Communications in computer and information science, Год журнала: 2024, Номер unknown, С. 95 - 109
Опубликована: Янв. 1, 2024
Язык: Английский
Communications in computer and information science, Год журнала: 2024, Номер unknown, С. 95 - 109
Опубликована: Янв. 1, 2024
Язык: Английский
Information Fusion, Год журнала: 2025, Номер unknown, С. 102928 - 102928
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
5Neurocomputing, Год журнала: 2024, Номер 573, С. 127218 - 127218
Опубликована: Янв. 5, 2024
Язык: Английский
Процитировано
6BMC Medical Genomics, Год журнала: 2025, Номер 18(S1)
Опубликована: Янв. 13, 2025
Abstract Background Drug and protein targets affect the physiological functions metabolic effects of body through bonding reactions, accurate prediction drug-protein target interactions is crucial for drug development. In order to shorten development cycle reduce costs, machine learning methods are gradually playing an important role in field drug-target interactions. Results Compared with other methods, regression-based affinity more representative binding ability. Accurate can effectively time cost retargeting new this paper, a model (WPGraphDTA) based on power graph word2vec proposed. Conclusions model, molecular features module extracted by neural network, then obtained Word2vec method. After feature fusion, they input into three full connection layers obtain value. We conducted experiments Davis Kiba datasets, experimental results showed that WPGraphDTA exhibited good performance.
Язык: Английский
Процитировано
0Environmental Research, Год журнала: 2024, Номер 258, С. 119248 - 119248
Опубликована: Май 31, 2024
Язык: Английский
Процитировано
3Trends in Food Science & Technology, Год журнала: 2024, Номер 153, С. 104700 - 104700
Опубликована: Сен. 5, 2024
Язык: Английский
Процитировано
3IET Systems Biology, Год журнала: 2025, Номер 19(1)
Опубликована: Янв. 1, 2025
Abstract Metal ions are significant ligands that bind to proteins and play crucial roles in cell metabolism, material transport, signal transduction. Predicting the protein‐metal ion ligand binding residues (PMILBRs) accurately is a challenging task theoretical calculations. In this study, authors employed fused amino acids their derived information as feature parameters predict PMILBRs using three classical machine learning algorithms, yielding favourable prediction results. Subsequently, deep algorithm was incorporated prediction, resulting improved results for sets of Ca 2+ Mg compared previous studies. The validation matrix provided optimal model each ionic residue, exhibiting capability effectively predicting sites metal real protein chains.
Язык: Английский
Процитировано
0International Journal of Quantum Chemistry, Год журнала: 2025, Номер 125(7)
Опубликована: Март 19, 2025
ABSTRACT Machine learning has revolutionized computational chemistry by improving the accuracy of predicting thermodynamic and kinetic properties like activation energies Gibbs free energies, accelerating materials discovery optimizing reaction conditions in both academic industrial applications. This review investigates recent strides applying advanced machine techniques, including transfer learning, for accurately within complex chemical reactions. It thoroughly provides an extensive overview pivotal methods utilized this domain, sophisticated neural networks, Gaussian processes, symbolic regression. Furthermore, prominently highlights commonly adopted frameworks, such as Chemprop, SchNet, DeepMD, which have consistently demonstrated remarkable exceptional efficiency properties. Moreover, it carefully explores numerous influential studies that notably reported substantial successes, particularly focusing on predictive performance, diverse datasets, innovative model architectures profoundly contributed to enhancing methodologies. Ultimately, clearly underscores transformative potential significantly power intricate systems, bearing considerable implications cutting‐edge theoretical research practical
Язык: Английский
Процитировано
0Neurocomputing, Год журнала: 2024, Номер 594, С. 127833 - 127833
Опубликована: Май 9, 2024
Язык: Английский
Процитировано
2Information Sciences, Год журнала: 2024, Номер 684, С. 121286 - 121286
Опубликована: Авг. 3, 2024
Язык: Английский
Процитировано
2Innovative Infrastructure Solutions, Год журнала: 2024, Номер 10(1)
Опубликована: Дек. 26, 2024
Язык: Английский
Процитировано
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