AIMC Topic: Drug Discovery

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A Combination of Machine Learning and PBPK Modeling Approach for Pharmacokinetics Prediction of Small Molecules in Humans.

Pharmaceutical research Jun 25, 2024
PURPOSE: Recently, there has been rapid development in model-informed drug development, which has the potential to reduce animal experiments and accelerate drug discovery. Physiologically based pharmacokinetic (PBPK) and machine learning (ML) models ...
Male Drug Discovery Caco-2 Cells Blood Proteins Area Under Curve Pharmaceutical Preparations Humans Machine Learning Computer Simulation Models, Biological Pharmacokinetics Administration, Intravenous
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Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery.

Nature communications Jun 25, 2024
Artificial intelligence transforms drug discovery, with phenotype-based approaches emerging as a promising alternative to target-based methods, overcoming limitations like lack of well-defined targets. While chemical-induced transcriptional profiles ...
Deep Learning Pancreatic Neoplasms Drug Repositioning Humans Antineoplastic Agents Gene Expression Profiling Transcriptome Artificial Intelligence Phenotype Drug Discovery
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Advancing drug discovery with deep attention neural networks.

Drug discovery today Jun 24, 2024
In the dynamic field of drug discovery, deep attention neural networks are revolutionizing our approach to complex data. This review explores the attention mechanism and its extended architectures, including graph attention networks (GATs), transform...
Drug Discovery Deep Learning Humans Neural Networks, Computer Drug Design
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AlphaFold2 structures guide prospective ligand discovery.

Science (New York, N.Y.) Jun 21, 2024
AlphaFold2 (AF2) models have had wide impact but mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 models of the σ and serotonin 2A (5-HT2A) receptors, testing hundreds of new molecules a...
Drug Discovery Deep Learning Ligands Humans Molecular Docking Simulation Drug Design Serotonin 5-HT2 Receptor Antagonists Receptors, sigma Small Molecule Libraries Protein Folding Receptor, Serotonin, 5-HT2A Serotonin 5-HT2 Receptor Agonists Protein Conformation Cryoelectron Microscopy
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From Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors.

ChemMedChem Jun 20, 2024
Vascular endothelial growth factor receptor 2 (VEGFR-2) stands as a prominent therapeutic target in oncology, playing a critical role in angiogenesis, tumor growth, and metastasis. FDA-approved VEGFR-2 inhibitors are associated with diverse side effe...
Dose-Response Relationship, Drug Drug Screening Assays, Antitumor Structure-Activity Relationship Deep Learning Cell Line, Tumor Humans Cell Proliferation Molecular Docking Simulation Drug Discovery Antineoplastic Agents Molecular Structure Vascular Endothelial Growth Factor Receptor-2 Protein Kinase Inhibitors
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Task-Similarity is a Crucial Factor for Few-Shot Meta-Learning of Structure-Activity Relationships.

Chembiochem : a European journal of chemical biology Jun 18, 2024
Machine learning models support computer-aided molecular design and compound optimization. However, the initial phases of drug discovery often face a scarcity of training data for these models. Meta-learning has emerged as a potentially promising str...
Humans Machine Learning Drug Discovery Structure-Activity Relationship
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Artificial intelligence for small molecule anticancer drug discovery.

Expert opinion on drug discovery Jun 18, 2024
INTRODUCTION: The transition from conventional cytotoxic chemotherapy to targeted cancer therapy with small-molecule anticancer drugs has enhanced treatment outcomes. This approach, which now dominates cancer treatment, has its advantages. Despite th...
Antineoplastic Agents Deep Learning Big Data Animals Humans Drug Development Neoplasms Artificial Intelligence Drug Resistance, Neoplasm Drug Discovery Molecular Targeted Therapy Machine Learning
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Harnessing machine learning potential for personalised drug design and overcoming drug resistance.

Journal of drug targeting Jun 13, 2024
Drug resistance in cancer treatment presents a significant challenge, necessitating innovative approaches to improve therapeutic efficacy. Integrating machine learning (ML) in cancer research is promising as ML algorithms outrival in analysing comple...
Antineoplastic Agents Bayes Theorem Drug Resistance, Neoplasm Neoplasms Drug Design Drug Discovery Humans Machine Learning Algorithms Precision Medicine
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Topological regression as an interpretable and efficient tool for quantitative structure-activity relationship modeling.

Nature communications Jun 13, 2024
Quantitative structure-activity relationship (QSAR) modeling is a powerful tool for drug discovery, yet the lack of interpretability of commonly used QSAR models hinders their application in molecular design. We propose a similarity-based regression ...
Deep Learning Humans Regression Analysis Algorithms Quantitative Structure-Activity Relationship Drug Discovery
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Synthetically Feasible De Novo Molecular Design of Leads Based on a Reinforcement Learning Model: AI-Assisted Discovery of an Anti-IBD Lead Targeting CXCR4.

Journal of medicinal chemistry Jun 12, 2024
Artificial intelligence (AI) de novo molecular generation provides leads with novel structures for drug discovery. However, the target affinity and synthesizability of the generated molecules present critical challenges for the successful application...
Receptors, CXCR4 Molecular Structure Drug Discovery Mice Structure-Activity Relationship Male Molecular Docking Simulation Drug Design Inflammatory Bowel Diseases Animals Humans Artificial Intelligence
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