Applications of Artificial Intelligence in Drug Repurposing

Advanced Science, Journal Year: 2025, Volume and Issue: unknown

Published: March 6, 2025

Drug repurposing identifies new therapeutic uses for the existing drugs originally developed different indications, aiming at capitalizing on established safety and efficacy profiles of known drugs. Thus, it is beneficial to bypass early stages drug development, reduction time cost associated with bringing therapies market. Traditional experimental methods are often time-consuming expensive, making artificial intelligence (AI) a promising alternative due its lower cost, computational advantages, ability uncover hidden patterns. This review focuses availability AI algorithms in their positive specific roles revealing drugs, especially being integrated virtual screening. It shown that excel analyzing large-scale datasets, identifying complicated patterns responses from these predictions potential repurposing. Building insights, challenges remain developing efficient future research, including integrating drug-related data across databases better repurposing, enhancing efficiency, advancing personalized medicine.

Language: Английский

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Revitalizing Cancer Treatment: Exploring the Role of Drug Repurposing

Cancers, Journal Year: 2024, Volume and Issue: 16(8), P. 1463 - 1463

Published: April 11, 2024

Cancer persists as a global challenge necessitating continual innovation in treatment strategies. Despite significant advancements comprehending the disease, cancer remains leading cause of mortality worldwide, exerting substantial economic burdens on healthcare systems and societies. The emergence drug resistance further complicates therapeutic efficacy, underscoring urgent need for alternative approaches. Drug repurposing, characterized by utilization existing drugs novel clinical applications, emerges promising avenue addressing these challenges. Repurposed drugs, comprising FDA-approved (in other disease indications), generic, off-patent, failed medications, offer distinct advantages including established safety profiles, cost-effectiveness, expedited development timelines compared to discovery processes. Various methodologies, such knowledge-based analyses, drug-centric strategies, computational approaches, play pivotal roles identifying potential candidates repurposing. However, despite promise repurposed repositioning confronts formidable obstacles. Patenting issues, financial constraints associated with conducting extensive trials, necessity combination therapies overcome limitations monotherapy pose This review provides an in-depth exploration covering diverse array approaches experimental, re-engineering protein, nanotechnology, methods. Each avenues presents opportunities obstacles pursuit uses drugs. By examining multifaceted landscape this aims comprehensive insights into its transform therapeutics.

Language: Английский

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Emerging drug design strategies in anti-influenza drug discovery

Acta Pharmaceutica Sinica B, Journal Year: 2023, Volume and Issue: 13(12), P. 4715 - 4732

Published: Aug. 14, 2023

Influenza is an acute respiratory infection caused by influenza viruses (IFV), According to the World Health Organization (WHO), seasonal IFV epidemics result in approximately 3–5 million cases of severe illness, leading about half a deaths worldwide, along with economic losses and social burdens. Unfortunately, frequent mutations lead certain lag vaccine development as well resistance existing antiviral drugs. Therefore, it great importance develop anti-IFV drugs high efficiency against wild-type resistant strains, needed fight current future outbreaks different strains. In this review, we summarize general strategies used for discovery agents targeting multiple strains (including those available drugs). Structure-based drug design, mechanism-based multivalent interaction-based design repurposing are amongst most relevant that provide framework IFV.

Language: Английский

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Artificial Intelligence/Machine Learning-Driven Small Molecule Repurposing via Off-Target Prediction and Transcriptomics

Toxics, Journal Year: 2023, Volume and Issue: 11(10), P. 875 - 875

Published: Oct. 22, 2023

The process of discovering small molecule drugs involves screening numerous compounds and optimizing the most promising ones, both in vitro vivo. However, approximately 90% these optimized candidates fail during trials due to unexpected toxicity or insufficient efficacy. Current concepts with respect drug–protein interactions suggest that each interacts an average 6–11 targets. This implies approved even discontinued could be repurposed by leveraging their unintended Therefore, we developed a computational repurposing framework for molecules, which combines artificial intelligence/machine learning (AI/ML)-based chemical similarity-based target prediction methods cross-species transcriptomics information. methodology incorporates eight distinct methods, including three machine methods. By using multiple orthogonal “dataset” composed 2766 FDA-approved targeting therapeutic classes, identified 27,371 off-target involving 2013 protein targets (i.e., around 10 per drug). Relative dataset, 150,620 structurally similar compounds. highest number predicted were G protein-coupled receptors (GPCRs), enzymes, kinases 10,648, 4081, 3678 interactions, respectively. Notably, 17,283 (63%) have been confirmed vitro. Approximately 4000 had IC50 <100 nM 1105 1661 <10 696 drugs. Together, confirmation exploration tissue-specific expression patterns human animal tissues offer insights into potential drug new applications.

Language: Английский

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Reversal gene expression assessment for drug repurposing, a case study of glioblastoma

Journal of Translational Medicine, Journal Year: 2025, Volume and Issue: 23(1)

Published: Jan. 7, 2025

Abstract Background Glioblastoma (GBM) is a rare brain cancer with an exceptionally high mortality rate, which illustrates the pressing demand for more effective therapeutic options. Despite considerable research efforts on GBM, its underlying biological mechanisms remain unclear. Furthermore, none of United States Food and Drug Administration (FDA) approved drugs used GBM deliver satisfactory survival improvement. Methods This study presents novel computational pipeline by utilizing gene expression data analysis drug repurposing to address challenges in disease development, particularly focusing GBM. The Gene Expression Profile (GGEP) was constructed multi-omics identify reversal GGEP from Integrated Network-Based Cellular Signatures (iLINCS) database. Results We prioritized candidates via hierarchical clustering their signatures quantification strength calculating two self-defined indices based genes’ log2 foldchange (LFC) that could induce. Among five candidates, in-vitro experiments validated Clofarabine Ciclopirox as highly efficacious selectively targeting cells. Conclusions success this illustrated promising avenue accelerating development uncovering effect between diseases, can be extended other diseases non-rare diseases.

Language: Английский

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New strategies to enhance the efficiency and precision of drug discovery

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

Published: Feb. 11, 2025

Drug discovery plays a crucial role in medicinal chemistry, serving as the cornerstone for developing new treatments to address wide range of diseases. This review emphasizes significance advanced strategies, such Click Chemistry, Targeted Protein Degradation (TPD), DNA-Encoded Libraries (DELs), and Computer-Aided Design (CADD), boosting drug process. Chemistry streamlines synthesis diverse compound libraries, facilitating efficient hit lead optimization. TPD harnesses natural degradation pathways target previously undruggable proteins, while DELs enable high-throughput screening millions compounds. CADD employs computational methods refine candidate selection reduce resource expenditure. To demonstrate utility these methodologies, we highlight exemplary small molecules discovered past decade, along with summary marketed drugs investigational that exemplify their clinical impact. These examples illustrate how techniques directly contribute advancing chemistry from bench bedside. Looking ahead, Artificial Intelligence (AI) technologies interdisciplinary collaboration are poised growing complexity discovery. By fostering deeper understanding transformative this aims inspire innovative research directions further advance field chemistry.

Language: Английский

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Repurposing of nervous system drugs for cancer treatment: recent advances, challenges, and future perspectives

Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)

Published: March 26, 2025

Language: Английский

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Uncovering novel therapeutic targets in glucose, nucleotides and lipids metabolism during cancer and neurological diseases

International Journal of Immunopathology and Pharmacology, Journal Year: 2024, Volume and Issue: 38

Published: Jan. 1, 2024

Cell metabolism functions without a stop in normal and pathological cells. Different metabolic changes occur the disease. influences biochemical processes, signaling pathways, gene regulation. Knowledge regarding disease is limited.

Language: Английский

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Machine Learning Applications for Drug Repurposing

Published: June 19, 2024

Machine learning (ML) is revolutionizing drug repurposing, offering a more efficient, cost-effective approach to discovery by identifying new therapeutic uses for existing drugs. ML algorithms process large, complex biomedical datasets, find hidden patterns that reveal unexpected links between drugs and diseases, predict potential side effects. This advancement holds significant promise precision medicine personalized healthcare. chapter aims explore the growing role of in an emergent frontier identify drugs, thereby accelerating pace medical innovation while mitigating cost risk. The discusses various case studies, demonstrating application drug–disease connections predicting adverse reactions, significantly contributing medicine. In addition, investigates successes challenges encountered this nascent field, highlighting modernize discovery. Emphasis placed on ethical privacy concerns surrounding use patient data models, urging need robust regulations. comprehensive review serves as practical guide those at intersection pharmaceutical research, clinical practice, computer sciences, advocating synergetic these fields advancing

Language: Английский

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Application of artificial intelligence and machine learning in drug repurposing

Progress in molecular biology and translational science, Journal Year: 2024, Volume and Issue: unknown, P. 171 - 211

Published: Jan. 1, 2024

Language: Английский

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