AIMC Topic: Biological Products
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The latest advances with natural products in drug discovery and opportunities for the future: a 2025 update.
Expert opinion on drug discovery
Jun 5, 2025
INTRODUCTION: The landscape of drug discovery is rapidly evolving, with natural products (NPs) playing a pivotal role in the development of novel therapeutics. Despite their historical significance, challenges persist in fully harnessing their potent...
Advances in brain-targeted delivery strategies and natural product-mediated enhancement of blood-brain barrier permeability.
Journal of nanobiotechnology
May 26, 2025
The blood-brain barrier (BBB) represents a formidable challenge in the treatment of neurological disorders, as it restricts the passage of most therapeutic agents into the central nervous system (CNS). Research in brain-targeted delivery strategies a...
Digital Butterworth filter as preprocessing method for implementing Raman spectroscopy as an analytical method in downstream processing of biopharmaceuticals.
Journal of chromatography. A
May 19, 2025
For implementing Raman spectroscopy as an analytical method in downstream processing, extracting molecular information related to biopharmaceuticals is still challenging due to spectral variations caused by spectrometer, setup and fluorescence. This ...
Empowering natural product science with AI: leveraging multimodal data and knowledge graphs.
Natural product reports
Apr 16, 2025
Artificial intelligence (AI) is accelerating how we conduct science, from folding proteins with AlphaFold and summarizing literature findings with large language models, to annotating genomes and prioritizing newly generated molecules for screening u...
A Review of In Silico Approaches for Discovering Natural Viral Protein Inhibitors in Aquaculture Disease Control.
Journal of fish diseases
Mar 20, 2025
Viral diseases pose a significant threat to the sustainability of global aquaculture, causing economic losses and compromising food security. Traditional control methods often demonstrate limited effectiveness, highlighting the need for alternative a...
Personalized prediction of psoriasis relapse post-biologic discontinuation: a machine learning-driven population cohort study.
The Journal of dermatological treatment
Mar 19, 2025
BACKGROUND: Identifying the risk of psoriasis relapse after discontinuing biologics can help optimize treatment strategies, potentially reducing relapse rates and alleviating the burden of disease management.
Multitarget Natural Compounds for Ischemic Stroke Treatment: Integration of Deep Learning Prediction and Experimental Validation.
Journal of chemical information and modeling
Mar 14, 2025
Ischemic stroke's complex pathophysiology demands therapeutic approaches targeting multiple pathways simultaneously, yet current treatments remain limited. We developed an innovative drug discovery pipeline combining a deep learning approach with exp...
Bidirectional Long Short-Term Memory (BiLSTM) Neural Networks with Conjoint Fingerprints: Application in Predicting Skin-Sensitizing Agents in Natural Compounds.
Journal of chemical information and modeling
Mar 3, 2025
Skin sensitization, or allergic contact dermatitis, represents a critical end point in toxicity assessment, with profound implications for drug safety and regulatory decision-making. This study aims to develop a robust deep-learning-based quantitativ...
Recommendations for Artificial Intelligence Application in Continued Process Verification: A Journey Toward the Challenges and Benefits of AI in the Biopharmaceutical Industry.
PDA journal of pharmaceutical science and technology
Mar 3, 2025
This review paper explores the transformative impact of Artificial Intelligence (AI) on Continued Process Verification (CPV) in the biopharmaceutical industry. Originating from the CPV of the Future project, the study investigates the challenges and ...
New solutions for antibiotic discovery: Prioritizing microbial biosynthetic space using ecology and machine learning.
PLoS biology
Feb 28, 2025
With the explosive increase in genome sequence data, perhaps the major challenge in natural-product-based drug discovery is the identification of gene clusters most likely to specify new chemistry and bioactivities. We discuss the challenges and stat...
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