AIMC Topic: Drug Discovery

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Labels as a feature: Network homophily for systematically annotating human GPCR drug-target interactions.

Nature communications May 3, 2025
Machine learning has revolutionized drug discovery by enabling the exploration of vast, uncharted chemical spaces essential for discovering novel patentable drugs. Despite the critical role of human G protein-coupled receptors in FDA-approved drugs, ...
Machine Learning Drug Discovery Receptors, G-Protein-Coupled Humans Neural Networks, Computer
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M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy.

BMC bioinformatics Apr 30, 2025
Accelerating drug discovery for glucocorticoid receptor (GR)-related disorders, including innovative machine learning (ML)-based approaches, holds promise in advancing therapeutic development, optimizing treatment efficacy, and mitigating adverse eff...
Computational Biology Algorithms Ensemble Learning Humans Drug Discovery Receptors, Glucocorticoid Machine Learning
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OPTUNA optimization for predicting chemical respiratory toxicity using ML models.

Journal of computer-aided molecular design Apr 26, 2025
Predicting molecular toxicity is an important stage in the process of drug discovery. It is directly related to medical destiny and human health. This paper presents an enhanced model for chemical respiratory toxicity prediction. It used a combinatio...
Drug Discovery Machine Learning Algorithms Humans
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The QDπ dataset, training data for drug-like molecules and biopolymer fragments and their interactions.

Scientific data Apr 25, 2025
The development of universal machine learning potentials (MLP) for small organic and drug-like molecules requires large, accurate datasets that span diverse chemical spaces. In this study, we introduce the QDπ dataset which incorporates data taken fr...
Neural Networks, Computer Pharmaceutical Preparations Machine Learning Biopolymers Drug Discovery
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Artificial intelligence for RNA-ligand interaction prediction: advances and prospects.

Drug discovery today Apr 24, 2025
Accurate prediction of RNA-ligand interactions is vital for understanding biological processes and advancing RNA-targeted drug discovery. Given their complexity, artificial intelligence (AI) is revolutionizing the study of RNA-ligand interactions, of...
Artificial Intelligence Drug Discovery Humans Ligands Binding Sites Models, Molecular RNA
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DrugGen enhances drug discovery with large language models and reinforcement learning.

Scientific reports Apr 18, 2025
Traditional drug design faces significant challenges due to inherent chemical and biological complexities, often resulting in high failure rates in clinical trials. Deep learning advancements, particularly generative models, offer potential solutions...
Deep Learning Ligands Large Language Models Drug Design Drug Discovery Humans Algorithms
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Advances in artificial intelligence-envisioned technologies for protein and nucleic acid research.

Drug discovery today Apr 17, 2025
Artificial intelligence (AI) and machine learning (ML) have revolutionized pharmaceutical research, particularly in protein and nucleic acid studies. This review summarizes the current status of AI and ML applications in the pharmaceutical sector, fo...
Precision Medicine Animals Artificial Intelligence Machine Learning Proteins Nucleic Acids Drug Discovery Drug Development Humans
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Artificial intelligence in preclinical research: enhancing digital twins and organ-on-chip to reduce animal testing.

Drug discovery today Apr 17, 2025
Artificial intelligence (AI) is reshaping preclinical drug research offering innovative alternatives to traditional animal testing. Advanced techniques, including machine learning (ML), deep learning (DL), AI-powered digital twins (DTs), and AI-enhan...
Lab-On-A-Chip Devices Machine Learning Drug Discovery Animals Drug Development Humans Drug Evaluation, Preclinical Artificial Intelligence Animal Testing Alternatives
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Ligand-Based Drug Discovery Leveraging State-of-the-Art Machine Learning Methodologies Exemplified by Cdr1 Inhibitor Prediction.

Journal of chemical information and modeling Apr 16, 2025
Artificial intelligence (AI) is revolutionizing drug discovery with unprecedented speed and efficiency. In computer-aided drug design, structure-based and ligand-based methodologies are the main driving forces for innovation. In cases where no experi...
Bayes Theorem Humans Machine Learning Ligands Drug Discovery
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Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML.

Journal of chemical information and modeling Apr 15, 2025
The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate. Different techniques are available to address the class i...
Support Vector Machine Random Forest Machine Learning Drug Discovery Algorithms Drug Development
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