AIMC Topic: Drug Discovery

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Effective and Explainable Molecular Property Prediction by Chain-of-Thought Enabled Large Language Models and Multi-Modal Molecular Information Fusion.

Journal of chemical information and modeling May 20, 2025
Molecular property prediction (MPP) plays a critical role in drug design and discovery. Due to the multimodal nature of molecular data (e.g., 1D SMILES strings and 2D molecular graphs), multimodal information fusion can generally achieve better perfo...
Drug Design Large Language Models Drug Discovery
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TCoCPIn reveals topological characteristics of chemical protein interaction networks for novel feature discovery.

Scientific reports May 18, 2025
Understanding chemical-protein interactions (CPIs) is crucial for drug discovery and biological research, yet their complexity often challenges traditional methods. We propose TCoCPIn, a novel framework integrating graph neural networks (GNN) with th...
Humans Neural Networks, Computer Algorithms Ibuprofen Tumor Necrosis Factor-alpha Drug Discovery Protein Interaction Maps Computational Biology
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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, ...
Receptors, G-Protein-Coupled Machine Learning Drug Discovery 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 Receptors, Glucocorticoid Algorithms Humans Drug Discovery Ensemble Learning Machine Learning
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Machine learning: Python tools for studying biomolecules and drug design.

Molecular diversity Apr 29, 2025
The increasing adoption of computational methods and artificial intelligence in scientific research has led to a growing interest in versatile tools like Python. In the fields of medical chemistry, biochemistry, and bioinformatics, Python has emerged...
Computational Biology Programming Languages Humans Drug Design Machine Learning Drug Discovery Software
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Big data-driven target identification by machine learning: DRD2 as a therapeutic target for psoriasis.

Journal of dermatological science Apr 26, 2025
BACKGROUND: The development of medical treatments has traditionally relied on researchers leveraging scientific knowledge to hypothesize disease mechanisms and identify therapeutic agents. However, the depletion of novel therapeutic targets has becom...
Drug Discovery Big Data Tumor Necrosis Factor-alpha Animals Disease Models, Animal Psoriasis Interleukin-17 Mice Humans Machine Learning Receptors, Dopamine D2 Molecular Targeted Therapy Skin Dopamine Agonists Imiquimod
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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 Humans Machine Learning Algorithms
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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 Biopolymers Machine Learning Drug Discovery
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KRN-DTI: Towards accurate drug-target interaction prediction with Kolmogorov-Arnold and residual networks.

Methods (San Diego, Calif.) Apr 24, 2025
Predicting drug-target interactions (DTIs) accurately is essential in the field of drug discovery. Recently, artificial intelligence (AI) technologies, especially graph convolutional networks (GCNs), have been developed to tackle this challenge. Howe...
Artificial Intelligence Algorithms Drug Discovery Humans Neural Networks, Computer
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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...
Ligands Binding Sites Models, Molecular RNA Artificial Intelligence Drug Discovery Humans
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