While language models (LMs) have demonstrated remarkable general-purpose capabilities across domains, including molecule generation using line notations such as SMILES and SELFIES, their direct application to 3D structure design remains constrained by two interdependent challenges. First, the difficulty in designing a 3D line notation that ensures SE(3)-invariant atomic coordinates and supports autoregressive generation. Second, the incompatibility between continuous spatial coordinates and the discrete token inputs required by LMs. To address this, we propose Mol-StrucTok, a unified framework for tokenizing 3D molecular structures. Our approach comprises two key innovations: (1) a 3D line notation—Spherical Coordinate Notation—that encodes local atomic environments in spherical coordinates, agnostic to 2D notations and inherently SE(3)-invariant; and (2) a structure-aware Vector Quantized Variational Autoencoder (VQ-VAE) for discretizing these coordinates into chemically valid tokens suitable for language model processing. Leveraging this tokenization framework, we train a GPT-2 style model for end-to-end 3D molecular generation. Empirical results demonstrate strong, task-dependent performance: in unconditional generation, Mol-StrucTok achieves diffusion-level stability with ~28× faster inference; in conditional generation, it reduces property-matching mean absolute error (MAE) by 5–8× compared to diffusion-based methods, highlighting the advantage of autoregressive contextual modeling for precise control of molecular attributes. Our code is available at https://github.com/KyGao/Mol-StrucTok.

Molecular docking is a pivotal process in drug discovery. While traditional techniques rely on extensive sampling and simulation governed by physical principles, deep learning has emerged as a promising alternative, offering improvements in both accuracy and efficiency. Building upon the foundational work of FABind, a model focused on speed and accuracy, we introduce FABind+, an enhanced iteration that significantly elevates the performance of its predecessor. We identify pocket prediction as a critical bottleneck in molecular docking and introduce an enhanced approach. In addition to the pocket prediction module, the docking module has also been upgraded with permutation loss and a more refined model design. These designs enable the regression-based FABind+ to surpass most of the generative models. In contrast, while sampling-based models often struggle with inefficiency, they excel in capturing a wide range of potential docking poses, leading to better overall performance. To bridge the gap between sampling and regression docking models, we incorporate a simple yet effective sampling technique coupled with a lightweight confidence model, transforming the regression-based FABind+ into a sampling version without requiring additional training. This involves the introduction of pocket clustering to capture multiple binding sites and dropout sampling for various conformations. The combination of a classification loss and a ranking loss enables the lightweight confidence model to select the most accurate prediction. Experimental results and analysis demonstrate that FABind+ (both the regression and sampling versions) not only significantly outperforms the original FABind, but also achieves competitive state-of-the-art performance. Our code is available at https://github.com/QizhiPei/FABind.

Molecular docking plays a crucial role in predicting the binding mode of ligands to target proteins, and covalent interactions, which involve the formation of a covalent bond between the ligand and the target, are particularly valuable due to their strong, enduring binding nature. However, most existing docking methods and deep learning approaches hardly account for the formation of covalent bonds and the associated structural changes. To address this gap, we introduce a comprehensive benchmark for covalent docking, CovDocker, which is designed to better capture the complexities of covalent binding. We decompose the covalent docking process into three main tasks: reactive location prediction, covalent reaction prediction, and covalent docking. By adapting state-of-the-art models, such as Uni-Mol and Chemformer, we establish baseline performances and demonstrate the effectiveness of the benchmark in accurately predicting interaction sites and modeling the molecular transformations involved in covalent binding. These results confirm the role of the benchmark as a rigorous framework for advancing research in covalent drug design. It underscores the potential of data-driven approaches to accelerate the discovery of selective covalent inhibitors and addresses critical challenges in therapeutic development.

Antibodies are proteins that effectively protect the human body by binding to pathogens. Recently, deep learning-based computational antibody design has attracted popular attention since it automatically mines the antibody patterns from data that could be complementary to human experiences. However, the computational methods heavily rely on high-quality antibody structure data, which is quite limited. Besides, the complementarity-determining region (CDR), which is the key component of an antibody that determines the specificity and binding affinity, is highly variable and hard to predict. Therefore, the limited availability of high-quality antibody structure data exacerbates the difficulty of CDR generation. Fortunately, there is a large amount of sequence data for antibodies that can help model the CDR and reduce reliance on structure data. By witnessing the success of pre-training models for protein modeling, in this paper, we develop the antibody pre-training language model and incorporate it into the antigen-specific antibody design model in a systemic way. Specifically, we first pre-train a novel antibody language model based on the sequence data, then propose a one-shot way for sequence and structure generation of CDR to mitigate the high cost and error propagation associated with autoregressive methods, and finally leverage the pre-trained antibody model for the antigen-specific antibody generation model with some carefully designed modules. Our experiments demonstrate the superiority of our method over previous baselines in tasks such as sequence and structure generation, CDR-H3 design for antigen binding, and antibody optimization1. The code is available at https://github.com/KyGao/ABGNN.