用流模型精准预测生物分子复合物结构，助力药物研发。
NeuralPLexer3: Accurate Biomolecular Complex Structure Prediction with Flow Models
- 基于物理启发的流模型生成结构，提升精度与效率。
- 在关键生物分子互作类型上达当前最优，支持配体诱导构象变化。
- 适合从事结构生物学与药物设计的研究者参考。
结构解析对于理解疾病机制和开发新疗法至关重要。基于机器学习的结构预测方法已能仅凭序列和分子拓扑信息计算预测蛋白质及生物大分子复合物结构。尽管该领域进展显著，但将预测模型应用于真实药物发现仍面临挑战。本文提出NeuralPLexer3——一种受物理启发的流模型生成方法，在关键生物分子相互作用类型上实现当前最佳预测精度，并在训练与采样效率方面优于前代模型及其它方法。通过新开发的基准测试策略评估，NeuralPLexer3在结构药物设计至关重要的方面表现优异，如物理合理性及配体诱导的构象变化预测能力。
原文摘要 · Abstract (English)
Structure determination is essential to a mechanistic understanding of diseases and the development of novel therapeutics. Machine-learning-based structure prediction methods have made significant advancements by computationally predicting protein and bioassembly structures from sequences and molecular topology alone. Despite substantial progress in the field, challenges remain to deliver structure prediction models to real-world drug discovery. Here, we present NeuralPLexer3 -- a physics-inspired flow-based generative model that achieves state-of-the-art prediction accuracy on key biomolecular interaction types and improves training and sampling efficiency compared to its predecessors and alternative methodologies. Examined through newly developed benchmarking strategies, NeuralPLexer3 excels in vital areas that are crucial to structure-based drug design, such as physical validity and ligand-induced conformational changes.
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