AIMC Topic: Drug Discovery

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Developing muscarinic receptor M1 classification models utilizing transfer learning and generative AI techniques.

Scientific reports May 12, 2025
Muscarinic receptor subtype 1 (M1) is a G protein-coupled receptor (GPCR) and a key pharmacological target for peripheral neuropathy, chronic obstructive pulmonary disease, nerve agent exposures, and cognitive disorders. Screening and identifying com...
Drug Discovery Receptor, Muscarinic M1 Humans Machine Learning
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MHNfs: Prompting In-Context Bioactivity Predictions for Low-Data Drug Discovery.

Journal of chemical information and modeling May 12, 2025
Today's drug discovery increasingly relies on computational and machine learning approaches to identify novel candidates, yet data scarcity remains a significant challenge. To address this limitation, we present , an application specifically designed...
Machine Learning Drug Discovery
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IFPTML Multi-Output Model for Anti-Retroviral Compounds Including the Drug Structure and Target Protein Sequence Information.

Journal of chemical information and modeling May 12, 2025
Retroviruses such as HIV cause significant diseases in humans and other organisms, making the discovery of antiretroviral (ARV) drugs a critical priority. While databases like ChEMBL contain valuable information, their complexity poses challenges. Th...
Amino Acid Sequence Viral Proteins Drug Discovery Anti-Retroviral Agents Humans Machine Learning
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Accelerating antibody discovery and optimization with high-throughput experimentation and machine learning.

Journal of biomedical science May 9, 2025
The integration of high-throughput experimentation and machine learning is transforming data-driven antibody engineering, revolutionizing the discovery and optimization of antibody therapeutics. These approaches employ extensive datasets comprising a...
High-Throughput Screening Assays Drug Discovery Humans Protein Engineering Antibodies Machine Learning
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SMFF-DTA: using a sequential multi-feature fusion method with multiple attention mechanisms to predict drug-target binding affinity.

BMC biology May 9, 2025
BACKGROUND: Drug-target binding affinity (DTA) prediction can accelerate the drug screening process, and deep learning techniques have been used in all facets of drug research. Affinity prediction based on deep learning methods has proven crucial to ...
Protein Binding Drug Discovery Deep Learning
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Discovery, Biological Evaluation and Binding Mode Investigation of Novel Butyrylcholinesterase Inhibitors Through Hybrid Virtual Screening.

Molecules (Basel, Switzerland) May 8, 2025
Butyrylcholinesterase (BChE), plays a critical role in alleviating the symptoms of Alzheimer's disease (AD) by regulating acetylcholine levels, emerging as an attractive target for AD treatment. This study employed a quantitative structure-activity r...
Molecular Dynamics Simulation Drug Evaluation, Preclinical Protein Binding Neuroprotective Agents Humans Cholinesterase Inhibitors Machine Learning Quantitative Structure-Activity Relationship Alzheimer Disease Butyrylcholinesterase Molecular Docking Simulation Ligands Drug Discovery
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Machine learning assisted in Silico discovery and optimization of small molecule inhibitors targeting the Nipah virus glycoprotein.

Scientific reports May 8, 2025
The Nipah virus (NiV), a lethal pathogen from the Paramyxoviridae family, presents a significant global health threat as a result of its high mortality rate and inter-human transmission. This investigation employed in silico methods that were assiste...
Humans Machine Learning Glycoproteins Molecular Dynamics Simulation Viral Proteins Drug Discovery Nipah Virus Antiviral Agents Small Molecule Libraries Computer Simulation Molecular Docking Simulation
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A Multi-Modal Graph Neural Network Framework for Parkinson's Disease Therapeutic Discovery.

International journal of molecular sciences May 7, 2025
Parkinson's disease (PD) is a complex neurodegenerative disorder lacking effective disease-modifying treatments. In this study, we integrated large-scale protein-protein interaction networks with a multi-modal graph neural network (GNN) to identify a...
Neural Networks, Computer Drug Discovery Humans Drug Repositioning Graph Neural Networks Protein Interaction Maps Parkinson Disease
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SimSon: simple contrastive learning of SMILES for molecular property prediction.

Bioinformatics (Oxford, England) May 6, 2025
MOTIVATION: Molecular property prediction with deep learning has accelerated drug discovery and retrosynthesis. However, the shortage of labeled molecular data and the challenge of generalizing across the vast chemical spaces pose significant hurdles...
Computational Biology Cheminformatics Drug Discovery Deep Learning
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Scoring protein-ligand binding structures through learning atomic graphs with inter-molecular adjacency.

PLoS computational biology May 1, 2025
With a burgeoning number of artificial intelligence (AI) applications in various fields, biomolecular science has also given a big welcome to advanced AI techniques in recent years. In this broad field, scoring a protein-ligand binding structure to o...
Computational Biology Proteins Deep Learning Protein Binding Protein Conformation Algorithms Models, Molecular Binding Sites Drug Discovery Ligands
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