RNA molecules adopt three-dimensional structures that are critical to their function and of interest in drug discovery. Few RNA structures are known, however, and predicting them computationally has proven challenging. We introduce a machine learning approach that enables identification of accurate structural models without assumptions about their defining characteristics, despite being trained with only 18 known RNA structures. The resulting scoring function, the Atomic Rotationally Equivariant Scorer (ARES), substantially outperforms previous methods and consistently produces the best results in community-wide blind RNA structure prediction challenges. By learning effectively even from a small amount of data, our approach overcomes a major limitation of standard deep neural networks. Because it uses only atomic coordinates as inputs and incorporates no RNA-specific information, this approach is applicable to diverse problems in structural biology, chemistry, materials science, and beyond.

RNA 分子采用三维结构，这对其功能至关重要，并且对药物发现也很重要。然而，我们所知的 RNA 结构很少，并且通过计算来预测它们已被证明具有挑战性。我们引入了一种机器学习方法，尽管仅使用 18 种已知的 RNA 结构进行训练，但该方法能够识别准确的结构模型，而无需对其定义特征进行假设。由此产生的评分函数，原子旋转等变评分器 (ARES)，大大优于以前的方法，并在全群盲 RNA 结构预测挑战中持续产生最佳结果。通过即使从少量数据中也能有效学习，我们的方法克服了标准深度神经网络的主要限制。由于它仅使用原子坐标作为输入，并且不包含 RNA 特异性信息，因此该方法适用于结构生物学、化学、材料科学等领域的各种问题。