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 3D 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 multi-scale 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 to 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 (average TM-score=0.93) and recently determined ligand-binding proteins (average TM-score=0.89). Case studies reveal that the predicted conformational variations are consistent with structure determination experiments for important targets, including human KRAS$^\textrm{G12C}$, ketol-acid reductoisomerase, and purine GPCRs. Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.

Zhuoran Qiao

Weili Nie

Arash Vahdat

Thomas F. Miller

Animashree Anandkumar

Nature Machine Intelligence

NATURE MACHINE INTELLIGENCE 将在机器学习、机器人和人工智能的广泛主题中发表高质量的原创研究和评论。该杂志还将探讨和讨论这些领域开始对其他科学学科以及社会和工业的许多方面产生的重大影响。在科学发现、医疗保健、医学诊断以及安全和可持续的城市、交通和农业等领域，机器智能可以增强人类能力和知识的机会不计其数。与此同时，出现了许多关于道德、社会和法律问题的重要问题，特别是考虑到快速发展的自然机器智能将提供一个平台来讨论这些广泛的影响——鼓励跨学科对话——通过评论、新闻专题、新闻和观点文章以及通信。

Nature Machine Intelligence will publish high-quality original research and reviews on a wide range of topics in machine learning, robotics, and artificial intelligence. The journal will also explore and discuss the significant impacts these fields are beginning to have on other scientific disciplines as well as many aspects of society and industry. The opportunities for machine intelligence to enhance human capabilities and knowledge are countless, particularly in areas such as scientific discovery, healthcare, medical diagnostics, and safe and sustainable cities, transportation, and agriculture. At the same time, many important ethical, social, and legal issues have emerged, especially considering the rapid development of natural machine intelligence. The journal will provide a platform to discuss these broad impacts, encouraging interdisciplinary dialogue through reviews, news features, news and opinion articles, and communications.

《Nature Machine Intelligence》将发表关于机器学习、机器人和人工智能等广泛主题的高质量原创研究和评论。该期刊还将探讨这些领域对其他科学学科、社会和工业的重大影响。在科学发现、医疗保健、医学诊断，以及安全和可持续的城市、交通和农业等领域，机器智能为增强人类能力和知识提供了无数机会。同时，随着自然机器智能的快速发展，许多关于伦理、社会和法律的问题也随之出现。该期刊将为讨论这些广泛影响提供一个平台，鼓励跨学科的对话，包括评论、新闻专题、新闻和观点文章以及通信。

Category	Quartile
计算机科学	1
计算机科学, 计算机人工智能	1
计算机科学, 计算机跨学科应用	1

State-specific protein–ligand complex structure prediction with a multiscale deep generative model

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Journal Info

Category	Quartile
COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS	1