DPI_CDF: druggable protein identifier using cascade deep forest

BMC Bioinformatics, Год журнала: 2024, Номер 25(1)

Опубликована: Апрель 5, 2024

Abstract Background Drug targets in living beings perform pivotal roles the discovery of potential drugs. Conventional wet-lab characterization drug is although accurate but generally expensive, slow, and resource intensive. Therefore, computational methods are highly desirable as an alternative to expedite large-scale identification druggable proteins (DPs); however, existing silico predictor’s performance still not satisfactory. Methods In this study, we developed a novel deep learning-based model DPI_CDF for predicting DPs based on protein sequence only. utilizes evolutionary-based (i.e., histograms oriented gradients position-specific scoring matrix), physiochemical-based component representation), compositional-based normalized qualitative characteristic) properties generate features. Then hierarchical forest fuses these three encoding schemes build proposed DPI_CDF. Results The empirical outcomes 10-fold cross-validation demonstrate that achieved 99.13 % accuracy 0.982 Matthew’s-correlation-coefficient (MCC) training dataset. generalization power trained further examined independent dataset 95.01% maximum 0.900 MCC. When compared current state-of-the-art methods, improves terms by 4.27% 4.31% testing datasets, respectively. We believe, will support research community identify escalate process. Availability benchmark datasets source codes available GitHub: http://github.com/Muhammad-Arif-NUST/DPI_CDF .

Язык: Английский

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IP-GCN: A Deep Learning Model for Prediction of Insulin using Graph Convolutional Network for Diabetes Drug Design

Journal of Computational Science, Год журнала: 2024, Номер 81, С. 102388 - 102388

Опубликована: Июль 14, 2024

Язык: Английский

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Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting

Journal of Biomolecular Structure and Dynamics, Год журнала: 2023, Номер 42(22), С. 12330 - 12341

Опубликована: Окт. 18, 2023

AbstractThe identification of druggable proteins (DPs) is significant for the development new drugs, personalized medicine, understanding disease mechanisms, drug repurposing, and economic benefits. By identifying targets, researchers can develop therapies a range diseases, leading to better patient outcomes. Identification DPs by machine learning strategies more efficient cost-effective than conventional methods. In this study, computational predictor, namely Drug-LXGB, introduced enhance DPs. Features are discovered composition, transition, distribution (CTD), composition K-spaced amino acid pair (CKSAAP), pseudo-position-specific scoring matrix (PsePSSM), novel descriptor, called multi-block pseudo (MB-PseAAC). The dimensions CTD, CKSAAP, PsePSSM, MB-PseAAC integrated utilized sequential forward selection as feature algorithm. best characteristics provided random forest, extreme gradient boosting, light eXtreme boosting (LXGB). predictive analysis these methods measured via 10-fold cross-validation. LXGB-based model secures highest results other existing predictors. Our protocol will perform an active role in designing drugs would be fruitful explore potential target. This study help capture universal view target.Communicated Ramaswamy H. SarmaKeywords: Druggable proteinslight boostingmachine AcknowledgmentsThe authors gratefully acknowledge technical financial support Ministry Education King Abdulaziz University, DSR, Jeddah, Saudi Arabia.Disclosure statementThe have no competing interest.Additional informationFundingThis research work was supported Institutional Fund Projects under grant number IFPIP: 1396-611-1443.

Язык: Английский

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A stacking ensemble model for predicting soil organic carbon content based on visible and near-infrared spectroscopy

Infrared Physics & Technology, Год журнала: 2024, Номер 140, С. 105404 - 105404

Опубликована: Июнь 15, 2024

Язык: Английский

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Identification of potential novel therapeutic drug target against Elizabethkingia anophelis by integrative pan and subtractive genomic analysis: An in silico approach

Computers in Biology and Medicine, Год журнала: 2023, Номер 165, С. 107436 - 107436

Опубликована: Авг. 30, 2023

Язык: Английский

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PMPred-AE: a computational model for the detection and interpretation of pathological myopia based on artificial intelligence

Frontiers in Medicine, Год журнала: 2025, Номер 12

Опубликована: Март 13, 2025

Introduction Pathological myopia (PM) is a serious visual impairment that may lead to irreversible damage or even blindness. Timely diagnosis and effective management of PM are great significance. Given the increasing number cases worldwide, there an urgent need develop automated, accurate, highly interpretable diagnostic technology. Methods We proposed computational model called PMPred-AE based on EfficientNetV2-L with attention mechanism optimization. In addition, Gradient-weighted class activation mapping (Grad-CAM) technology was used provide intuitive interpretation for model’s decision-making process. Results The experimental results demonstrated achieved excellent performance in automatically detecting PM, accuracies 98.50, 98.25, 97.25% training, validation, test datasets, respectively. can focus specific areas image when making detection decisions. Discussion developed capable reliably providing accurate detection. Grad-CAM also process model. This approach provides healthcare professionals tool AI

Язык: Английский

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Collective in-silico and in-vitro evaluation indicate natural phenolics as a potential therapeutic candidate targeting antimicrobial-resistant genes of Helicobacter pylori

International Journal of Biological Macromolecules, Год журнала: 2025, Номер unknown, С. 142197 - 142197

Опубликована: Март 1, 2025

Язык: Английский

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M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy

BMC Bioinformatics, Год журнала: 2025, Номер 26(1)

Опубликована: Апрель 30, 2025

Accelerating drug discovery for glucocorticoid receptor (GR)-related disorders, including innovative machine learning (ML)-based approaches, holds promise in advancing therapeutic development, optimizing treatment efficacy, and mitigating adverse effects. While experimental methods can accurately identify GR antagonists, they are often not cost-effective large-scale discovery. Thus, computational approaches leveraging SMILES information precise silico identification of antagonists crucial, enabling efficient scalable Here, we develop a new ensemble approach using multi-step stacking strategy (M3S), termed M3S-GRPred, aimed at rapidly discovering novel antagonists. To the best our knowledge, M3S-GRPred is first SMILES-based predictor designed to without use 3D structural information. In constructed different balanced subsets an under-sampling approach. Using these subsets, explored evaluated heterogeneous base-classifiers trained with variety feature descriptors coupled popular ML algorithms. Finally, was by integrating probabilistic from selected derived two-step selection technique. Our comparative experiments demonstrate that precisely effectively address imbalanced dataset. Compared traditional classifiers, attained superior performance terms both training independent test datasets. Additionally, applied potential among FDA-approved drugs confirmed through molecular docking, followed detailed MD simulation studies repurposing Cushing's syndrome. We anticipate will serve as screening tool vast libraries unknown compounds manner.

Язык: Английский

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GRU4ACE: Enhancing ACE inhibitory peptide prediction by integrating gated recurrent unit with multi‐source feature embeddings

Protein Science, Год журнала: 2025, Номер 34(6)

Опубликована: Май 15, 2025

Abstract Accurate identification of angiotensin‐I‐converting enzyme (ACE) inhibitory peptides is essential for understanding the primary factor regulating renin‐angiotensin system and guiding development new drug candidates. Given inherent challenges in experimental processes, computational methods silico peptide can be invaluable enabling high‐throughput characterization ACE peptides. This study introduces GRU4ACE, an innovative deep learning framework based on multi‐view information identifying First, GRU4ACE utilizes multi‐source feature encoding to capture embedded peptides, including sequential information, graphical semantic contextual information. Specifically, representations used herein are derived from conventional descriptors, natural language processing (NLP)‐based embeddings, pre‐trained protein model (PLM)‐based embeddings. Next, multiple embeddings were fused, elastic net was employed optimization. Finally, optimal subset with strong representation input into a gated recurrent unit (GRU). The proposed approach demonstrated superior performance over existing terms independent test. To specific, balanced accuracy, sensitivity, MCC scores reached 0.948, 0.934, 0.895, which 6.46%, 8.92%, 12.51% higher than those compared methods, respectively. In addition, when comparing well‐regarded we found that features effectively captured crucial leading improved prediction performance. These comprehensive results highlight enhances accuracy significantly narrows down search potential antihypertensive drugs.

Язык: Английский

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Repositioning of clinically approved drug Bazi Bushen capsule for treatment of Aizheimer's disease using network pharmacology approach and in vitro experimental validation

Heliyon, Год журнала: 2023, Номер 9(7), С. e17603 - e17603

Опубликована: Июнь 27, 2023

AimsTo explore the new indications and key mechanism of Bazi Bushen capsule (BZBS) by network pharmacology in vitro experiment.MethodsThe ingredients library BZBS was constructed retrieving multiple TCM databases. The potential target profiles components were predicted prediction algorithms based on different principles, validated using known activity data. spectrum with high reliability screened considering source targets node degree compound-target (C-T) network. Subsequently, for disease ontology (DO) enrichment analysis initially GO KEGG pathway analysis. Furthermore, sets acting AD signaling identified intersection Based STRING database, PPI their calculated. Two Alzheimer's (AD) cell models, BV-2 SH-SY5Y, used to preliminarily verify anti-AD efficacy vitro.ResultsIn total, 1499 non-repeated obtained from 16 herbs formula, 1320 confidence predicted. Disease results strongly suggested that formula has be treatment AD. provide a preliminary verification this point. Among them, 113 functional belong pathway. A containing 1051 edges constructed. In experiments showed could significantly reduce release TNF-α IL-6 expression COX-2 PSEN1 Aβ25-35-induced cells, which may related regulation ERK1/2/NF-κB reduced apoptosis rate Aβ25-35 induced SH-SY5Y increased mitochondrial membrane potential, Caspase3 active fragment PSEN1, IDE. This GSK-3β/β-catenin pathway.ConclusionsBZBS use AD, is achieved through ERK1/2, NF-κB pathways, technology feasible drug repurposing strategy reposition clinical approved action. study lays foundation subsequent in-depth provides basis its application

Язык: Английский

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