AIMC Topic: Drug Evaluation, Preclinical
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CPHNet: a novel pipeline for anti-HAPE drug screening via deep learning-based Cell Painting scoring.
Respiratory research
Mar 8, 2025
BACKGROUND: High altitude pulmonary edema (HAPE) poses a significant medical challenge to individuals ascending rapidly to high altitudes. Hypoxia-induced cellular morphological changes in the alveolar-capillary barrier such as mitochondrial structur...
Machine learning enables high-throughput, low-replicate screening for novel anti-seizure targets and compounds using combined movement and calcium fluorescence in larval zebrafish.
European journal of pharmacology
Feb 4, 2025
Identifying new anti-seizure medications (ASMs) is difficult due to limitations in animal-based assays. Zebrafish (Danio rerio) serve as a model for chemical and genetic seizures, but current methods for detecting anti-seizure responses are limited b...
Evaluating the synergistic use of advanced liver models and AI for the prediction of drug-induced liver injury.
Expert opinion on drug metabolism & toxicology
Feb 2, 2025
INTRODUCTION: Drug-induced liver injury (DILI) is a leading cause of acute liver failure. Hepatotoxicity typically occurs only in a subset of individuals after prolonged exposure and constitutes a major risk factor for the termination of drug develop...
AVP-GPT2: A Transformer-Powered Platform for De Novo Generation, Screening, and Explanation of Antiviral Peptides.
Viruses
Dec 25, 2024
Human respiratory syncytial virus (RSV) remains a significant global health threat, particularly for vulnerable populations. Despite extensive research, effective antiviral therapies are still limited. To address this urgent need, we present AVP-GPT2...
Next-Gen Therapeutics: Pioneering Drug Discovery with iPSCs, Genomics, AI, and Clinical Trials in a Dish.
Annual review of pharmacology and toxicology
Dec 17, 2024
In the high-stakes arena of drug discovery, the journey from bench to bedside is hindered by a daunting 92% failure rate, primarily due to unpredicted toxicities and inadequate therapeutic efficacy in clinical trials. The FDA Modernization Act 2.0 he...
Combined usage of ligand- and structure-based virtual screening in the artificial intelligence era.
European journal of medicinal chemistry
Dec 10, 2024
Drug design has always been pursuing techniques with time- and cost-benefits. Virtual screening, generally classified as ligand-based (LBVS) and structure-based (SBVS) approaches, could identify active compounds in the large chemical library to reduc...
Machine Learning-Assisted High-Throughput Screening for Anti-MRSA Compounds.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
BACKGROUND: Antimicrobial resistance is a major public health threat, and new agents are needed. Computational approaches have been proposed to reduce the cost and time needed for compound screening.
Implementing enclosed sterile integrated robotic platforms to improve cell-based screening for drug discovery.
SLAS technology
Dec 5, 2024
At GSK, we have implemented custom integrated robotics platforms housed in bespoke biosafety enclosures to augment our capabilities in advanced cellular screening. Here we present and discuss the impact of one such system, the Cellular Automated Scre...
An object detection-based model for automated screening of stem-cells senescence during drug screening.
Neural networks : the official journal of the International Neural Network Society
Nov 23, 2024
Deep learning-based cell senescence detection is crucial for accurate quantitative analysis of senescence assessment. However, senescent cells are small in size and have little differences in appearance and shape in different states, which leads to i...
vScreenML v2.0: Improved Machine Learning Classification for Reducing False Positives in Structure-Based Virtual Screening.
International journal of molecular sciences
Nov 18, 2024
The enthusiastic adoption of make-on-demand chemical libraries for virtual screening has highlighted the need for methods that deliver improved hit-finding discovery rates. Traditional virtual screening methods are often inaccurate, with most compoun...
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