TTD: Therapeutic Target Database describing target druggability information

Nucleic Acids Research, Journal Year: 2023, Volume and Issue: 52(D1), P. D1465 - D1477

Published: Sept. 15, 2023

Target discovery is one of the essential steps in modern drug development, and identification promising targets fundamental for developing first-in-class drug. A variety methods have emerged target assessment based on druggability analysis, which refers to likelihood a being effectively modulated by drug-like agents. In therapeutic database (TTD), nine categories established characteristics were thus collected 426 successful, 1014 clinical trial, 212 preclinical/patented, 1479 literature-reported via systematic review. These characteristic classified into three distinct perspectives: molecular interaction/regulation, human system profile cell-based expression variation. With rapid progression technology concerted effort discovery, TTD other databases highly expected facilitate explorations validation innovative target. now freely accessible at: https://idrblab.org/ttd/.

Language: Английский

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Artificial intelligence for drug discovery: Resources, methods, and applications

Molecular Therapy — Nucleic Acids, Journal Year: 2023, Volume and Issue: 31, P. 691 - 702

Published: Feb. 18, 2023

Conventional wet laboratory testing, validations, and synthetic procedures are costly time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques have revolutionized their applications to Combined with accessible data resources, AI changing the landscape of In past decades, a series AI-based models been developed various steps These used as complements conventional experiments accelerated discovery process. this review, we first introduced widely resources discovery, such ChEMBL DrugBank, followed by molecular representation schemes that convert into computer-readable formats. Meanwhile, summarized algorithms develop Subsequently, discussed pharmaceutical analysis including predicting toxicity, bioactivity, physicochemical property. Furthermore, de novo design, drug-target structure prediction, interaction, binding affinity prediction. Moreover, also highlighted advanced synergism/antagonism prediction nanomedicine design. Finally, challenges future perspectives on

Language: Английский

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Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology

Biomedicine & Pharmacotherapy, Journal Year: 2023, Volume and Issue: 163, P. 114784 - 114784

Published: April 28, 2023

More information about a person's genetic makeup, drug response, multi-omics and genomic response is now available leading to gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled computational toxicogenomics as pivotal part next-gen risk assessment paradigm. Artificial Intelligence (AI) potential provid new ways analyzing patient data making predictions treatment outcomes or toxicity. As medicine involve huge processing, AI can expedite this process by providing powerful analysis, interpretation algorithms. integrate multitude including genome data, records, clinical identify patterns derive predictive models anticipating assessing any approaches. In article, we have studied current trends future perspectives in & toxicology, role connecting two fields, impact on toxicology. work, also study key challenges limitations medicine, toxicogenomics, order fully realize their potential.

Language: Английский

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Machine learning for synergistic network pharmacology: a comprehensive overview

Briefings in Bioinformatics, Journal Year: 2023, Volume and Issue: 24(3)

Published: April 6, 2023

Abstract Network pharmacology is an emerging area of systematic drug research that attempts to understand actions and interactions with multiple targets. has changed the paradigm from ‘one-target one-drug’ highly potent ‘multi-target drug’. Despite that, this synergistic approach currently facing many challenges particularly mining effective information such as targets, mechanism action, organism interaction massive, heterogeneous data. To overcome bottlenecks in multi-target discovery, computational algorithms are welcomed by scientific community. Machine learning (ML) especially its subfield deep (DL) have seen impressive advances. Techniques developed within these fields now able analyze learn huge amounts data disparate formats. In terms network pharmacology, ML can improve discovery decision making big Opportunities apply occur all stages research. Examples include screening biologically active small molecules, target identification, metabolic pathways protein–protein analysis, hub gene analysis finding binding affinity between compounds proteins. This review summarizes premier algorithmic concepts forecasts future opportunities, potential applications well several remaining implementing pharmacology. our knowledge, study provides first comprehensive assessment approaches we hope it encourages additional efforts toward development acceptance pharmaceutical industry.

Language: Английский

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Artificial Intelligence and Machine Learning in Pharmacological Research: Bridging the Gap Between Data and Drug Discovery

Cureus, Journal Year: 2023, Volume and Issue: unknown

Published: Aug. 30, 2023

Artificial intelligence (AI) has transformed pharmacological research through machine learning, deep and natural language processing. These advancements have greatly influenced drug discovery, development, precision medicine. AI algorithms analyze vast biomedical data identifying potential targets, predicting efficacy, optimizing lead compounds. diverse applications in research, including target identification, repurposing, virtual screening, de novo design, toxicity prediction, personalized improves patient selection, trial real-time analysis clinical trials, leading to enhanced safety efficacy outcomes. Post-marketing surveillance utilizes AI-based systems monitor adverse events, detect interactions, support pharmacovigilance efforts. Machine learning models extract patterns from complex datasets, enabling accurate predictions informed decision-making, thus accelerating discovery. Deep specifically convolutional neural networks (CNN), excels image analysis, aiding biomarker identification formulation. Natural processing facilitates the mining of scientific literature, unlocking valuable insights information. However, adoption raises ethical considerations. Ensuring privacy security, addressing algorithm bias transparency, obtaining consent, maintaining human oversight decision-making are crucial concerns. The responsible deployment necessitates robust frameworks regulations. future is promising, with integration emerging technologies like genomics, proteomics, metabolomics offering for medicine targeted therapies. Collaboration among academia, industry, regulatory bodies essential implementation discovery development. Continuous development techniques comprehensive training programs will empower scientists healthcare professionals fully exploit AI's potential, improved outcomes innovative interventions.

Language: Английский

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Artificial Intelligence in Medicine and Dentistry

Acta Stomatologica Croatica, Journal Year: 2023, Volume and Issue: 57(1), P. 70 - 84

Published: March 15, 2023

Introduction: Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent.The first applications of AI were primarily academia and government research institutions, as technology advanced, also industry, commerce, medicine dentistry.Objective: Considering that the possibilities applying artificial are developing rapidly this field one areas with greatest increase number newly published articles, aim paper was to provide an overview literature give insight dentistry.In addition, discuss advantages disadvantages.Conclusion: The dentistry just being discovered.Artificial will greatly contribute developments dentistry, it a tool enables development progress, especially terms personalized healthcare lead much better treatment outcomes.

Language: Английский

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Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives

Seminars in Cancer Biology, Journal Year: 2023, Volume and Issue: 95, P. 52 - 74

Published: July 18, 2023

Language: Английский

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Artificial intelligence in metabolomics: a current review

TrAC Trends in Analytical Chemistry, Journal Year: 2024, Volume and Issue: 178, P. 117852 - 117852

Published: July 3, 2024

Language: Английский

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Artificial intelligence in drug development

Nature Medicine, Journal Year: 2025, Volume and Issue: 31(1), P. 45 - 59

Published: Jan. 1, 2025

Language: Английский

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Integrating artificial intelligence in drug discovery and early drug development: a transformative approach

Biomarker Research, Journal Year: 2025, Volume and Issue: 13(1)

Published: March 14, 2025

Abstract Artificial intelligence (AI) can transform drug discovery and early development by addressing inefficiencies in traditional methods, which often face high costs, long timelines, low success rates. In this review we provide an overview of how to integrate AI the current process, as it enhance activities like target identification, discovery, clinical development. Through multiomics data analysis network-based approaches, help identify novel oncogenic vulnerabilities key therapeutic targets. models, such AlphaFold, predict protein structures with accuracy, aiding druggability assessments structure-based design. also facilitates virtual screening de novo design, creating optimized molecular for specific biological properties. development, supports patient recruitment analyzing electronic health records improves trial design through predictive modeling, protocol optimization, adaptive strategies. Innovations synthetic control arms digital twins reduce logistical ethical challenges simulating outcomes using real-world or data. Despite these advancements, limitations remain. models may be biased if trained on unrepresentative datasets, reliance historical lead overfitting lack generalizability. Ethical regulatory issues, privacy, challenge implementation AI. conclusion, a comprehensive about into processes. These efforts, although they will demand collaboration between professionals, robust quality, have transformative potential accelerate

Language: Английский

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