AIMC Topic: Drug Discovery

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Machine learning-assisted affinity ultrafiltration for bioactive natural products discovery:Application to screening of neuraminidase inhibitors from medicinal herbs.

Analytica chimica acta Aug 8, 2025
BACKGROUND: Bioactive natural products represent a vital resource for combating human diseases. However, their discovery often encounters multiple challenges. Bioactivity-guided isolation can yield bioactive compounds but are labor-intensive and have...
Drug Discovery Antiviral Agents Biological Products Humans Machine Learning Influenza A Virus, H1N1 Subtype Enzyme Inhibitors Ultrafiltration Neuraminidase Plants, Medicinal
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Deep learning model enables the discovery of a novel BET inhibitor YD-851.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie Aug 7, 2025
BET inhibitor is a novel strategy in tumor therapy based on targeting epigenetic mechanism. In recent decades, dozens of clinical trials have been conducted to validate the potential efficacy of the first-generation BET inhibitors in refractory cance...
Mice, Inbred BALB C Xenograft Model Antitumor Assays Cell Proliferation Mice, Nude Cell Line, Tumor Mice Female Animals Antineoplastic Agents Humans Drug Discovery Deep Learning
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Targeting neurodegeneration: three machine learning methods for G9a inhibitors discovery using PubChem and scikit-learn.

Journal of computer-aided molecular design Aug 6, 2025
In light of the increasing interest in G9a's role in neuroscience, three machine learning (ML) models, that are time efficient and cost effective, were developed to support researchers in this area. The models are based on data provided by PubChem an...
Drug Discovery Neurodegenerative Diseases Enzyme Inhibitors Machine Learning Humans Algorithms
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Retrospective Benchmarking and Novel Shape-Pharmacophore Based Implementation of the MORLD Method for the Autonomous Optimization of 3-Aroyl-1,4-diarylpyrroles (ARDAP).

Journal of chemical information and modeling Aug 5, 2025
The use of artificial intelligence (AI) is increasingly integral to the drug-discovery process, and among AI-driven methodologies, deep generative models stand out as one of the most promising approaches for hit identification and optimization. Here,...
Benchmarking Tubulin Pyrroles Pharmacophore Molecular Docking Simulation Algorithms Tubulin Modulators Drug Discovery Retrospective Studies
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Robust Quantum Reservoir Learning for Molecular Property Prediction.

Journal of chemical information and modeling Aug 5, 2025
Machine learning has been increasingly utilized in the field of biomedical research to accelerate the drug discovery process. In recent years, the emergence of quantum computing has led to the extensive exploration of quantum machine learning algorit...
Quantum Theory Machine Learning Algorithms Drug Discovery
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Transfer Learning for Heterocycle Retrosynthesis.

Journal of chemical information and modeling Jul 29, 2025
Heterocycles are important scaffolds in medicinal chemistry that can be used to modulate the binding mode as well as the pharmacokinetic properties of drugs. The importance of heterocycles has been exemplified by the publication of numerous data sets...
Drug Discovery Heterocyclic Compounds Machine Learning
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Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN.

BMC bioinformatics Jul 29, 2025
BACKGROUND: The exploration of drug-target interactions (DTIs) is a critical step in drug discovery and drug repurposing. Recently, network-based methods have emerged as a prominent research area for predicting DTIs. These methods excel by extracting...
Computational Biology Drug Repositioning Algorithms Autoencoder Humans Neural Networks, Computer Drug Discovery
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Affinity prediction of inhibitor-kinase based on mixture of experts enhanced by multimodal feature semantic analysis.

International journal of biological macromolecules Jul 28, 2025
Accurate identification of inhibitor-kinase binding affinity is crucial for drug discovery. However, many deep learning models often overlook high-order feature information from biological networks and face challenges related to the cold-start proble...
Protein Kinases Semantics Protein Kinase Inhibitors Protein Interaction Maps Protein Binding Drug Discovery Deep Learning Humans
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Task-Specific Activity Cliff Prediction Method Based on Transfer Learning and a Hyper Connection Graph Model.

Journal of chemical information and modeling Jul 26, 2025
Activity cliffs (ACs) are defined as significant changes in biological activity triggered by minor chemical structural modifications. Accurately predicting ACs is crucial for drug discovery and molecular optimization. Existing approaches often overlo...
Machine Learning Drug Discovery Drug Interactions
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A dual-view deep learning-driven discovery of cinnamoyl anthranilic acid derivatives against orthopoxvirus through targeting host ITGB3.

European journal of medicinal chemistry Jul 25, 2025
The orthopoxvirus genus, particularly the monkeypox virus (MPXV), continues to pose a significant global public health threat. Therefore, the development of novel anti-orthopoxvirus agents remains an urgent priority. Machine learning has proven to be...
Mice Dose-Response Relationship, Drug Structure-Activity Relationship Microbial Sensitivity Tests Antiviral Agents Humans ortho-Aminobenzoates Animals Molecular Structure Drug Discovery Integrin beta3 Cinnamates Deep Learning Orthopoxvirus
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