Article
Engineering, Biomedical
Yixuan Lin, Yiqi Yang, Kai Yuan, Shengbing Yang, Shuhong Zhang, Hanjun Li, Tingting Tang
Summary: This study reports a three-dimensional bioprinted osteosarcoma model that incorporates osteosarcoma cells and a mimicked extracellular matrix. Compared to traditional models, this model shows significant differences in cell cycle, metabolism, and other cellular pathways, and is more sensitive to therapies targeting the autophagy pathway.
BIOACTIVE MATERIALS
(2022)
Article
Pharmacology & Pharmacy
Peng Li, Chujie Bai, Lingmin Zhan, Haoran Zhang, Yuanyuan Zhang, Wuxia Zhang, Yingdong Wang, Jinzhong Zhao
Summary: Identifying the biological targets of a compound is crucial for understanding drug mechanisms and developing new drugs. The Connectivity Map concept connects genes, drugs, and diseases based on gene-expression signatures. However, existing methods are inefficient due to the need for reference drugs. In this study, we developed a procedure to extract target-induced gene modules and identified target gene clusters. Additionally, we proposed a gene module pair-based approach to predict novel compound-target interactions, leading to the discovery of new inhibitors for PI3K pathway proteins.
FRONTIERS IN PHARMACOLOGY
(2023)
Article
Biochemical Research Methods
Liang Yu, Mingfei Xia, Qi An
Summary: In this paper, a Network Embedding frameWork in MultIplex Network (NEWMIN) is proposed to predict synthetic drug combinations. By integrating information from multiple networks and determining their importance, several novel drug combinations have been discovered, with better performance compared to other methods.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Pharmacology & Pharmacy
Tianduanyi Wang, Otto I. Pulkkinen, Tero Aittokallio
Summary: Most drug molecules have the ability to modulate multiple target proteins, which can lead to both therapeutic effects and unwanted side effects. Evaluating the selectivity of a compound is an important factor in drug development and repurposing efforts. Traditional methods for characterizing selectivity fall short in quantifying how selective a compound is against a particular target protein. In this study, we propose an optimization-based selectivity scoring method that allows for the identification of potent and selective compounds against given kinase targets. We demonstrate the effectiveness of this method in finding highly selective compounds in computational experiments using a large-scale kinase inhibitor dataset.
FRONTIERS IN PHARMACOLOGY
(2022)
Review
Chemistry, Medicinal
Xing-Jie Dai, Lei-Peng Xue, Shi-Kun Ji, Ying Zhou, Ya Gao, Yi-Chao Zheng, Hui-Min Liu, Hong-Min Liu
Summary: In recent years, triazole-fused pyrimidines, a widely-used class of heterocycles in medicinal chemistry, have shown potential as anticancer agents that target various cancer-associated targets. This review focuses on the latest advancements in triazole-pyrimidines as target-based anticancer agents, published between 2007 and 2022. The review also discusses the structure-activity relationships (SARs) and multiple pathways to facilitate the development of more effective and biotargeted anticancer candidates.
EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY
(2023)
Article
Chemistry, Medicinal
Jingxing Wu, Yi Xiao, Mujie Lin, Hanxuan Cai, Duancheng Zhao, Yirui Li, Hailin Luo, Chuanqi Tang, Ling Wang
Summary: In this study, a total of 832 classification models were constructed using the FP-GNN deep learning method, based on the collection of 485,900 compounds and their bioactivity records. These models showed considerable predictive performance, with the highest AUC values of 0.91, 0.88, and 0.91 for targets, academia-sourced cell lines, and NCI60 cancer cell lines, respectively. A user-friendly webserver called DeepCancerMap was developed based on these models, providing various functions for anticancer drug discovery.
EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY
(2023)
Article
Immunology
Sovan Saha, Piyali Chatterjee, Anup Kumar Halder, Mita Nasipuri, Subhadip Basu, Dariusz Plewczynski
Summary: This research highlights the significance of exploring unexplored druggable protein targets in the Human interactome for various diseases, including COVID-19. Using machine learning approaches can save time and labor compared to traditional experimental methods. The proposed Machine Learning-based Drug Target Discovery (ML-DTD) approach shows promising results in predicting and validating novel COVID-19 human drug targets.
Article
Biochemical Research Methods
Tianyi Zhao, Yang Hu, Linda R. Valsdottir, Tianyi Zang, Jiajie Peng
Summary: In order to improve the identification of DTIs, a DPP network was established and a novel learning framework GCN-DTI was proposed. The method utilizes graph convolutional networks to learn DPP features and deep neural networks to predict final DTI labels, outperforming existing approaches significantly.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Hegang Chen, Yuyin Lu, Yuedong Yang, Yanghui Rao
Summary: Combination therapy plays an important role in treating complex diseases, but the large number of possible combinations limits our ability to identify effective ones. This study introduces a new computational pipeline, DCMGCN, which integrates diverse drug-related information to predict novel drug combinations. The tests show that DCMGCN outperforms existing methods and may help to clarify the understanding of drug mechanisms.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Pharmacology & Pharmacy
Chaokun Yan, Zhihao Suo, Jianlin Wang, Ge Zhang, Huimin Luo
Summary: This article introduces a DACPGTN model for predicting the ATC code of drugs. The model constructs composite features of drugs, diseases, and targets, and learns diverse biomedical information. It utilizes graph convolution networks to generate embeddings of drug nodes, achieving better prediction performance in multi-label learning tasks for drug discovery.
FRONTIERS IN PHARMACOLOGY
(2022)
Article
Oncology
Antonio Federico, Michele Fratello, Giovanni Scala, Lena Mobus, Alisa Pavel, Giusy del Giudice, Michele Ceccarelli, Valerio Costa, Alfredo Ciccodicola, Vittorio Fortino, Angela Serra, Dario Greco
Summary: The study found that current treatments for complex diseases have high toxicity and often lead to drug resistance, highlighting the need for novel and more specific pharmacological therapies. The authors developed an integrated network pharmacology framework combining mechanistic and chemocentric approaches to predict potential drug combinations for cancer therapy. The results show paclitaxel as a suitable drug for combination therapy in many cancer types and identified several non-cancer-related genes as potential candidates for cancer pharmacological treatment.
Article
Biochemical Research Methods
Minwoo Pak, Sangseon Lee, Inyoung Sung, Bonil Koo, Sun Kim
Summary: Drug response prediction (DRP) is crucial for precision medicine to anticipate patient reactions to drugs. While most studies use cell line transcriptome data and drug chemical structures to predict drug response, this study proposes a framework that leverages drug target interaction (DTI) information to improve deep learning-based DRP models. By computing gene perturbation scores through network propagation techniques, the framework integrates this DTI information with existing DRP models. The results show significant performance boosts, especially when dealing with unknown drugs.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Qi An, Liang Yu
Summary: This study introduces a new method for predicting drug-target interactions in multiplex networks and achieves accurate results that outperform existing algorithms. Additionally, a reasonable model is proposed to address the widespread negative sampling problem, offering new insights for future research.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biology
Yinchun Su, Jiashuo Wu, Xiangmei Li, Ji Li, Xilong Zhao, Bingyue Pan, Junling Huang, Qingfei Kong, Junwei Han
Summary: In order to identify potential drugs against COVID-19, researchers have developed a computational approach called DTSEA. This method effectively ranks genes and performs enrichment analysis on drug target sets to predict candidate drugs. The DTSEA method has shown high accuracy and reliability in predicting potential drugs for COVID-19.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Review
Pharmacology & Pharmacy
Ying-Shan Ren, Hui-Lin Li, Xiu-Hong Piao, Zhi-You Yang, Shu-Mei Wang, Yue-Wei Ge
Summary: DARTS is a novel target discovery approach that is particularly adept at screening small molecule targets without requiring any structural modifications. It can reveal drug-target interactions from cells or tissues and has been applied to uncover drug-action mechanisms.
BIOCHEMICAL PHARMACOLOGY
(2021)