AIMC Topic: Drug Discovery

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A Computational Framework for Predicting Novel Drug Indications Using Graph Convolutional Network With Contrastive Learning.

IEEE journal of biomedical and health informatics Aug 6, 2024
Inferring potential drug indications plays a vital role in the drug discovery process. It can be time-consuming and costly to discover novel drug indications through biological experiments. Recently, graph learning-based methods have gained popularit...
Computational Biology Drug Discovery Deep Learning Humans Neural Networks, Computer Machine Learning Algorithms
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Protein ligand binding site prediction using graph transformer neural network.

PloS one Aug 6, 2024
Ligand binding site prediction is a crucial initial step in structure-based drug discovery. Although several methods have been proposed previously, including those using geometry based and machine learning techniques, their accuracy is considered to ...
Binding Sites Algorithms Databases, Protein Neural Networks, Computer Protein Binding Machine Learning Ligands Drug Discovery Proteins
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Combining machine learning, molecular dynamics, and free energy analysis for (5HT)-2A receptor modulator classification.

Journal of molecular graphics & modelling Aug 5, 2024
The 5-Hydroxytryptamine (5HT)-2A receptor, a key target in psychoactive drug development, presents significant challenges in the design of selective compounds. Here, we describe the construction, evaluation and validation of two machine learning (ML)...
Thermodynamics Drug Discovery Protein Binding Molecular Dynamics Simulation Receptor, Serotonin, 5-HT2A Machine Learning Molecular Docking Simulation Humans
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Generative artificial intelligence for small molecule drug design.

Current opinion in biotechnology Aug 5, 2024
In recent years, the rapid advancement of generative artificial intelligence (GenAI) has revolutionized the landscape of drug design, offering innovative solutions to potentially expedite the discovery of novel therapeutics. GenAI encompasses algorit...
Artificial Intelligence Algorithms Humans Small Molecule Libraries Drug Design Drug Discovery
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Meta Learning With Graph Attention Networks for Low-Data Drug Discovery.

IEEE transactions on neural networks and learning systems Aug 5, 2024
Finding candidate molecules with favorable pharmacological activity, low toxicity, and proper pharmacokinetic properties is an important task in drug discovery. Deep neural networks have made impressive progress in accelerating and improving drug dis...
Humans Algorithms Drug Discovery Deep Learning Neural Networks, Computer Machine Learning
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Navigating the frontier of drug-like chemical space with cutting-edge generative AI models.

Drug discovery today Aug 3, 2024
Deep generative models (GMs) have transformed the exploration of drug-like chemical space (CS) by generating novel molecules through complex, nontransparent processes, bypassing direct structural similarity. This review examines five key architecture...
Humans Drug Discovery Pharmaceutical Preparations Neural Networks, Computer Artificial Intelligence
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Progress in the application of artificial intelligence in molecular generation models based on protein structure.

European journal of medicinal chemistry Jul 31, 2024
The molecular generation models based on protein structures represent a cutting-edge research direction in artificial intelligence-assisted drug discovery. This article aims to comprehensively summarize the research methods and developments by analyz...
Artificial Intelligence Drug Discovery Protein Conformation Humans Proteins Models, Molecular
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A Hybrid GNN Approach for Improved Molecular Property Prediction.

Journal of computational biology : a journal of computational molecular cell biology Jul 31, 2024
The development of new drugs is a vital effort that has the potential to improve human health, well-being and life expectancy. Molecular property prediction is a crucial step in drug discovery, as it helps to identify potential therapeutic compounds....
Computational Biology Drug Discovery Deep Learning Neural Networks, Computer Humans Algorithms
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A New Fingerprint and Graph Hybrid Neural Network for Predicting Molecular Properties.

Journal of chemical information and modeling Jul 25, 2024
Machine learning plays a role in accelerating drug discovery, and the design of effective machine learning models is crucial for accurately predicting molecular properties. Characterizing molecules typically involves the use of molecular fingerprints...
Humans Neural Networks, Computer Machine Learning Cell Line, Tumor Algorithms Drug Discovery
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From mundane to surprising nonadditivity: drivers and impact on ML models.

Journal of computer-aided molecular design Jul 25, 2024
Nonadditivity (NA) in Structure-Activity and Structure-Property Relationship (SAR) data is a rare but very information rich phenomenon. It can indicate conformational flexibility, structural rearrangements, and errors in assay results and structural ...
Molecular Conformation Humans Machine Learning Structure-Activity Relationship Drug Discovery Ligands
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