Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

Life, Journal Year: 2024, Volume and Issue: 14(2), P. 233 - 233

Published: Feb. 7, 2024

Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) emerged as transformative tool in drug discovery, offering innovative solutions to complex challenges the pharmaceutical industry. This manuscript covers multifaceted role of AI encompassing AI-assisted delivery design, discovery new drugs, novel techniques. We explore various methodologies, including machine learning deep learning, their applications target identification, virtual screening, design. paper also discusses historical medicine, emphasizing its profound impact on healthcare. Furthermore, it addresses AI’s repositioning existing drugs identification combinations, underscoring potential revolutionizing systems. The provides comprehensive overview programs platforms currently used illustrating technological advancements future directions this field. study not only presents current state but anticipates trajectory, highlighting opportunities that lie ahead.

Language: Английский

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Current methods in explainable artificial intelligence and future prospects for integrative physiology

Pflügers Archiv - European Journal of Physiology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 25, 2025

Abstract Explainable artificial intelligence (XAI) is gaining importance in physiological research, where now used as an analytical and predictive tool for many medical research questions. The primary goal of XAI to make AI models understandable human decision-makers. This can be achieved particular through providing inherently interpretable methods or by making opaque their outputs transparent using post hoc explanations. review introduces core topics provides a selective overview current physiology. It further illustrates solved discusses open challenges existing practical examples from the field. article gives outlook on two possible future prospects: (1) provide trustworthy integrative (2) integrating expertise about explanation into method development useful beneficial human-AI partnerships.

Language: Английский

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Comparison of new secondgeneration H1 receptor blockers with some molecules; a study involving DFT, molecular docking, ADMET, biological target and activity

BMC Chemistry, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 4, 2025

Although the antiallergic properties of compounds such as CAPE, Melatonin, Curcumin, and Vitamin C have been poorly discussed by experimental studies, these famous molecules never with calculations. The histamine-1 receptor (H1R) belongs to family rhodopsin-like G-protein-coupled receptors expressed in cells that mediate allergies other pathophysiological diseases. In this study, pharmacological activities FDA-approved second generation H1 antihistamines (Levocetirizine, desloratadine fexofenadine) C, ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiles, density functional theory (DFT), molecular docking, biological targets were compared calculating. Since drug development is an extremely risky, costly time-consuming process, data obtained study will facilitate guide future studies. It also enable researchers focus on most promising compounds, providing effective design strategy. Their activity was carried out using computer-based computational techniques including DFT, analysis, targeting, methods. best binding sites Desloratadine, Levocetirizine, Fexofenadine, Quercetin, curcumin, ligands Desmoglein 1, Human Histamine receptor, IgE IL13 protons determined docking method energy interaction states analyzed. Fexofenadine Quercetin ligand showed affinity. Melatonin had Caco-2 permeability PPB values CAPE Curcumin at optimal levels. On OATP1B1 OATP1B3 curcumin found strong inhibition effects BCRP. highest CYP1A2, while CYP2C19 CYP2C9. be safer terms cardiac toxicity mutagenic risks, Desloratadine Levocetirizine high risks neurotoxicity hematotoxicity, noted for its enzyme inhibitory low hERG blockade, DILI, cytotoxicity pointed various safety concerns. This demonstrated potential machine learning methods understanding discovering blockers. results provide important clues strategies clinical use light data, are remarkable molecules.

Language: Английский

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COX‐2 Inhibitor Prediction With KNIME: A Codeless Automated Machine Learning‐Based Virtual Screening Workflow

Journal of Computational Chemistry, Journal Year: 2025, Volume and Issue: 46(2)

Published: Jan. 11, 2025

Cyclooxygenase-2 (COX-2) is an enzyme that plays a crucial role in inflammation by converting arachidonic acid into prostaglandins. The overexpression of associated with conditions such as cancer, arthritis, and Alzheimer's disease (AD), where it contributes to neuroinflammation. In silico virtual screening pivotal early-stage drug discovery; however, the absence coding or machine learning expertise can impede development reliable computational models capable accurately predicting inhibitor compounds based on their chemical structure. this study, we developed automated KNIME workflow for COX-2 inhibitory potential novel molecules building multi-level ensemble model constructed five algorithms (i.e., Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Extreme Gradient Boosting) various molecular fingerprint descriptors AtomPair, Avalon, MACCS, Morgan, RDKit, Pattern). Post-applicability domain filtering, final majority voting-based achieved 90.0% balanced accuracy, 87.7% precision, 86.4% recall external validation set. freely accessible empowers users swiftly effortlessly predict inhibitors, eliminating need any prior knowledge learning, coding, statistical modeling, significantly broadening its accessibility. While beginners seamlessly use tool is, experienced leverage foundation build advanced workflows, driving further research innovation.

Language: Английский

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Biological activity evaluation and molecular docking studies of newly synthesized phenylamino derivatives

Journal of Molecular Structure, Journal Year: 2025, Volume and Issue: unknown, P. 142037 - 142037

Published: March 1, 2025

Language: Английский

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Integrated Network Pharmacology and Proteomics to Elucidate the Mechanism and Potential Targets of Tongfu Guanjie Gao on KOA in a Papain-Induced Rabbit Model

Natural Product Communications, Journal Year: 2025, Volume and Issue: 20(3)

Published: March 1, 2025

Objectives: Knee osteoarthritis (KOA) is a common chronic degenerative joint disease in the world. Tongfu Guanjie Gao (TFGJG) external preparations of Traditional Chinese Medicine which has remarkable effect on relieving KOA. In this study, we aimed to construct papain-induced KOA model New Zealand white rabbits investigate mechanism action TFGJG ameliorating Methods: After successful induction rabbit model, was given daily for 18 days pharmacological intervention. The therapeutic verified by H&E staining and ELISA assay, improving explored Network pharmacology Proteomics. Results: Our results showed that significantly improved range motion reduced levels tumor necrosis factor (TNF), interleukin-1beta (IL-1β), interleukin-6 (IL-6), cyclooxygenase-2 (COX-2), collagen type II (COL-2) matrix metalloproteinase-13 (MMP-13) inflammatory factors synovial fluid. analyses identified 218 targets treatment KOA, proteomics experiments regulates 150 differentially expressed proteins. Through comprehensive analysis, MAP2K1 CRTAP were as core Meanwhile, PRM inhibited expression articular cartilage. Conclusion: may be promising agent exerts mediating pathways, inhibiting offers potential new drug

Language: Английский

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Evaluation of Machine Learning Methods for Identifying Carbonic Anhydrase-II Inhibitors as Drug Candidates for Glaucoma

Malacca Pharmaceutics, Journal Year: 2025, Volume and Issue: 3(1), P. 32 - 41

Published: March 4, 2025

Glaucoma is a leading cause of irreversible blindness, primarily managed by lowering intraocular pressure (IOP). Carbonic Anhydrase-II (CA-II) inhibitors play crucial role in this treatment reducing aqueous humor production. However, existing CA-II often suffer from poor selectivity, side effects, and limited bioavailability, highlighting the need for more efficient targeted drug discovery approaches. This study uses machine learning-driven Quantitative Structure-Activity Relationship (QSAR) modeling to predict inhibition based on molecular descriptors, significantly enhancing screening efficiency over traditional experimental methods. By evaluating multiple learning models, including Support Vector Machine, Gradient Boosting, Random Forest, we identify SVM as most effective classifier, achieving highest accuracy (83.70%) F1-score (89.36%). Class imbalance remains challenging despite high sensitivity, necessitating further improvements through resampling hyperparameter optimization. Our findings underscore potential learning-based virtual accelerating inhibitor identification advocate integrating AI-driven approaches with techniques. Future directions include deep enhancements hybrid learning-docking frameworks improve prediction facilitate development potent selective glaucoma treatments.

Language: Английский

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Investigating the potential compounds of Kalanchoe pinnata plant for the treatment of Inflammation utilizing molecular docking and molecular dynamic simulation approach

Published: April 1, 2025

Language: Английский

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Integrating ensemble machine learning and multi-omics approaches to identify Dp44mT as a novel anti-Candida albicans agent targeting cellular iron homeostasis

Frontiers in Pharmacology, Journal Year: 2025, Volume and Issue: 16

Published: April 24, 2025

Candidiasis, mainly caused by Candida albicans, poses a serious threat to human health. The escalating drug resistance in C. albicans and the limited antifungal options highlight critical need for novel therapeutic strategies. We evaluated 12 machine learning models on self-constructed dataset with known anti-C. activity. Based their performance, optimal model was selected screen our separate in-house compound library unknown activity potential agents. of compounds confirmed through vitro susceptibility assays, hyphal growth biofilm formation assays. Through transcriptomics, proteomics, iron rescue experiments, CTC staining, JC-1 DAPI molecular docking, dynamics simulations, we elucidated mechanism underlying compound. Among models, best predictive an ensemble constructed from Random Forests Categorical Boosting using soft voting. It predicts that Dp44mT exhibits potent tests further verified this finding can inhibit planktonic growth, formation, albicans. Mechanistically, exerts disrupting cellular homeostasis, leading collapse mitochondrial membrane ultimately causing apoptosis. This study presents practical approach predicting com-pounds provides new insights into development homeostasis

Language: Английский

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Structure based screening and molecular docking with dynamic simulation of natural secondary metabolites to target RNA-dependent RNA polymerase of five different retroviruses

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(8), P. e0307615 - e0307615

Published: Aug. 5, 2024

Viral diseases pose a serious global health threat due to their rapid transmission and widespread impact. The RNA-dependent RNA polymerase (RdRp) participates in the synthesis, transcription, replication of viral host. current study investigates antiviral potential secondary metabolites particularly those derived from bacteria, fungi, plants develop novel medicines. Using virtual screening approach that combines molecular docking dynamics (MD) simulations, we aimed discover compounds with strong interactions RdRp five different retroviruses. top were selected for each based on scores, binding patterns, interactions, drug-likeness properties. uncovered several activity against RdRp. For instance, cytochalasin Z8 had lowest score -8.9 (kcal/mol) SARS-CoV-2, aspulvinone D (-9.2 kcal/mol) HIV-1, talaromyolide (-9.9 hepatitis C, Ebola also maintained -9.2 kcal/mol enzyme dengue virus. These showed remarkable comparable standard drug (remdesivir -7.4 approved target possess no significant toxicity. simulation confirmed best ligands firmly bound respective proteins time 200 ns. identified lead distinctive pharmacological characteristics, making them candidates repurposing as drugs SARS-CoV-2. Further experimental evaluation investigation are recommended ascertain efficacy potential.

Language: Английский

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