AIMC Topic: Biological Products
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CSEL-BGC: A Bioinformatics Framework Integrating Machine Learning for Defining the Biosynthetic Evolutionary Landscape of Uncharacterized Antibacterial Natural Products.
Interdisciplinary sciences, computational life sciences
Sep 30, 2024
The sluggish pace of new antibacterial drug development reflects a vulnerability in the face of the current severe threat posed by bacterial resistance. Microbial natural products (NPs), as a reservoir of immense chemical potential, have emerged as t...
Machine Learning for Deconvolution and Segmentation of Hyperspectral Imaging Data from Biopharmaceutical Resins.
Molecular pharmaceutics
Sep 17, 2024
Biopharmaceutical resins are pivotal inert matrices used across industry and academia, playing crucial roles in a myriad of applications. For biopharmaceutical process research and development applications, a deep understanding of the physical and ch...
Leveraging artificial intelligence for better translation of fibre-based pharmaceutical systems into real-world benefits.
Pharmaceutical development and technology
Aug 24, 2024
The increasing prominence of biologics in the pharmaceutical market requires more advanced delivery systems to deliver these delicate and complex drug molecules for better therapeutic outcomes. Fibre technology has emerged as a promising approach for...
Deep learning large-scale drug discovery and repurposing.
Nature computational science
Aug 21, 2024
Large-scale drug discovery and repurposing is challenging. Identifying the mechanism of action (MOA) is crucial, yet current approaches are costly and low-throughput. Here we present an approach for MOA identification by profiling changes in mitochon...
Unlocking plant bioactive pathways: omics data harnessing and machine learning assisting.
Current opinion in biotechnology
May 9, 2024
Plant bioactives hold immense potential in the medicine and food industry. The recent advancements in omics applied in deciphering specialized metabolic pathways underscore the importance of high-quality genome releases and the wealth of data in meta...
PECAN Predicts Patterns of Cancer Cell Cytostatic Activity of Natural Products Using Deep Learning.
Journal of natural products
Feb 13, 2024
Many machine learning techniques are used as drug discovery tools with the intent to speed characterization by determining relationships between compound structure and biological function. However, particularly in anticancer drug discovery, these mod...
Pain-free oral delivery of biologic drugs using intestinal peristalsis-actuated microneedle robots.
Science advances
Jan 5, 2024
Biologic drugs hold immense promise for medical treatments, but their oral delivery remains a daunting challenge due to the harsh digestive environment and restricted gastrointestinal absorption. Here, inspired by the porcupinefish's ability to infla...
Prospective Antiviral Effect of Aqueous Extract against COVID-19 Infection.
Marine drugs
Dec 30, 2023
Marine algal extracts exhibit a potent inhibitory effect against several enveloped and non-enveloped viruses. The infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has several adverse effects, including an increased mortality ...
APOLLO 11 Project, Consortium in Advanced Lung Cancer Patients Treated With Innovative Therapies: Integration of Real-World Data and Translational Research.
Clinical lung cancer
Dec 22, 2023
INTRODUCTION: Despite several therapeutic efforts, lung cancer remains a highly lethal disease. Novel therapeutic approaches encompass immune-checkpoint inhibitors, targeted therapeutics and antibody-drug conjugates, with different results. Several s...
Bioactive Molecules from the Innate Immunity of Ascidians and Innovative Methods of Drug Discovery: A Computational Approach Based on Artificial Intelligence.
Marine drugs
Dec 20, 2023
The study of bioactive molecules of marine origin has created an important bridge between biological knowledge and its applications in biotechnology and biomedicine. Current studies in different research fields, such as biomedicine, aim to discover m...
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