The binding complexes formed by proteins and small molecule ligands are ubiquitous and critical to life. Despite recent advancements in protein structure prediction, existing algorithms are so far unable to systematically predict the binding ligand structures along with their regulatory effects on protein folding. To address this discrepancy, we present NeuralPLexer, a computational approach that can directly predict protein–ligand complex structures solely using protein sequence and ligand molecular graph inputs. NeuralPLexer adopts a deep generative model to sample the three-dimensional structures of the binding complex and their conformational changes at an atomistic resolution. The model is based on a diffusion process that incorporates essential biophysical constraints and a multiscale geometric deep learning system to iteratively sample residue-level contact maps and all heavy-atom coordinates in a hierarchical manner. NeuralPLexer achieves state-of-the-art performance compared with all existing methods on benchmarks for both protein–ligand blind docking and flexible binding-site structure recovery. Moreover, owing to its specificity in sampling both ligand-free-state and ligand-bound-state ensembles, NeuralPLexer consistently outperforms AlphaFold2 in terms of global protein structure accuracy on both representative structure pairs with large conformational changes and recently determined ligand-binding proteins. NeuralPLexer predictions align with structure determination experiments for important targets in enzyme engineering and drug discovery, suggesting its potential for accelerating the design of functional proteins and small molecules at the proteome scale.

由蛋白质和小分子配体形成的结合复合物无处不在，对生命至关重要。尽管最近在蛋白质结构预测方面取得了进展，但现有算法迄今为止无法系统地预测结合配体结构及其对蛋白质折叠的调节作用。为了解决这种差异，我们提出了 NeuralPLexer，这是一种计算方法，可以仅使用蛋白质序列和配体分子图输入直接预测蛋白质-配体复合物结构。 NeuralPLexer 采用深度生成模型以原子分辨率对结合复合物的三维结构及其构象变化进行采样。该模型基于扩散过程，该过程结合了基本的生物物理约束和多尺度几何深度学习系统，以分层方式迭代采样残留级接触图和所有重原子坐标。与所有现有方法相比，NeuralPLexer 在蛋白质-配体盲对接和灵活结合位点结构恢复的基准上实现了最先进的性能。此外，由于其对配体游离态和配体结合态整体采样的特异性，NeuralPLexer 在具有较大构象变化的代表性结构对和最近确定的配体结合蛋白上的整体蛋白质结构准确性方面始终优于 AlphaFold2。 NeuralPLexer 预测与酶工程和药物发现中重要靶点的结构测定实验相一致，表明其在蛋白质组规模上加速功能蛋白和小分子设计的潜力。