AIMC Topic: Drug Discovery

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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-...
Proteins Machine Learning Ligands Indicators and Reagents Molecular Docking Simulation Drug Discovery
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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...
Humans Machine Learning SARS-CoV-2 COVID-19 Drug Treatment Drug Discovery Drug Repositioning
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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 Drug Discovery Computational Biology Neural Networks, Computer Humans Algorithms
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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...
Computational Biology Algorithms Principal Component Analysis Drug Discovery Humans Neural Networks, Computer
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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 Algorithms Drug Repositioning Lung Neoplasms Drug Discovery Humans Neural Networks, Computer
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GraphkmerDTA: integrating local sequence patterns and topological information for drug-target binding affinity prediction and applications in multi-target anti-Alzheimer's drug discovery.

Molecular diversity Jan 10, 2025
Identifying drug-target binding affinity (DTA) plays a critical role in early-stage drug discovery. Despite the availability of various existing methods, there are still two limitations. Firstly, sequence-based methods often extract features from fix...
Humans Neural Networks, Computer Protein Binding Alzheimer Disease Drug Discovery ErbB Receptors Deep Learning
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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...
Humans Pharmaceutical Preparations Small Molecule Libraries Molecular Structure Machine Learning Drug Discovery
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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...
Developing Countries Neglected Diseases Drug Discovery Public Health Humans Artificial Intelligence
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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...
Drug Discovery Proteins Databases, Protein Humans Machine Learning Protein Conformation Cysteine Models, Molecular Ligands
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Attention-based deep learning for accurate cell image analysis.

Scientific reports Jan 8, 2025
High-content analysis (HCA) holds enormous potential for drug discovery and research, but widely used methods can be cumbersome and yield inaccurate results. Noisy and redundant signals in cell images impede accurate deep learning-based image analysi...
Image Processing, Computer-Assisted Mitochondria Humans Neural Networks, Computer Drug Discovery Deep Learning
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