Atomic charge is a fundamental quantum chemical property essential for advancing drug design and discovery. Although quantum mechanics (QM) methods offer the highest level of accuracy, their computational demands scale quadratically with the number of atoms, limiting their practicality for large-scale applications. In light of this, empirical and semiempirical methods have been introduced to improve computational efficiency, albeit often at the expense of accuracy. The advent of artificial intelligence has witnessed a growing application of machine learning (ML) techniques to accelerate atomic charge predictions. However, existing ML models often suffer from low accuracy and limited generalization capabilities. To address these challenges, we introduce an advanced equivariant graph attention neural network specifically engineered to model long-range atomic electrostatic interactions with high precision. This model introduces a sophisticated global graph attention mechanism, enabling it to capture charge contributions across multiple scales. By utilizing a combination of structural symmetry-preserving transformations and multiscale attention, our approach not only preserves the inherent symmetries of molecular structures but also substantially improves the model's accuracy, generalization, and robustness in complex scenarios. Our empirical analyses demonstrate that, compared to leading baseline models, the proposed model improves charge prediction accuracy by over 40% on average across various charge-calculation schemes. Remarkably, the model achieves superior performance on the external RESP (restrained electrostatic potential) test data sets, with a 54.6% improvement over the baseline. Additionally, we evaluated our charge model under the setting of virtual screening, where it outperforms both the OPLS3 charges and baseline deep learning models across all evaluation metrics, highlighting its extensive potential for scientific discovery.
Qiaolin Gou, Qun Su, Jike Wang et al.· Journal of Chemical Informat...· 1 citation
Protein-protein interactions play pivotal roles in a wide range of biological processes. Determining the atomic-level structures of protein-protein complexes is indispensable for elucidating macromolecular interaction mechanisms and advancing structure-based drug design. Protein-protein docking, as one of the leading computational approaches for predicting complex structures, has seen considerable progress but requires rigorous evaluation in practical applications. In this study, we proposed a comprehensive benchmarking framework to evaluate 11 docking methods spanning traditional (HDOCK, PatchDock, PIPER, ZDOCK) and deep learning (DL)-based (EquiDock, ElliDock, EBMDock, GeoDock, DiffDock-PP, AlphaFold-Multimer, AlphaFold3) approaches. Our framework incorporates the classical DockingBenchmark 5.5 data set for evaluating flexible docking, introduces a newly curated data set (AACBench) for antibody-antigen complex docking, and establishes the PPCBench data set to examine the out-of-distribution (OOD) generalization capabilities of DL-based methods. In docking against apo structures, AlphaFold3 achieves a superior top-5 success rate of 77.98%, whereas the traditional approach HDOCK reaches merely 12.84%, despite its highest top-5 success rate of 85.24% when docking against holo structures. For antibody-antigen docking, AlphaFold3 remains the most accurate method (top-5 success rate: 31.78%) and substantially outperforms AlphaFold-Multimer in modeling the CDR-H3 loop. In OOD generalization tests, all DL-based models exhibit markedly reduced performance on the PPCBench data set. Overall, our work establishes a unified benchmarking framework that enables systematic evaluation of docking methods across diverse tasks and provides critical insights into the strengths and limitations of current docking strategies, thereby informing future developments in protein-protein docking research.
Linlong Jiang, Ke Zhang, Kai Zhu et al.· Journal of Chemical Informat...· 3 citations
Accurate prediction of metalloprotein-ligand interactions is critical for metalloprotein-targeted drug discovery. Conventional docking tools and existing deep learning (DL) models fail to reliably capture metal-ligand interactions, hampering the discovery of potent metalloprotein inhibitors. Here, we propose MetalloDock, the first DL-based docking framework specially designed for metalloprotein targets. By innovatively integrating an autoregressive spatial decoding engine with a physics-constrained geometric generation paradigm, MetalloDock can precisely reconstruct metal coordination geometries and accurately capture metal-ligand interactions, which enhance both the accuracy of metalloprotein-ligand docking and binding affinity prediction. Extensive evaluations on our custom-built benchmark data set demonstrate that MetalloDock outperforms existing methods, including AlphaFold3, in docking success rate and virtual screening performance for metalloprotein targets. In real-world applications, MetalloDock successfully identified multiple novel hit compounds in a virtual screening campaign targeting the prostate-specific membrane antigen. Additionally, it enabled rational drug design for acidic polymerase endonuclease, leading to the discovery of potent inhibitors. These results highlight the broad applicability of MetalloDock in accelerating metalloprotein-targeted drug discovery and provide a standardized framework for future evaluation of metalloprotein-specific docking algorithms.
Hui Zhang, Xujun Zhang, Qun Su et al.· Journal of the American Chem...· 5 citations
Accurate atomistic biomolecular simulations are vital for understanding disease mechanisms and drug discovery, yet existing methods struggle to balance quantum-mechanical accuracy with computational scalability. Classical force fields often lack precision, while quantum methods are computationally prohibitive for complex biological systems. Here we show that LiTEN, a scalable equivariant neural network, resolves this dilemma by efficiently modeling complex three- and four-body interactions with linear complexity via Linearly Tensorized Quadrangle Attention. We introduce LiTEN-FF, a foundation model pre-trained on extensive datasets to ensure broad chemical generalization across diverse molecular spaces. We demonstrate that LiTEN achieves state-of-the-art accuracy on standard benchmarks, consistently outperforming leading approaches in both precision and speed. Furthermore, LiTEN-FF enables comprehensive modeling tasks, ranging from geometry optimization to free energy surface construction, with high computational efficiency for large biomolecules. This framework provides a physically grounded, versatile foundation for advanced biomolecular modeling and drug design applications.
Qun Su, Kai Zhu, Qiaolin Gou et al.· Nature Communications· 2 citations
Molecular dynamics simulations hold great promise for providing insight into the microscopic behavior of complex molecular systems. However, their effectiveness is often constrained by long timescales associated with rare events. Enhanced sampling methods have been developed to address these challenges, and recent years have seen a growing integration with machine learning techniques. This Review provides a comprehensive overview of how they are reshaping the field, with a particular focus on the data-driven construction of collective variables. Furthermore, these techniques have also improved biasing schemes and unlocked novel strategies via reinforcement learning and generative approaches. In addition to methodological advances, we highlight applications spanning different areas, such as biomolecular processes, ligand binding, catalytic reactions, and phase transitions. We conclude by outlining future directions aimed at enabling more automated strategies for rare-event sampling.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 54 citations
Targeting the intrinsically disordered N-terminal domain of the androgen receptor (AR-NTD) represents a promising strategy to overcome resistance in prostate cancer. However, its inherent lack of a stable tertiary structure and highly dynamic conformational ensemble pose formidable challenges for rational drug design. This study introduces an integrated computational workflow that combines enhanced sampling techniques and machine learning collective variables to identify druggable conformations of the AR-NTD and elucidate the binding mechanism of its modulator, EPI-002. We characterize nine metastable states of the Tau-5 region and reveal that ligand recognition is driven by π–π stacking and structured water-mediated hydrogen bonds. Leveraging these insights, we perform structure-based virtual screening based on the identified druggable conformations and identify K53, a rationally designed AR-NTD antagonist, which exhibits potent anti-proliferative activity in enzalutamide-resistant prostate cancer cells. K53 directly binds the AR-NTD, suppresses AR transcriptional activity, and demonstrates high selectivity for cancer cells. This work provides a rational design paradigm for targeting intrinsically disordered proteins and offers a therapeutic candidate for resistant prostate cancer. In this work, the authors develop a machine learning–based enhanced sampling workflow to target the intrinsically disordered AR-NTD, identifying druggable conformations and enabling transferable modeling of ligand binding for rational drug discovery.
Kai Zhu, Huating Wang, Jintu Zhang et al.· Nature Communications· 0 citations
The first comprehensive benchmarking framework specifically designed to accommodate inter-dataset heterogeneity is presented, finding that well-designed small datasets can match or even surpass the performance of larger benchmarks, suggesting that different metrics are applicable to different datasets/testing scenarios.
Yingjuan Cheng, Qing Ye, Linlong Jiang et al.· Journal of Cheminformatics· 0 citations
