Article
Biochemistry & Molecular Biology
Xiao-Yan Feng, Ting-Ting Ding, Ya-Ya Liu, Wei-Ren Xu, Xian-Chao Cheng
Summary: The development of dual PPAR agonists for the treatment of type 2 diabetes has gained attention, as they can improve metabolism and reduce side effects. Through virtual screening, molecular docking, and ADMET prediction, a representative compound with higher docking score and lower toxicity was obtained, showing promise as an effective treatment.
JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
(2021)
Article
Biochemistry & Molecular Biology
Yue Li, Mengjia Lv, Meiling Shen, Xi Gu, Li Zhang, Xingyong Liu, Jing Chen, Likun Gong, Zhili Zuo
Summary: Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, characterized by dysregulated glucose homeostasis and lipid accumulation. PPAR alpha has been identified as a potential target for NAFLD treatment, and new 3H-benzo[b][1,4]diazepine PPAR alpha agonists were discovered using pharmacophore modeling, molecular docking, and bioassays. Compound LY-2 showed promising results in promoting the expression of PPAR alpha downstream gene and may serve as a novel lead compound for the development of potent PPAR alpha agonists.
JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
(2023)
Article
Biochemistry & Molecular Biology
Satvik Kotha, B. Swapna, Vithal M. Kulkarni, S. Ramachandra Setty, B. Harish Kumar, R. Harisha
Summary: Alzheimer's Disease is a complex neurodegenerative disorder characterized by neurofibrillary tangles and senile plaques in the brain. PPAR-gamma plays an important role in improving cognitive function in AD by reducing A beta synthesis and inhibiting neuro-inflammation. However, current PPAR-gamma agonists have limited clinical use due to poor blood-brain barrier permeability, hence the need for new agonists with improved BBB penetration ability.
JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
(2021)
Article
Biochemistry & Molecular Biology
Chetna Kharbanda, Mohammad Sarwar Alam, Hinna Hamid, Yakub Ali, Syed Nazreen, Abhijeet Dhulap, Perwez Alam, M. A. Q. Pasha
Summary: The study synthesized forty-eight molecules derived from arylpmpionic acid scaffold, showing good anti-diabetic activity. These molecules displayed excellent dock scores against PPAR-gamma receptor site, indicating the potential for developing anti-diabetic agents with fewer side effects.
BIOORGANIC CHEMISTRY
(2021)
Article
Biochemistry & Molecular Biology
Muhammad Harith Zulkifli, Zafirah Liyana Abdullah, Nur Intan Saidaah Mohamed Yusof, Fazlin Mohd Fauzi
Summary: This article summarizes three in silico approaches for toxicity studies of Traditional Chinese medicine (TCM) herbal medicine, including machine learning, network toxicology, and molecular docking. Although these methods can provide data-driven toxicity prediction validated in vitro and/or in vivo, they are limited to single compound analysis and specific types of toxicity. Future studies should involve testing combinations of compounds to improve in silico toxicity modeling of TCM compounds.
CURRENT OPINION IN STRUCTURAL BIOLOGY
(2023)
Article
Biochemistry & Molecular Biology
Juan Sun, Han-Yu Liu, Yi-Heng Zhang, Ze-Yu Fang, Peng-Cheng Lv
Summary: A series of novel TZD analogues were developed, among which 4 g showed promising anti-hyperglycemic and anti-inflammatory activities, potentially serving as a PPAR gamma agonist deserving further investigation.
BIOORGANIC CHEMISTRY
(2021)
Article
Clinical Neurology
Song Li, Zhengzhi Wu, Weidong Le
Summary: In Western medicine, dementia affects cognition, mental health, and physical abilities, while Traditional Chinese Medicine explains it from the perspectives of brain dystrophy, Spleen-Kidney weakness, Blood stasis, and Phlegm stagnation. Ancient Chinese physicians believed that dementia manifests not only as cognitive symptoms but also as psychiatric disorders and sleep disturbance, offering various treatment methods.
ALZHEIMERS & DEMENTIA
(2021)
Article
Chemistry, Medicinal
Zongtao Zhou, Qiang Ren, Shixuan Jiao, Zongyu Cai, Xinqian Geng, Liming Deng, Bin Wang, Lijun Hu, Luyong Zhang, Ying Yang, Zheng Li
Summary: A new quadruple FFA1/PPAR-alpha/gamma/delta agonist, ZLY18, with longer metabolic half-life and stronger lipid-lowering effects, has been discovered for the treatment of NAFLD and NASH. It also exhibits stronger regulation on genes related to lipid synthesis, oxidative stress, inflammation, and fibrosis.
EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY
(2022)
Article
Biochemistry & Molecular Biology
Shahid Ali, Khurshid Ahmad, Sibhghatulla Shaikh, Jeong Ho Lim, Hee Jin Chun, Syed Sayeed Ahmad, Eun Ju Lee, Inho Choi
Summary: This study identified two compounds with high binding affinity and specificity from a Traditional Chinese Medicine database that may have the potential to inhibit the myostatin protein and increase myogenesis in skeletal muscle tissues.
Review
Pharmacology & Pharmacy
Lei Li, Man Wang, Jikuai Chen, Juelin Chen, Yawei Wang, Minghao Zhao, Qing Song, Shuogui Xu
Summary: As global warming continues, heat waves are becoming more frequent and intense, leading to an increase in the incidence of heat stroke. Heat stroke is associated with significant morbidity and mortality, and further research is urgently needed to address this issue. Traditional Chinese medicine offers potential treatment methods, including herbal therapies and external treatments, which may provide clinical benefits and research directions for heat stroke.
FRONTIERS IN PHARMACOLOGY
(2023)
Article
Pharmacology & Pharmacy
Dongna Li, Jing Hu, Lin Zhang, Lili Li, Qingsheng Yin, Jiangwei Shi, Hong Guo, Yanjun Zhang, Pengwei Zhuang
Summary: Multi-Ingredient-Based interventions are increasingly recognized as advantageous for complex diseases compared to single-target therapy. However, the combination rules and mechanisms of these interventions remain unclear, and there is a need for more powerful strategies to interpret their synergistic effects. Artificial intelligence (AI) and computer vision have rapidly expanded in the field of Traditional Chinese Medicine (TCM), improving diagnostics and drug research. This review focuses on the application of AI in herb quality evaluation, drug target discovery, optimized compatibility, and medical diagnoses of TCM.
EUROPEAN JOURNAL OF PHARMACOLOGY
(2022)
Article
Biochemistry & Molecular Biology
Terukazu Kato, Takafumi Ohara, Naoyuki Suzuki, Noriyuki Naya, Keita Fukao, Ryukou Tokuyama, Susumu Muto, Hiroshi Fukasawa, Akiko Itai, Ken-ichi Matsumura
Summary: A novel PPARd agonist 5g with high selectivity ratio and strong agonist activity was discovered through further optimization study of compound 1. Additionally, 5g showed significant upregulation of high-density lipoprotein cholesterol level in vivo.
BIOORGANIC & MEDICINAL CHEMISTRY
(2023)
Article
Plant Sciences
Wei Zhuang, Shao-Li Liu, Sheng-Yan Xi, Ying-Nan Feng, Ke Wang, Teliebald Abduwali, Ping Liu, Xiao-Jiang Zhou, Lan Zhang, Xian-Zhe Dong
Summary: This article summarizes the frequently used traditional Chinese medicine decoctions and Chinese patent medicines for treating depression, reviews their clinical therapeutic effects and possible mechanisms, and highlights the importance of traditional Chinese medicine in the management of depression.
JOURNAL OF ETHNOPHARMACOLOGY
(2023)
Article
Pharmacology & Pharmacy
Baoyue Zhang, Jun Zhao, Zhe Wang, Pengfei Guo, Ailin Liu, Guanhua Du
Summary: AD is a neurodegenerative disease with no cure currently available. A multi-target approach using Chinese herbal compounds was used to predict and validate potential anti-AD drugs, leading to the discovery of 12 compounds with promising activity against AD.
FRONTIERS IN PHARMACOLOGY
(2021)
Review
Pharmacology & Pharmacy
Xue Bai, Meng Zhang
Summary: Research has shown that traditional Chinese medicine has a clinical impact on vascular dementia, improving patients' cognitive function and quality of life. The pharmacological mechanisms of TCM for VD treatment include targeting the kidneys, eliminating turbidity, and promoting blood circulation, indicating promising prospects.
FRONTIERS IN PHARMACOLOGY
(2021)
Review
Chemistry, Physical
Yang Zhao, Jiazhao Huang, Jianqiang Chen, Youwen Liu, Tianyou Zhai
Summary: Two-dimensional transition metal dichalcogenides (TMDs) have shown excellent catalytic performance for hydrogen evolution and are considered suitable substitutes for commercial Pt-based catalysts. Chemical vapor deposition (CVD) is an important technique for synthesizing controllable and high-purity TMDs for electrocatalysis and electronic devices. Recent research progress in CVD-grown TMDs nanosheets for electrocatalytic hydrogen evolution, including synthesis factors and engineering strategies, has been presented in this review.
Article
Chemistry, Physical
Dingbo Chen, Yu-Chang Chen, Guang Zeng, Yu-Chun Li, Xiao-Xi Li, Dong Li, Chao Shen, Nan Chi, Boon S. Ooi, David Wei Zhang, Hong-Liang Lu
Summary: We designed a chip-scale UV photodiode based on a hybrid GaN heterojunction, which can switch the photocurrent direction at different wavelengths using photocurrent switching effect. The device exhibits high responsivity in the UV range and demonstrates special logic operations.
Article
Computer Science, Artificial Intelligence
Xuedong He, Calvin Yu-Chian Chen
Summary: Most end-to-end discriminative trackers aim to improve performance by mining more representative target features, but it is challenging to obtain comprehensive features due to diverse challenges. To address this issue, we propose a novel feature enhancement module for richer and comprehensive feature representation, which is trainable end-to-end. Additionally, we use a metric learning method to devise a verifier for adaptive update of the sample memory and learning rate.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Chemistry, Physical
Ziduo Yang, Weihe Zhong, Qiujie Lv, Tiejun Dong, Calvin Yu -Chian Chen
Summary: This paper proposes a geometric interaction graph neural network (GIGN) that incorporates 3D structures and physical interactions for predicting protein-ligand binding affinities. GIGN achieves state-of-the-art performance on three external test sets, and the learned representations of protein-ligand complexes are biologically meaningful.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
(2023)
Article
Nanoscience & Nanotechnology
Kazi Hasibur Rahman, Asit Kumar Kar, Kuan-Chung Chen, Ching-Jung Chen
Summary: This study focuses on the synthesis of Fe3+ doped TiO2 nanoparticles with different molar concentrations of Fe3+ and their potential use as photocatalysts. The photocatalysts were thoroughly characterized using various analytical techniques to determine their morphological, chemical, structural, and optical properties. The presence of Fe3+ ions resulted in a red shift phenomenon, indicating the transfer of charges between the dopant and TiO2. Experimental results showed that Fe3+ ions regulate the catalytic property of TiO2 nanomaterials, and achieved high degradation efficiency rates for Methylene Blue and Malachite Green Oxalate under visible light irradiation.
Article
Multidisciplinary Sciences
Weihe Zhong, Ziduo Yang, Calvin Yu-Chian Chen
Summary: The authors develop a graph-to-edits framework called Graph2Edits for retrosynthesis prediction based on graph neural network. This framework combines the two-stage processes of semi-template-based methods, improving the applicability and interpretability of predictions. Evaluated on the USPTO-50k dataset, the model achieves state-of-the-art performance for semi-template-based retrosynthesis with a promising 55.1% top-1 accuracy.
NATURE COMMUNICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Xuedong He, Calvin Yu-Chian Chen
Summary: This paper proposes a lightweight feature separation and fusion module to improve the performance of discriminative trackers. Additionally, a target uncertain detection technique is designed to address the problem of tracking model corruption. Through comprehensive experimental evaluations, the results demonstrate the excellent performance of the proposed methods on seven public benchmarks.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Qiujie Lv, Jun Zhou, Ziduo Yang, Haohuai He, Calvin Yu-Chian Chen
Summary: Understanding drug-drug interactions (DDI) is crucial for minimizing adverse drug reactions. The modeling of new drugs in a cold start scenario is challenging due to limited structural and physicochemical information. A 3D graph neural network with few-shot learning, Meta3D-DDI, is proposed to predict DDI events. It incorporates 3D structure and distance information and develops a strategy to transfer meta-knowledge for DDI prediction tasks.
Article
Business, Finance
Jianqiang Chen, Pei-Fang Hsieh, Kun Wang
Summary: This study examines the impact of infringement and counterfeiting on corporate innovation performance. A quasi-natural experiment is used to analyze the effects of the Chinese government's crackdown on intellectual property rights violations. The results show that after the implementation, companies in industries with high risk of intellectual property violations or imitation experience a significant increase in patent counts and citations, especially for financially strong and risk-taking firms. This event leads to increased R&D investment and a prioritization of invention patents over utility patents by firms.
FINANCE RESEARCH LETTERS
(2023)
Article
Chemistry, Multidisciplinary
Qiujie Lv, Guanxing Chen, Haohuai He, Ziduo Yang, Lu Zhao, Hsin-Yi Chen, Calvin Yu-Chian Chen
Summary: Traditional Chinese Medicine (TCM) is a valuable resource for modern drug discovery. However, two challenges exist in applying AI-assisted drug discovery (AIDD) to guide TCM drug discovery: lack of standardized TCM-related information and AIDD's susceptibility to failures in out-of-domain data. To address these challenges, TCMBank, the largest systematic free TCM database, was developed as an extension of TCM Database@Taiwan. TCMBank provides comprehensive information on herbs, ingredients, targets, and diseases, and enables researchers to identify potential leads and drug repurposing through an ensemble learning-based drug discovery protocol. It offers a valuable tool for accelerating drug discovery using artificial intelligence.
Letter
Biochemistry & Molecular Biology
Qiujie Lv, Guanxing Chen, Haohuai He, Ziduo Yang, Lu Zhao, Kang Zhang, Calvin Yu-Chian Chen
SIGNAL TRANSDUCTION AND TARGETED THERAPY
(2023)
Article
Computer Science, Artificial Intelligence
Qiujie Lv, Guanxing Chen, Ziduo Yang, Weihe Zhong, Calvin Yu-Chian Chen
Summary: Finding candidate molecules with favorable pharmacological activity, low toxicity, and proper pharmacokinetic properties is an important task in drug discovery. Deep neural networks have made impressive progress in accelerating and improving drug discovery, but they rely on a large amount of label data. Applying deep neural networks for low-data drug discovery is still a challenge.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Chemistry, Physical
Ziduo Yang, Weihe Zhong, Qiujie Lv, Tiejun Dong, Calvin Yu-Chian Chen
Summary: This paper proposes a geometric interaction graph neural network (GIGN) that incorporates 3D structures and physical interactions for predicting protein-ligand binding affinities. GIGN achieves state-of-the-art performance on three external test sets and its predictions are shown to be biologically meaningful through visualization of learned representations of protein-ligand complexes.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Yiyang Yin, Shuangling Luo, Jun Zhou, Liang Kang, Calvin Yu-Chian Chen
Summary: Medical image segmentation is crucial for modern healthcare systems, especially in reducing surgical risks and planning treatments. Transanal total mesorectal excision (TaTME) has become an important method for treating colon and rectum cancers. Real-time instance segmentation during TaTME surgeries can assist surgeons in minimizing risks. However, the dynamic variations in TaTME images pose challenges for accurate instance segmentation.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.