Artificial Intelligence in Natural Product Drug Discovery: Current Applications and Future Perspectives

Journal of Medicinal Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 6, 2025

Drug discovery, a multifaceted process from compound identification to regulatory approval, historically plagued by inefficiencies and time lags due limited data utilization, now faces urgent demands for accelerated lead identification. Innovations in biological computational chemistry have spurred shift trial-and-error methods holistic approaches medicinal chemistry. Computational techniques, particularly artificial intelligence (AI), notably machine learning (ML) deep (DL), revolutionized drug development, enhancing analysis predictive modeling. Natural products (NPs) long served as rich sources of biologically active compounds, with many successful drugs originating them. Advances information science expanded NP-related databases, enabling deeper exploration AI. Integrating AI into NP discovery promises discoveries, leveraging AI's analytical prowess, including generative synthesis. This perspective illuminates current landscape addressing strengths, limitations, future trajectories advance this vital research domain.

Language: Английский

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Future prospective of AI in drug discovery

Advances in pharmacology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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Boosting engineering strategies for plastic hydrocracking applications: a machine learning-based multi-objective optimization framework

Green Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

A novel waste plastic pyrolysis oil hydrocracking process uniquely integrating simulation with advanced deep learning models for multi-objective optimization.

Language: Английский

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Healthcare Security Challenges Leveraging Generative AI to Transform Cybersecurity

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 205 - 250

Published: Feb. 28, 2025

Generative AI technologies, such as GANs and Transformer-based models, are transforming healthcare cybersecurity. In healthcare, they improve medical imaging, diagnostics, personalized treatments, enhancing patient outcomes operational efficiency. cybersecurity, generative strengthens defenses through real-time threat detection, anomaly identification, synthetic data generation for secure testing, tackling modern cyber threats. Both fields, however, face challenges in quality, ethics, transparency, regulation. Addressing these requires domain-specific frameworks like the Technology Acceptance Model (TAM) Zero Trust Architecture (ZTA) This chapter explores AI's impact, highlighting challenges, tailored solutions, strategic to ensure ethical effectiveness. As evolves, it stands a cornerstone progress both balancing innovation with responsibility.

Language: Английский

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Utilizing machine learning and molecular dynamics for enhanced drug delivery in nanoparticle systems

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 4, 2024

Materials data science and machine learning (ML) are pivotal in advancing cancer treatment strategies beyond traditional methods like chemotherapy. Nanotherapeutics, which merge nanotechnology with targeted drug delivery, exemplify this advancement by offering improved precision reduced side effects therapy. The development of these nanotherapeutic agents depends critically on understanding nanoparticle (NP) properties their biological interactions, often analyzed through molecular dynamics (MD) simulations. This study enhances analyses integrating ML MD simulations, significantly improving both prediction accuracy computational efficiency. We introduce a comprehensive three-stage methodology for predicting the solvent-accessible surface area (SASA) NPs, is crucial therapeutic efficacy. process involves training an model to forecast many-body tensor representation (MBTR) future time steps, applying augmentation increase dataset realism, refining SASA predictor augmented original data. Results demonstrate that our can predict values 299 steps ahead 40-fold speed improvement 25% over existing methods. Importantly, it provides 300-fold compared simulation techniques, substantial cost savings research development.

Language: Английский

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Advancements in Virtual Bioequivalence: A Systematic Review of Computational Methods and Regulatory Perspectives in the Pharmaceutical Industry

Pharmaceutics, Journal Year: 2024, Volume and Issue: 16(11), P. 1414 - 1414

Published: Nov. 3, 2024

Background/Objectives: The rise of virtual bioequivalence studies has transformed the pharmaceutical landscape, enabling more efficient drug development processes. This systematic review aims to explore advancements in physiologically based pharmacokinetic (PBPK) modeling, its regulatory implications, and role achieving bioequivalence, particularly for complex formulations. Methods: We conducted a clinical trials using computational methods, PBPK carry out assessments. Eligibility criteria are emphasized during silico modeling simulations. Comprehensive literature searches were performed across databases such as PubMed, Scopus, Cochrane Library. A search strategy key terms Boolean operators ensured that extensive coverage was achieved. adhered PRISMA guidelines regard study selection, data extraction, quality assessment, focusing on characteristics, methodologies, outcomes, perspectives from FDA EMA. Results: Our findings indicate significantly enhances prediction profiles, optimizing dosing regimens, while minimizing need trials. Regulatory agencies have recognized this utility, with EMA developing frameworks integrate methods into evaluations. However, challenges heterogeneity publication bias may limit generalizability results. Conclusions: highlights critical standardized protocols robust facilitate integration methodologies practices. By embracing these advancements, industry can improve efficiency patient paving way innovative therapeutic solutions. Continued research adaptive will be essential navigating evolving field.

Language: Английский

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AI-Enhanced Multi-Algorithm R Shiny App for Predictive Modeling and Analytics- A Case study of Alzheimer’s Disease Diagnostics (Preprint)

Published: Dec. 18, 2024

Recent studies have demonstrated that AI can surpass medical practitioners in diagnostic accuracy, underscoring the increasing importance of AI-assisted diagnosis healthcare. This research introduces SMART-Pred (Shiny Multi-Algorithm R Tool for Predictive Modeling), an innovative AI-based application Alzheimer's disease (AD) prediction utilizing handwriting analysis Our objective is to develop and evaluate a non-invasive, cost-effective, efficient tool early AD detection, addressing need accessible accurate screening methods. methodology employs comprehensive approach AI-driven prediction. We begin with Principal Component Analysis dimensionality reduction, ensuring processing complex data. followed by training evaluation ten diverse, highly optimized models, including logistic regression, Naïve Bayes, random forest, AdaBoost, Support Vector Machine, neural networks. multi-model allows robust comparison different machine learning techniques To rigorously assess model performance, we utilize range metrics sensitivity, specificity, F1-score, ROC-AUC. These provide holistic view each model's predictive capabilities. For validation, leveraged DARWIN dataset, which comprises samples from 174 participants (89 patients 85 healthy controls). balanced dataset ensures fair our models' ability distinguish between individuals based on characteristics. The forest strong achieving accuracy 88.68% test set during analysis. Meanwhile, AdaBoost algorithm exhibited even higher reaching 92.00% after leveraging models identify most significant variables predicting disease. results current clinical tools, typically achieve around 81.00% accuracy. SMART-Pred's performance aligns recent advancements prediction, such as Cambridge scientists' 82.00% identifying progression within three years using cognitive tests MRI scans. Furthermore, revealed consistent pattern across all employed. "air_time" "paper_time" consistently stood out critical predictors (AD). two factors were repeatedly identified influential assessing probability onset, their potential detection risk assessment Even though some limitations exist SMART-Pred, it offers several advantages, being efficient, customizable datasets diagnostics. study demonstrates transformative healthcare, particularly may contribute improved patient outcomes through intervention. Clinical validation necessary confirm whether key this are sufficient accurately real-world settings. step crucial ensure practical applicability reliability these findings practice.

Language: Английский

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Synergizing Human and Machine

Advances in environmental engineering and green technologies book series, Journal Year: 2024, Volume and Issue: unknown, P. 249 - 282

Published: Nov. 1, 2024

Rapid technological breakthroughs in the 21st century have changed knowledge discovery and management, especially with AI. AI is great at processing massive datasets quickly accurately but lacks contextual awareness, ethical judgment, creative problem-solving. The mismatch highlights a key gap: present systems often function silos, analyzing data humans interpreting results, missing potential for deeper insights. We propose new framework combining AI's computing power human cognition. show that hybrid strategy can improve complex multidisciplinary environments using these complementary forces. According to our findings, this integration enhances efficiency generates more meaningful human-valued This research significant because it promotes dynamic iterative process, which healthcare education decision-making.

Language: Английский

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