AIMC Topic: Drug Discovery

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Exploring a new paradigm for serum-accessible component rules of natural medicines using machine learning and development and validation of a direct predictive model.

International journal of pharmaceutics Jan 16, 2025
In the field of pharmaceutical research, Lipinski's Rule of Five (RO5) was once widely regarded as the prevailing standard for the development of novel drugs. Despite the fact that an increasing number of recently approved drugs no longer adhere to t...
Drug Discovery Humans Biological Products Machine Learning Reproducibility of Results Animals
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Applications of Machine Learning Approaches for the Discovery of SARS-CoV-2 PLpro Inhibitors.

Journal of chemical information and modeling Jan 16, 2025
The global impact of SARS-CoV-2 highlights the need for treatments beyond vaccination, given the limited availability of effective medications. While Pfizer introduced , an FDA-approved antiviral targeting the SARS-CoV-2 main protease (Mpro), this st...
SARS-CoV-2 Small Molecule Libraries Coronavirus 3C Proteases Molecular Docking Simulation Coronavirus Papain-Like Proteases High-Throughput Screening Assays Drug Discovery Antiviral Agents Humans Protease Inhibitors COVID-19 Drug Treatment Machine Learning
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Active Learning to Select the Most Suitable Reagents and One-Step Organic Chemistry Reactions for Prioritizing Target-Specific Hits from Ultralarge Chemical Spaces.

Journal of chemical information and modeling Jan 16, 2025
Designing chemically novel and synthesizable ligands from the largest possible chemical space is a major issue in modern drug discovery to identify early hits that are easily amenable to medicinal chemistry optimization. Starting from the sole three-...
Machine Learning Molecular Docking Simulation Ligands Indicators and Reagents Drug Discovery Proteins
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MVCL-DTI: Predicting Drug-Target Interactions Using a Multiview Contrastive Learning Model on a Heterogeneous Graph.

Journal of chemical information and modeling Jan 15, 2025
Accurate prediction of drug-target interactions (DTIs) is pivotal for accelerating the processes of drug discovery and drug repurposing. MVCL-DTI, a novel model leveraging heterogeneous graphs for predicting DTIs, tackles the challenge of synthesizin...
Machine Learning SARS-CoV-2 Drug Repositioning COVID-19 Drug Treatment Drug Discovery Humans
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Predicting drug combination side effects based on a metapath-based heterogeneous graph neural network.

BMC bioinformatics Jan 15, 2025
In recent years, combined drug screening has played a very important role in modern drug discovery. Generally, synergistic drug combinations are crucial in treatment for many diseases. However, the toxic side effects of drug combinations are probably...
Graph Neural Networks Drug Combinations Drug-Related Side Effects and Adverse Reactions Humans Drug Discovery Neural Networks, Computer Algorithms Computational Biology
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DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks.

BMC bioinformatics Jan 13, 2025
Drug-target interactions (DTIs) are pivotal in drug discovery and development, and their accurate identification can significantly expedite the process. Numerous DTI prediction methods have emerged, yet many fail to fully harness the feature informat...
Humans Neural Networks, Computer Algorithms Principal Component Analysis Drug Discovery Computational Biology
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MCF-DTI: Multi-Scale Convolutional Local-Global Feature Fusion for Drug-Target Interaction Prediction.

Molecules (Basel, Switzerland) Jan 12, 2025
Predicting drug-target interactions (DTIs) is a crucial step in the development of new drugs and drug repurposing. In this paper, we propose a novel drug-target prediction model called MCF-DTI. The model utilizes the SMILES representation of drugs an...
Antineoplastic Agents Drug Repositioning Humans Algorithms Lung Neoplasms Neural Networks, Computer Drug Discovery
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Progress of machine learning in the application of small molecule druggability prediction.

European journal of medicinal chemistry Jan 10, 2025
Machine learning (ML) has become an important tool for predicting the pharmaceutical properties of small molecules. Recent advancements in ML algorithms enable the rapid and accurate evaluation of solubility, activity, toxicity, pharmacokinetics, and...
Drug Discovery Molecular Structure Humans Machine Learning Pharmaceutical Preparations Small Molecule Libraries
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AI-powered drug discovery for neglected diseases: accelerating public health solutions in the developing world.

Journal of global health Jan 10, 2025
The emergence of artificial intelligence (AI) in drug discovery represents a transformative development in addressing neglected diseases, particularly in the context of the developing world. Neglected diseases, often overlooked by traditional pharmac...
Artificial Intelligence Drug Discovery Developing Countries Neglected Diseases Public Health Humans
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CovCysPredictor: Predicting Selective Covalently Modifiable Cysteines Using Protein Structure and Interpretable Machine Learning.

Journal of chemical information and modeling Jan 8, 2025
Targeted covalent inhibition is a powerful therapeutic modality in the drug discoverer's toolbox. Recent advances in covalent drug discovery, in particular, targeting cysteines, have led to significant breakthroughs for traditionally challenging targ...
Ligands Drug Discovery Proteins Protein Conformation Humans Cysteine Databases, Protein Machine Learning Models, Molecular
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