AIMC Topic: Biological Products
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Deep learning based predictive modeling to screen natural compounds against TNF-alpha for the potential management of rheumatoid arthritis: Virtual screening to comprehensive in silico investigation.
PloS one
Dec 5, 2024
Rheumatoid arthritis (RA) affects an estimated 0.1% to 2.0% of the world's population, leading to a substantial impact on global health. The adverse effects and toxicity associated with conventional RA treatment pathways underscore the critical need ...
Machine learning-assisted SERS sensor for fast and ultrasensitive analysis of multiplex hazardous dyes in natural products.
Journal of hazardous materials
Nov 19, 2024
The adulteration of natural products with multiple azo dyes has become a serious public health concern. Thus, on-site trace additive detection is demanded. Herein, we developed a gold-nanorod-based surface-enhanced Raman scattering (SERS) sensor to d...
Target Fisher: A Consensus Structure-Based Target Prediction Tool, and its Application in the Discovery of Selective MAO-B Inhibitors.
Chemistry (Weinheim an der Bergstrasse, Germany)
Nov 14, 2024
In this work we introduce Target Fisher, a consensus structure-based target prediction tool that integrates molecular docking and machine learning with the aim to aid in the identification of potential biological targets and the optimization of the u...
Screening for Potential Antiviral Compounds from Cyanobacterial Secondary Metabolites Using Machine Learning.
Marine drugs
Nov 5, 2024
The secondary metabolites of seawater and freshwater blue-green algae are a rich natural product pool containing diverse compounds with various functions, including antiviral compounds; however, high-efficiency methods to screen such compounds are la...
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...
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