AIMC Topic: Drug Discovery

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Predictive modeling of asthma drug properties using machine learning and topological indices in a MATLAB based QSPR study.

Scientific reports Aug 19, 2025
Machine learning is a vital tool in advancing drug development by accurately predicting the physical, chemical, and biological properties of various compounds. This study utilizes MATLAB program-based algorithms to calculate topological indices and m...
Machine Learning Algorithms Humans Quantitative Structure-Activity Relationship Anti-Asthmatic Agents Drug Discovery Asthma
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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...
Humans Drug Discovery Neurodegenerative Diseases Algorithms Machine Learning Enzyme Inhibitors
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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...
Machine Learning Heterocyclic Compounds Drug Discovery
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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...
Autoencoder Drug Repositioning Algorithms Neural Networks, Computer Computational Biology Drug Discovery 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...
Drug Interactions Machine Learning Drug Discovery
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Machine learning analysis of molecular dynamics properties influencing drug solubility.

Scientific reports Jul 24, 2025
Solubility is critical in drug discovery and development, as it significantly influences a medication's bioavailability and therapeutic efficacy. Understanding solubility at the early stages of drug discovery is essential for minimizing resource cons...
Solubility Molecular Dynamics Simulation Drug Discovery Pharmaceutical Preparations Machine Learning Algorithms Water
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DTIP-WINDGRU a novel drug-target interaction prediction with wind-enhanced gated recurrent unit.

BMC bioinformatics Jul 20, 2025
BACKGROUND: Identification of drug target interactions (DTI) is an important part of the drug discovery process. Since prediction of DTI using laboratory tests is time consuming and laborious, automated tools using computational intelligence (CI) tec...
Machine Learning Humans Drug Discovery Algorithms Computational Biology Deep Learning
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AI-Empowered Molecular Editing Opens a New Horizon in Pesticide Discovery.

Journal of agricultural and food chemistry Jul 15, 2025
Rapid evolution of digital technologies has enabled vital tools in pesticide discovery, which are crucial for agricultural productivity and food security. Therein, molecular editors have emerged as basic and critical tools in this field. However, exi...
Drug Discovery Pesticides Artificial Intelligence
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A hybrid framework of generative deep learning for antiviral peptide discovery.

Scientific reports Jul 15, 2025
Antiviral peptides (AVPs) hold great potential for combating viral infections, yet their discovery and development remain challenging. In this study, we present a hybrid model combining Wasserstein Generative Adversarial Networks with Gradient Penalt...
Humans Peptides Deep Learning Influenza A virus Antiviral Agents Drug Discovery
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Machine Learning-Assisted Iterative Screening for Efficient Detection of Drug Discovery Starting Points.

Journal of medicinal chemistry Jul 14, 2025
High-throughput screening (HTS) remains central to small molecule lead discovery, but increasing assay complexity challenges the screening of large compound libraries. While retrospective studies have assessed active-learning-guided screening, extens...
Small Molecule Libraries Mass Spectrometry Machine Learning Humans High-Throughput Screening Assays Protein Kinase Inhibitors Drug Discovery
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