AIMC Topic: Drug Discovery

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Trained Immunity: RoadMap for drug discovery and development.

eLife Dec 23, 2025
Trained Immunity is the nonspecific (pathogen agnostic) memory of innate immune cells, characterized by altered responses upon secondary stimulation. This review provides a RoadMap for the discovery and development of therapeutics targeting Trained I...
Humans Immunologic Memory Drug Discovery Trained Immunity Drug Development Artificial Intelligence Immunity, Innate Animals
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kinCSM-RTK: Machine Learning-Based Screening of Receptor Tyrosine Kinase Inhibitors in Drug Discovery.

Journal of chemical information and modeling Dec 21, 2025
Receptor tyrosine kinases (RTKs) are key regulators of cellular functions, such as differentiation, migration and proliferation. Dysregulated RTK activity contributes to various diseases, including neurological disorders and cancer, for which small m...
Receptor Protein-Tyrosine Kinases Tyrosine Kinase Inhibitors Machine Learning Drug Evaluation, Preclinical Drug Discovery Protein Kinase Inhibitors Humans
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Improved ADME Prediction by Multitask Pretraining on Predicted Data: Insights from the ASAP-Polaris-OpenADMET Blind Challenge.

Journal of chemical information and modeling Dec 21, 2025
Absorption, distribution, metabolism, and excretion (ADME) properties are among the key factors in determining the success of lead discovery and optimization campaigns. Fast and accurate prediction of molecular ADME profiles is hence of particular in...
Humans Drug Discovery Pharmaceutical Preparations Pharmacokinetics Neural Networks, Computer Machine Learning
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AI-assisted identification of innovative phytochemicals from Aizoon canariense aimed at brachyury protein in chordoma: a computational strategy.

Scientific reports Dec 19, 2025
Chordoma is an unique and aggressive bone malignancy along with limited therapeutic options, largely due to the undruggable nature of the TBXT oncoprotein. In this study, we employed an AI-assisted drug discovery approach to optimize β-sitosterol fro...
Phytochemicals Molecular Dynamics Simulation Sitosterols Animals Drug Discovery Fetal Proteins Brachyury Protein Chordoma T-Box Domain Proteins Humans Artificial Intelligence
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In silico-driven protocol for hit-to-lead optimization: a case study on PDE9A inhibitors.

Journal of computer-aided molecular design Dec 19, 2025
Hit-to-lead (H2L) optimization is a critical stage in small-molecule drug discovery, where efficient exploration of chemical space is required to identify promising lead compounds. Conventional H2L workflows rely on iterative synthesis and experiment...
Small Molecule Libraries Phosphodiesterase Inhibitors 3',5'-Cyclic-AMP Phosphodiesterases Protein Binding Humans Structure-Activity Relationship Thermodynamics Computer Simulation Drug Design Molecular Docking Simulation Drug Discovery Machine Learning
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LGABAN: An Integrated Multi-Scale Approach Combining Graph and Sequence Features for Enhanced Prediction of Drug-Protein Interactions.

Journal of chemical information and modeling Dec 17, 2025
The accurate identification of drug-target interactions is crucial for shortening the timeline and lowering the expenses of pharmaceutical research, as the discovery of novel drugs remains a highly complex, resource-intensive, and lengthy endeavor. D...
Humans Deep Learning Protein Binding Drug Discovery Proteins Pharmaceutical Preparations
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MambaTransDTA: A Hybrid Mamba-Transformer Architecture for Accurate Drug-Target Binding Affinity Prediction.

Journal of chemical information and modeling Dec 17, 2025
In recent years, deep learning techniques have made significant advances in drug-target affinity (DTA) prediction. However, existing models still have considerable room for improvement in prediction accuracy, robustness, and generalization ability. T...
Pharmaceutical Preparations Drug Discovery Deep Learning Protein Binding
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Discovery of Tetrahydroisoquinoline-Based SARS-CoV-2 Helicase Inhibitors with Iterative, Deep Learning-Enhanced Virtual Screening.

Journal of chemical information and modeling Dec 16, 2025
In this study, we pursued a structure-based drug discovery campaign targeting the SARS-CoV-2 helicase through three rounds of virtual screening (VS) enhanced with Artificial Intelligence (AI). The third round incorporated a deep neural network (DNN) ...
Tetrahydroisoquinolines Drug Evaluation, Preclinical COVID-19 Drug Treatment Enzyme Inhibitors Antiviral Agents Molecular Dynamics Simulation Humans SARS-CoV-2 Molecular Docking Simulation Drug Discovery Deep Learning
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Catalyzing change in MID3 through globalization, education, and innovation.

Journal of pharmacokinetics and pharmacodynamics Dec 16, 2025
The landscape of pharmaceutical research and drug development is undergoing a significant evolution, with Model-Informed Drug Discovery and Development (MID3) as a transformative approach to accelerate innovation. Realizing the full potential of MID3...
Drug Discovery Models, Biological Humans Internationality Drug Development Pharmaceutical Research
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An open-source screening platform accelerates discovery of drug combinations.

Nature communications Dec 15, 2025
Drug combinations are essential to modern medicine, but their discovery remains slow and inefficient as experimental complexity expands rapidly with each additional drug tested. Although modern liquid handling systems enable complex and highly custom...
Drug Combinations Drug Discovery Humans Drug Evaluation, Preclinical High-Throughput Screening Assays Neuroblastoma Cell Line, Tumor Machine Learning
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