Dejun Jiang
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Articles by Dejun Jiang
Discovery of ERα-Targeting Phytochemicals With In Vitro Cytotoxicity and Computational Prediction of Y537S Mutant Inhibition
1 Introduction Breast cancer is considered the most frequently diagnosed malignancy in women today (Arnold et al. 2022; Bray et al. 2024; Siegel et al. 2025). This type of cancer is highly associated with germline variants, including BRCA1, BRCA2, and estrogen-related signaling pathways (Ferreyra et al. 2023). Over 70% of breast cancer tumors express a ligand-activated hormone receptor, estrogen receptor α (ERα) (Porras et al. 2021).
Machine learning prediction of pediatric adverse drug reactions using consensus-derived scarce data
11 hours ago Whether you just got your first Mac or upgraded to a brand-new machine, it’s always helpful to get started on the right set of tools. Here are the very first apps I usually install as soon as I get a new Mac. I track every single second I spend working. I have been doing it for years, and this has …
Machine learning prediction of pediatric adverse drug reactions using consensus-derived scarce data - Communications Chemistry
Abstract Adverse drug reactions (ADRs) represent a significant cause of morbidity and mortality in children, who face distinct pharmacological vulnerabilities due to unique physiological development. Current pediatric drug safety research is hindered by limited clinical data and adult-focused studies, creating evidence gaps.
A virtual platform for automated hybrid organic-enzymatic synthesis planning - Nature Communications
Abstract The integration of organic synthesis with enzymatic catalysis offers a promising route toward efficient and sustainable construction of complex molecules. While organic synthesis enables diverse transformations, enzymatic catalysis enhances stereoselectivity under mild conditions, improving cost-effectiveness and environmental impact. However, current enzymatic synthesis planning algorithms face challenges in formulating robust hybrid organic–enzymatic strategies.
A fused deep learning approach to transform drug repositioning - Communications Chemistry
Abstract Drug repositioning holds promise for discovering new therapeutic applications for existing drugs, accelerating drug development and reducing associated costs. However, current methodologies encounter difficulties in managing diverse network representations, tackling cold start issues, and handling intrinsic attribute representations.
Protein–peptide docking with a rational and accurate diffusion generative model - Nature Machine Intelligence
Abstract Therapeutic peptides represent the forefront of drug discovery, offering potent and safe alternatives to traditional small molecules. However, their weak and context-dependent nature complicates the efficient virtual screening and structural characterization of protein–peptide patterns. Here we introduce RAPiDock, a diffusion generative model designed for rational, accurate and rapid protein–peptide docking at an all-atomic level.
Token-Mol 1.0: tokenized drug design with large language models - Nature Communications
Abstract 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.
A Comparative Molecular Dynamics Study of Food-Derived Compounds as PD-L1 Inhibitors: Insights Across Six Flavonoid Subgroups
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Leveraging language model for advanced multiproperty molecular optimization via prompt engineering - Nature Machine Intelligence
Abstract Optimizing a candidate molecule’s physiochemical and functional properties has been a critical task in drug and material design. Although the non-trivial task of balancing multiple (potentially conflicting) optimization objectives is considered ideal for artificial intelligence, several technical challenges such as the scarcity of multiproperty-labelled training data have hindered the development of a satisfactory AI solution for a long time.
Multi-modal deep learning enables efficient and accurate annotation of enzymatic active sites - Nature Communications
Abstract Annotating active sites in enzymes is crucial for advancing multiple fields including drug discovery, disease research, enzyme engineering, and synthetic biology. Despite the development of numerous automated annotation algorithms, a significant trade-off between speed and accuracy limits their large-scale practical applications.
PPFlow: Target-aware Peptide Design with Torsional Flow Matching
Abstract Therapeutic peptides have proven to have great pharmaceutical value and potential in recent decades. However, methods of AI-assisted peptide drug discovery are not fully explored. To fill the gap, we propose a target-aware peptide design method called PPFlow, based on conditional flow matching on torus manifolds, to model the internal geometries of torsion angles for the peptide structure design.
Comprehensive Evaluation of 10 Docking Programs on a Diverse Set of Protein-Cyclic Peptide Complexes
Download Hi-Res ImageDownload to MS-PowerPointCite This:J. Chem. Inf. Model. 2024, XXXX, XXX Terms & Conditions Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses).
A flexible data-free framework for structure-based de novo drug design with reinforcement learning
A flexible data-free framework for structure-based de novo drug design with reinforcement learning† Contemporary structure-based molecular generative methods have demonstrated their potential to model the geometric and energetic complementarity between ligands and receptors, thereby facilitating the design of molecules with favorable binding affinity and target specificity.
Learning on topological surface and geometric structure for 3D molecular generation - Nature Computational Science
Abstract Highly effective de novo design is a grand challenge of computer-aided drug discovery. Practical structure-specific three-dimensional molecule generations have started to emerge in recent years, but most approaches treat the target structure as a conditional input to bias the molecule generation and do not fully learn the detailed atomic interactions that govern the molecular conformation and stability of the binding complexes.
Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes
Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA–ligand complexes† Ribonucleic acid (RNA)–ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets.
Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking - Nature Communications
Datasets In order to verify the effectiveness of SME in explaining the predictions from a GNN model, we conduct experiments on four different molecular property datasets, concerning aqueous solubility (ESOL), mutagenicity, hERG-related cardiotoxicity and blood-brain barrier permeation (BBBP). We briefly give a short introduction to these four properties. Aqueous solubility is essential for drug candidates and is one of the key physical properties of interest for medicinal chemists40.
Landslide Monitoring along the Dadu River in Sichuan Based on Sentinel-1 Multi-Temporal InSAR
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Infinite Physical Monkey: Do Deep Learning Methods Really Perform Better in Conformation Generation?
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MetalProGNet: A Structure-based Deep Graph Model for Metalloprotein-Ligand Interaction Predictions
MetalProGNet: A Structure-based Deep Graph Model for Metalloprotein-Ligand Interaction Predictions Dejun Jiang, Zhaofeng Ye, Chang-Yu Hsieh, Zhiyi Yang, Xujun Zhang, Yu Kang, Hongyan Du, Zhenxing Wu, Jike Wang, Yundian Zeng, Haotian Zhang, Xiaorui Wang, Mingyang Wang, Xiaojun Yao, Shengyu Zhang, Jian Wu and Tingjun Hou Abstract Metalloproteins play indispensable roles in various biological processes ranging from reaction catalyzing to free radicals scavenging, and it is also pertinent to...
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