computer vision

A Unified Deep Graph Model for Identifying the Molecular Categories of Ligands Targeting Nuclear Receptors

To fulfill functions for differentially regulating the downstream signaling pathways, functional ligands (i.e., agonists or antagonists) targeting nuclear receptors (NRs) are designed to stabilize different conformations (active or inactive) of the proteins. However, in practical applications, it is usually difficult to determine the molecular category of an NR ligand because these molecules all bind in the same location of an NR protein, namely, the ligand-binding pocket (LBP). Considering that ligands with different properties (agonists or antagonists) prefer to bind with differential conformations of NRs, it is possible to identify the molecular type of a given ligand through the differential binding environment (active or inactive conformations) of the protein-ligand interaction. Therefore, in this study, we established a unified model (NRIGN) based on the deep graphic architecture to discriminate agonists and antagonists targeting 26 successful or in-clinical-trial NR targets. Our result shows that NRIGN achieves an excellent prediction accuracy (ACC >0.95) and is robust enough to be applied in various real-world scenarios, such as predicting the molecular type of ligands in crystallized NR structures, ligands with multiple NR activities, and ligands with their types altered by target mutations. The proposed model is expected to promote rational design of drugs targeting NR proteins.

ECloudGen: leveraging electron clouds as a latent variable to scale up structure-based molecular design

Improving the predictive performance of binding affinities and poses for protein–cyclic peptide complexes through fine-tuned MM/PBSA(GBSA)-based methods

Abstract Cyclic peptides represent a highly promising class of biopharmaceutical scaffolds. The screening of cyclic peptides against protein targets can be greatly facilitated using computational approaches, especially molecular docking. However, it remains a crucial challenge to accurately predict protein–cyclic peptide (P–cp) interactions employing scoring functions of molecular docking. End-point approaches, such as molecular mechanics generalized Born surface area (MM/GBSA) and molecular mechanics Poisson–Boltzmann surface area (MM/PBSA), provide theoretically more robust frameworks than conventional scoring functions, but their reliability in predicting binding affinities and discriminating native-like binding poses for P–cp complexes remains poorly quantified. Herein, we comprehensively assessed the predictive abilities of MM/PBSA(GBSA) in scoring binding affinities of P–cp complexes and re-ranking their binding poses. The binding affinity scoring ability of MM/PBSA(GBSA) was assessed on a carefully curated dataset consisting of 50 complexes involving P–cp binding affinities, and their re-ranking capability was evaluated on another dataset consisting of the decoys of 81 P–cp complexes. Based on these assessments, we proposed a two-step workflow for predicting P–cp binding affinities. First, we employed the assessed optimal re-ranking method to select the top-1 binding pose; second, we estimated the binding affinity based on the selected top-1 pose using the assessed optimal scoring method. Our proposed workflow, which requires only 3 s for each prediction, achieves binding affinity predictions with a Rp of −0.732 when compared to experimental values, which is twice as high as that of AutoDock CrankPep (Rp = −0.316). This study emphasizes the necessity of using fine-tuned MM/PBSA(GBSA) methods for predicting P–cp interactions.

Revisiting Protein-Protein Docking: A Systematic Evaluation Framework

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.

NavDB: A Comprehensive Database for Voltage-Gated Sodium Channels Modulators and Targets

Voltage-gated sodium channels (VGSCs/Navs) are essential targets for the treatment of numerous neurological, muscular, and cardiac disorders. Despite the increasing clinical interest in subtype-selective modulators, current public databases provide fragmented and inconsistent information on VGSC-related compounds and targets, particularly lacking coverage on peptides. To address this limitation, we developed NavDB, a specialized and open-access database focusing on VGSC modulators and targets. NavDB integrates 8023 curated data records covering 5168 compounds, including small molecules, toxins, drugs, and peptides, along with comprehensive annotations on biological activity, druggability, and structural feature. NavDB also features advanced functions such as text-based and structure-based search, peptide similarity matching, and AI-powered property prediction. Moreover, the database offers high-quality 3D visualizations of targets and peptides, with disulfide bond and signal peptide annotations. All data are freely downloadable to support both experimental and computational drug discovery. NavDB is publicly available at: http://cadd.zju.edu.cn/navdb/.

STE-DC2I Uncovers Driver Genes in Colorectal Cancer Subtypes Using Symbolic Trajectory-Embedded Dark Causal Inference

Colorectal cancer (CRC) exhibits substantial molecular heterogeneity, necessitating the inference of subtype-specific driver genes and their interactions for drug-target discovery and precision oncology. Prior studies often fail to capture subtle, latent nonlinear regulatory mechanisms (dark causal relationships) driving tumor progression in specific subtypes. Here, we develop an explainable intelligence computational framework, Symbolic Trajectory-Embedded Dark Causal Interaction Inference (STE-DC2I), which combines symbolic trajectory embedding with historical prediction mechanisms to model nonmonotonic oscillatory dependencies between genes. Integrating single-cell transcriptomic and multiomics profiles from malignant epithelial subpopulations, STE-DC2I classifies CRC subtypes, reconstructs developmental trajectories, and uncovers interpretable subtype-specific driver genes with functional relevance. Unlike correlation-based and explicit causal approaches, STE-DC2I captures weak yet biologically critical regulatory signals, outperforming state-of-the-art methods in predicting subtype-specific CRC driver genes. Functional assays in CRC cell lines (in vitro) validated nine predicted driver genes, highlighting their therapeutic potential.This work systematically explores dark causal interactions between genes in CRC subtypes. STE-DC2I offers interpretable insights and a generalizable strategy for CRC drug-target discovery.

Understanding the Kinetic Mechanism of Ligands Stabilizing the RAS-CYPA Interaction

Molecular glues, including protein degraders and protein-protein interaction (PPI) stabilizers, have emerged as a new paradigm of drug design for regulating interactions between biomacromolecules; yet it is still a challenge for rational design of molecular glues. KRAS, as a prevalent oncogenic driver, is notoriously difficult to target by traditional small molecular drugs due to its challenging binding surface and frequent mutations. Although the small molecular drug RMC7977 has been designed as a PPI stabilizer for stabilizing the inherently weak RAS-CYPA interaction, the precise molecular mechanism underlying its stabilization effect and selectivity difference requires a deeper understanding. To this end, we leverage an integrated computational strategy combining molecular dynamics (MD) simulation, end-point binding free-energy calculation, and enhanced sampling technologies to elucidate the dynamic characteristics of RAS-ligand-CYPA interactions. Our result exhibits a high correlation between the predicted binding affinities and the experimental observations, demonstrating that RMC7977, acting as a strong PPI stabilizer, significantly enhances the stability of the KRAS-CYPA interaction, where, by delicately remodeling the protein-protein interface, the drug optimizes various interactions. Moreover, the results also uncover the dynamic process of stabilizer-mediated KRAS-CYPA stabilization and the mechanistic origin of the binding selectivity. This study provides essential molecular-level insights into RMC7977's function and offers a valuable computational framework for evaluating the stabilization effect of ligands targeting the KRAS-CYPA and other challenging PPI systems.

Comprehensive Assessment and Benchmark of Deep Generative Models for Proteolysis TArgeting Chimera (PROTAC) Design

DRHIN: An Integrated and Interactive Web Server for Drug Repositioning

Drug repositioning (DR) identifies new therapeutic uses for approved drugs, reducing development burdens and offering safer treatment options for patients. While high-throughput technologies generate complex, large-scale multiomics data, existing DR tools struggle to comprehensively analyze the resulting biological networks. To address this challenge, we present DRHIN, an integrated, interactive web server for DR over heterogeneous information networks (HINs) using advanced deep learning techniques. DRHIN integrates transcriptomics, proteomics, and microbiome data, incorporating eight biological entities and 19 association types to build diverse HINs and elucidate the underlying molecular mechanisms. It includes 19 state-of-the-art graph representation algorithms, enabling flexible training, comparison, and evaluation of heterogeneous network data. The platform provides a code-free portal supporting three key predictive tasks: discovering drug-disease associations, repurposing existing drugs for new indications, and identifying potential therapies for specific diseases, making analyses accessible and reproducible. Leveraging high-performance computing, DRHIN efficiently processes million-scale networks, ensuring practical applicability in real-world scenarios. The web server is freely accessible at http://drhin.tianshanzw.cn.

A scalable and quantum-accurate foundation model for biomolecular force fields via linearly tensorized quadrangle attention

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.

Unified and explainable molecular representation learning for imperfectly annotated data from the hypergraph view

Molecular representation learning (MRL) has shown promise in accelerating drug development by predicting chemical properties. However, imperfectly annotation among datasets pose challenges in model design and explainability. In this work, we formulate molecules and corresponding properties as a hypergraph, extracting three key relationships: among properties, molecule-to-property, and among molecules, and developed a unified and explainable multi-task MRL framework, OmniMol. It integrates a task-related meta-information encoder and a task-routed mixture of experts (t-MoE) backbone to capture correlations among properties and produce task-adaptive outputs. To capture underlying physical principles among molecules, we implement an innovative SE(3)-encoder for physical symmetry, applying equilibrium conformation supervision, recursive geometry updates, and scale-invariant message passing to facilitate learning-based conformational relaxation. OmniMol achieves state-of-the-art performance in properties prediction, reaches top performance in chirality-aware tasks, demonstrates explainability for all three relations, and shows effective performance in practical applications. Our code is available in our https://github.com/bowenwang77/OmniMol public repository. AI models for drug discovery often struggle with real-world, incomplete data. Here, the authors present OmniMol, a framework using hypergraphs to improve predictions of molecular properties, addressing challenges of imperfect data annotation and enhancing model explainability.

RSGPT: a generative transformer model for retrosynthesis planning pre-trained on ten billion datapoints

Retrosynthesis planning is a crucial task in organic synthesis, and deep-learning methods have enhanced and accelerated this process. With the advancement of the emergence of large language models, the demand for data is rapidly increasing. However, available retrosynthesis data are limited to only millions. Therefore, we pioneer the utilization of the template-based algorithm to generate chemical reaction data, resulting in the production of over 10 billion reaction datapoints. A generative pretrained transformer model is subsequently developed for template-free retrosynthesis planning by pre-training on 10 billion generated data. Inspired by the strategies of large language models, we introduce reinforcement learning to capture the relationships among products, reactants, and templates more accurately. Experiments demonstrate that our model achieves state-of-the-art performance on the benchmark, with a Top-1 accuracy of 63.4%, substantially outperforming previous models. Computer-aided synthesis-planning methods have significantly assisted synthesis planning. In this work, the authors present RSGPT, a generative model pre-trained on ten billion data points, achieving state-of-the-art performance for synthesis planning