AIMC Topic: Drug Discovery
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Artificial intelligence with mass spectrometry-based multimodal molecular profiling methods for advancing therapeutic discovery of infectious diseases.
Pharmacology & therapeutics
Sep 4, 2024
Infectious diseases, driven by a diverse array of pathogens, can swiftly undermine public health systems. Accurate diagnosis and treatment of infectious diseases-centered around the identification of biomarkers and the elucidation of disease mechanis...
Enhancing protein-ligand binding affinity prediction through sequential fusion of graph and convolutional neural networks.
Journal of computational chemistry
Sep 2, 2024
Predicting protein-ligand binding affinity is a crucial and challenging task in structure-based drug discovery. With the accumulation of complex structures and binding affinity data, various machine-learning scoring functions, particularly those base...
Deep active learning with high structural discriminability for molecular mutagenicity prediction.
Communications biology
Aug 31, 2024
The assessment of mutagenicity is essential in drug discovery, as it may lead to cancer and germ cells damage. Although in silico methods have been proposed for mutagenicity prediction, their performance is hindered by the scarcity of labeled molecul...
Leveraging multiple data types for improved compound-kinase bioactivity prediction.
Nature communications
Aug 31, 2024
Machine learning provides efficient ways to map compound-kinase interactions. However, diverse bioactivity data types, including single-dose and multi-dose-response assay results, present challenges. Traditional models utilize only multi-dose data, o...
The role of artificial intelligence in the development of anticancer therapeutics from natural polyphenols: Current advances and future prospects.
Pharmacological research
Aug 30, 2024
Natural polyphenols, abundant in the human diet, are derived from a wide variety of sources. Numerous preclinical studies have demonstrated their significant anticancer properties against various malignancies, making them valuable resources for drug ...
Conformational Space Profiling Enhances Generic Molecular Representation for AI-Powered Ligand-Based Drug Discovery.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Aug 29, 2024
The molecular representation model is a neural network that converts molecular representations (SMILES, Graph) into feature vectors, and is an essential module applied across a wide range of artificial intelligence-driven drug discovery scenarios. Ho...
Harnessing Deep Learning Methods for Voltage-Gated Ion Channel Drug Discovery.
Physiology (Bethesda, Md.)
Aug 27, 2024
Voltage-gated ion channels (VGICs) are pivotal in regulating electrical activity in excitable cells and are critical pharmaceutical targets for treating many diseases including cardiac arrhythmia and neuropathic pain. Despite their significance, chal...
GNN-DDAS: Drug discovery for identifying anti-schistosome small molecules based on graph neural network.
Journal of computational chemistry
Aug 27, 2024
Schistosomiasis is a tropical disease that poses a significant risk to hundreds of millions of people, yet often goes unnoticed. While praziquantel, a widely used anti-schistosome drug, has a low cost and a high cure rate, it has several drawbacks. T...
Discovery of AMPs from random peptides via deep learning-based model and biological activity validation.
European journal of medicinal chemistry
Aug 26, 2024
The ample peptide field is the best source for discovering clinically available novel antimicrobial peptides (AMPs) to address emerging drug resistance. However, discovering novel AMPs is complex and expensive, representing a major challenge. Recent ...
Machine Learning-Driven Discovery and Evaluation of Antimicrobial Peptides from Mucus Proteome.
Marine drugs
Aug 26, 2024
Marine antimicrobial peptides (AMPs) represent a promising source for combating infections, especially against antibiotic-resistant pathogens and traditionally challenging infections. However, traditional drug discovery methods face challenges such a...
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