合亲和力对药物发现中的苗头化合物优化至关重要，但传统配体力场限制了预测精度。本文验证了基于神经网络势能（NNPs）的相对结合自由能（RBFE）计算方法。我们采用新型小分子神经网络势能模型AceFF 1.0，基于TensorNet架构，覆盖所有重要元素并支持带电分子，扩展了适用范围。在标准基准测试中，其结合亲和力预测的总体准确性和相关性优于GAFF2和ANI2-x；与OPLS4相比略低精度但相关性相当。此外，该模型可实现2 fs时间步长的模拟，比以往NNP模型至少快一倍，显著提升计算效率。结果表明，当前版本的神经网络势能已具备实际应用潜力，未来可进一步推动自由能计算发展。代码与模型已公开供研究使用。
原文摘要 · Abstract (English)
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.
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