AceFF用AI力场实现药物分子高精度高速模拟，适合新药研发。
AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules
- 基于优化的TensorNet2架构，训练于类药物化合物数据集。
- 在能量和力的精度上媲美量子计算，速度提升数百倍。
- 支持常见药物元素及带电状态，适合复杂分子模拟场景。
我们提出AceFF，一种专为小分子药物发现优化的预训练机器学习原子间势（MLIP）。尽管MLIP已成为密度泛函理论（DFT）的高效替代方案，但跨化学空间的泛化能力仍存挑战。AceFF通过在涵盖类药物化合物的综合性数据集上训练改进的TensorNet2架构，实现高通量推理速度与DFT级精度的平衡。该力场全面支持氢、硼、碳、氮、氧、氟、硅、磷、硫、氯、溴、碘等关键药物化学元素，并显式训练以处理带电状态。在复杂扭转能扫描、分子动力学轨迹、批量优化及力与能量精度测试等严格基准下验证，结果表明AceFF在有机分子的精度与速度组合中达到当前最先进水平，适用于药物发现场景。AceFF-2模型权重与推理代码已公开于https://huggingface.co/Acellera/AceFF-2.0。
原文摘要 · Abstract (English)
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. \mbox{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 tests of force and energy accuracy, demonstrates that AceFF is state-of-the-art for organic molecules in the accuracy and speed regime important for drug discovery. The AceFF-2 model weights and inference code are available at https://huggingface.co/Acellera/AceFF-2.0.
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