human-computer interaction

SynGFN: learning across chemical space with generative flow-based molecular discovery

Allosteric Cooperativity Mechanism Investigation of Orthosteric and Allosteric Ligands in Modulating AR Activity: A Molecular Dynamics Study

The androgen receptor (AR) represents a pivotal therapeutic target for prostate cancer. However, existing orthosteric ligand-binding pocket (LBP) antagonists [e.g., enzalutamide (ENZ)] encounter significant obstacles due to resistance-conferring mutations in the LBP. Allosteric antagonists targeting the BF3 site exhibit great potential in overcoming such resistance but have low inhibitory efficacy. In our study, we employed an integrated computational modeling strategy, including Gaussian-accelerated molecular dynamics (GaMD), MM/GBSA free-energy calculations, and elastic network model (ENM)-based signaling communication pathway analyses. This approach is used to probe the cooperativity of allosteric BF3 antagonists [e.g., VPC-13808 (VPC)] with diverse orthosteric LBP ligands [e.g., ENZ and testosterone (TES)] in suppressing AR activity. Herein, four types of AR systems were examined: AR bound to LBP agonist (AR·TES), LBP antagonists (e.g., AR·ENZ), and combinations of LBP agonist/antagonist with BF3 antagonist (e.g., AR·TES·VPC and AR·ENZ·VPC). Results indicate that BF3 antagonists can synergize with the LBP antagonist to amplify conformational flexibility in H12 and induce anticorrelated dynamics of H12 with H3 and H4. This induces the downward movement of H12 and its displacement away from H3/H4, triggering the wide opening of the AF2 binding cleft and substantially reducing the coactivator recruitment. Furthermore, the BF3 antagonist can interact with specific residues (e.g., F673, F826, L830, and Y834) and cooperate with the LBP agonist or antagonist to allosterically perturb the AF2 conformation. Multiple short- and/or long-range BF3→AF2 and LBP→AF2 signaling transition pathways are involved, such as F673→Y834→L722→L812→L744→V746→L873→ENZ→L880/V889/V891. These mechanistic insights establish the foundation for developing novel AR BF3 antagonist and LBP-BF3 combination therapies, suggesting a promising avenue for enhancing the efficacy and overcoming the resistance in castration-resistant prostate cancer treatment.

ChargeNet: E(3) Equivariant Graph Attention Network for Atomic Charge Prediction

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.

PepPCBench is a Comprehensive Benchmarking Framework for Protein-Peptide Complex Structure Prediction

Accurate modeling of protein-peptide interactions is essential for understanding fundamental biological processes and designing peptide-based drugs. However, predicting the complex structures of these interactions remains challenging, primarily due to the high conformational flexibility of peptides. To support a fair and systematic evaluation of recent deep learning (DL) approaches, we introduce PepPCBench, a benchmarking framework tailored to assess protein folding neural networks (PFNNs) in protein-peptide complex prediction. As part of this framework, we curated PepPCSet, a data set of 261 experimentally resolved complexes with peptides ranging from 5 to 30 residues. We benchmark five full-atom PFNNs, including AlphaFold3 (AF3), AlphaFold-Multimer (AFM), Chai-1, HelixFold3 (HF3), and RoseTTAFold-All-Atom (RFAA), using comprehensive evaluation metrics. Our benchmarking reveals meaningful performance differences among these methods and highlights the influence of peptide length, conformational flexibility, and training set similarity on prediction accuracy. While AF3 shows strong performance in structure prediction, further analysis indicates that confidence metrics correlate poorly with experimental binding affinities, underscoring the need for improved scoring strategies and generalizability. By providing a reproducible and extensible framework, PepPCBench enables a robust evaluation of PFNN-based methods and supports their continued development for peptide-protein structure prediction.

Q‐GEM: Quantum Chemistry Knowledge Fusion Geometry‐Enhanced Molecular Representation for Property Prediction

Recently, various self‐supervised learning (SSL) methods based on 3D graph neural networks (GNNs) have been developed to comprehensively represent the structural information of molecules in 3D space; this is essential for discovering new drugs. However, existing methods fail to comprehensively characterize the 3D structures of molecules and neglect the electronic structural information that significantly influences key properties such as molecular reactivity, strong electrostatic interactions, and chemical adsorption. Therefore, here, a novel molecular representation learning method is constructed, Q‐GEM, incorporating quantum and geometric structural information enhancement, based on the quantum chemical property database QuanDB and SSL methods. Q‐GEM comprises a GNN embedded with the molecular electronic and complete 3D geometrical structural information as well as several well‐designed multiscale SSL tasks, achieving superior absolute molecular conformation prediction and conformational discrimination. The Q‐GEM achieved state‐of‐the‐art performance in 12 out of 13 prediction tasks on the MoleculeNet dataset, with an average performance improvement of 3.3% and 2.0% for classification and regression prediction tasks, respectively. Moreover, an average performance improvement of 5.2% is achieved in three localized quantum chemical properties, fully demonstrating the excellent performance of Q‐GEM in distinguishing molecular electronic structures. The Q‐GEM represents a novel, powerful breakthrough for accurate molecular property prediction.

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.

Macrocycle-DB: a comprehensive database for macrocycle-based drug discovery

Abstract Macrocycles have gained significant attention in drug design owing to their distinctive structural and physicochemical features. Despite the abundance of available experimental data, there remains a need for a centralized resource to support macrocycle-based drug discovery. Here, we present Macrocycle-DB, the most extensive online database dedicated to macrocycles, featuring 45 525 compounds, including 76 approved drugs and 105 clinical candidates that target 2533 proteins. The database offers comprehensive structural information, experimental bioactivity data, and physicochemical properties for each macrocycle, along with co-crystal structures to visualize protein–ligand interactions. Additionally, Macrocycle-DB provides specialized descriptors, scaffold and linker details for synthetic macrocycles, and high-quality downloadable datasets to facilitate computational drug design. Macrocycle-DB is freely accessible at https://macro-db.dpbio.tech/ and https://macro-db.cn/.

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/.

MetalloDock: Decoding Metalloprotein-Ligand Interactions via Physics-Aware Deep Learning for Metalloprotein Drug Discovery.

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.