Atomic charge is crucial in drug design for analyzing reactive sites and interactions between ligands and targets. While quantum mechanical methods offer high accuracy, they are generally computationally costly. Conversely, empirical approaches, while computationally efficient, frequently suffer from lack of precision and generalizability. Recent a number of machine learning-based models have been developed for atomic charge predictions, but they struggle with accurately representing molecular structures and capturing the chemical environments affecting atomic charges, thus limiting their generalization and accuracy. To overcome these limitations, we propose LumiCharge, a novel atomic charge prediction framework that incorporates high-order spherical harmonics convolutions and explicitly models multibody interactions. In constructing this model, we employ a strategy that integrates both high- and low-order information, enhancing its geometric spatial perception capability, which is currently underexplored in the field. Benchmark evaluations demonstrate that LumiCharge outperforms state-of-the-art (SOTA) models by 30%-60% across diverse data sets. Additionally, in cross-scale experiments, LumiCharge demonstrates exceptional extrapolation capability and robustness across molecules of varying sizes, effectively overcoming the limitations imposed by molecular sizes. On an external halogen-containing test set, LumiCharge achieves an RMSE of 0.055e, meeting practical application requirements. Finally, a case study of virtual screening for the androgen receptor (AR) target further validates its outstanding accuracy compared to the OPLS3e force field and other deep learning (DL)-based baseline models, highlighting its exceptional generalization capacity and practical utility in real-world scenarios.
Qun Su, Hui Zhang, Qiaolin Gou et al.· Journal of Physical Chemistr...· 2 citations
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
The integration of large language models (LLMs) into drug design is gaining momentum; however, existing approaches often struggle to effectively incorporate three-dimensional molecular structures. Here, we present Token-Mol, a token-only 3D drug design model that encodes both 2D and 3D structural information, along with molecular properties, into discrete tokens. Built on a transformer decoder and trained with causal masking, Token-Mol introduces a Gaussian cross-entropy loss function tailored for regression tasks, enabling superior performance across multiple downstream applications. The model surpasses existing methods, improving molecular conformation generation by over 10% and 20% across two datasets, while outperforming token-only models by 30% in property prediction. In pocket-based molecular generation, it enhances drug-likeness and synthetic accessibility by approximately 11% and 14%, respectively. Notably, Token-Mol operates 35 times faster than expert diffusion models. In real-world validation, it improves success rates and, when combined with reinforcement learning, further optimizes affinity and drug-likeness, advancing AI-driven drug discovery. In this work the authors present Token-Mol, a token-only 3D drug design model, which deploys the Gaussian cross-entropy (GCE) loss function for regression tasks. It exhibits superior performance in molecular conformation generation, property prediction, and pocket-based generation, thus opening up new avenues for drug design.
Jike Wang, Rui Qin, Mingyang Wang et al.· Nature Communications· 30 citations· ⚡1
High-quality biological databases are the bedrock of data-driven scientific discovery. However, the construction of these resources remains a labor-intensive bottleneck, particularly for emerging research frontiers where structured data is non-existent. While LLM-based agents have catalyzed progress in downstream scientific modeling, their potential to automate the critical upstream challenge of database curation remains largely untapped. To bridge this gap, we introduce BioDataLab, a rigorous benchmark comprising 100 tasks meticulously derived from 57 high-impact database publications. BioDataLab evaluates the capability of autonomous agents to transform raw, heterogeneous biological resources into structured, analysis-ready databases. Unlike static evaluations, BioDataLab provides a fully interactive environment encompassing data retrieval, extraction, annotation, and integration, featuring process-oriented curation targets and contamination-control checks. We benchmark 11 state-of-the-art LLMs (including Gemini-3.0, GPT-5.2, and Claude-4.5) under different agent frameworks, revealing a substantial capability gap: the top-performing model achieves only a 40% success rate. Further error analysis identifies significant bottlenecks in multi-step tool orchestration and adherence to complex biological data formats. These findings underscore that while LLMs are proficient in downstream reasoning, autonomous upstream curation remains a formidable frontier. All data and codes are available at GitHub.
Jiaxian Yan, Xi Fang, Jintao Zhu et al.· Proceedings of the 32nd ACM...· 0 citations