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MSFF-MA-DDI: Multi-Source Feature Fusion with Multiple Attention blocks for predicting Drug-Drug Interaction events
This study designs a Multi-Source Feature Fusion framework with Multiple Attention blocks named MSFF-MA-DDI that utilizes multimodal data for DDI event prediction. MSFF-MA-DDI can (i) encode global correlations between long-distance atoms in drug molecular sequences by a self-attention layer based on a position embedding block and (ii) fuse drug sequence features and heterogeneous features (chemical substructure, target, and enzyme) through a multi-head attention block to better represent the features of drugs. Experiments on real-world datasets show that MSFF-MA-DDI can achieve performance that is close to or even better ...
Source: Computational Biology and Chemistry - December 28, 2023 Category: Bioinformatics Authors: Qi Jin Jiang Xie Dingkai Huang Chang Zhao Hongjian He Source Type: research

A method to improve the prediction performance of cancer-gene association by screening negative training samples through gene network data
Comput Biol Chem. 2023 Dec 9;108:107997. doi: 10.1016/j.compbiolchem.2023.107997. Online ahead of print.ABSTRACTThis work focuses on data sampling in cancer-gene association prediction. Currently, researchers are using machine learning methods to predict genes that are more likely to produce cancer-causing mutations. To improve the performance of machine learning models, methods have been proposed, one of which is to improve the quality of the training data. Existing methods focus mainly on positive data, i.e. cancer driver genes, for screening selection. This paper proposes a low-cancer-related gene screening method based...
Source: Computational Biology and Chemistry - December 28, 2023 Category: Bioinformatics Authors: Mingzhe Xu Nor Aniza Abdullah Aznul Qalid Md Sabri Source Type: research

T5S1607 identified as a antibacterial FtsZ inhibitor ：Virtual screening combined with bioactivity evaluation for the drug discovery
In conclusion, this work successfully discovered FtsZ inhibitors with good activity through the virtual screening drug discovery process.PMID:38142532 | DOI:10.1016/j.compbiolchem.2023.108006 (Source: Computational Biology and Chemistry)
Source: Computational Biology and Chemistry - December 24, 2023 Category: Bioinformatics Authors: Zhouling Xie Wei Ruan Jiaojiao Guo Yan Li Siqi Zhou Jing Zhao Li Wan Shan Xu Qidong Tang Pengwu Zheng Linxiao Wang Wufu Zhu Source Type: research

Computational insights into potential marine natural products as selective inhibitors of Mycobacterium tuberculosis InhA: A structure-based virtual screening study
Comput Biol Chem. 2023 Nov 28;108:107991. doi: 10.1016/j.compbiolchem.2023.107991. Online ahead of print.ABSTRACTSeveral factors are associated with the emergence of drug resistance mechanisms, such as impermeable cell walls, gene mutations, and drug efflux systems. Consequently, bacteria acquire resistance, leading to a decrease in drug efficacy. A new and innovative strategy is required to combat drug resistance in tuberculosis (TB) effectively. Therefore, targeting the mycolic acid biosynthesis pathway, which is involved in synthesising mycolic acids (MAs), essential structural components responsible for mycobacterial p...
Source: Computational Biology and Chemistry - December 12, 2023 Category: Bioinformatics Authors: Manikandan Jayaraman Vijayakumar Gosu Rajalakshmi Kumar Jeyakanthan Jeyaraman Source Type: research