We introduce AceFF, a pre-trained machine learning interatomic potential (MLIP) optimized for small molecule drug discovery. While MLIPs have emerged as efficient alternatives to Density Functional Theory (DFT), generalizability across diverse chemical spaces remains difficult. AceFF addresses this via a refined TensorNet2 architecture trained on a comprehensive dataset of drug-like compounds. This approach yields a force field that balances high-throughput inference speed with DFT-level accuracy. AceFF fully supports the essential medicinal chemistry elements (H, B, C, N, O, F, Si, P, S, Cl, Br, I) and is explicitly trained to handle charged states. Validation against rigorous benchmarks, including complex torsional energy scans, molecular dynamics trajectories, batched minimizations, and forces and anergy accuracy demonstrates that AceFF establishes a new state-of-the-art for organic molecules. The AceFF-2 model weights and inference code are available at https://huggingface.co/Acellera/AceFF-2.0.

翻译：本文介绍AceFF，一种针对小分子药物发现优化的预训练机器学习原子间势函数（MLIP）。尽管MLIP已成为密度泛函理论（DFT）的高效替代方案，但其在不同化学空间中的泛化能力仍具挑战性。AceFF通过基于类药化合物综合数据集精调的TensorNet2架构解决了这一问题，实现了高通量推理速度与DFT级精度的平衡。该势函数完整支持药物化学核心元素（H、B、C、N、O、F、Si、P、S、Cl、Br、I），并经过专门训练以处理带电态。在复杂扭转能扫描、分子动力学轨迹、批量最小化以及力与能量精度等严格基准测试中的验证表明，AceFF为有机分子建立了新的性能标杆。AceFF-2模型权重与推理代码已发布于https://huggingface.co/Acellera/AceFF-2.0。