AIMC Topic: Drug Discovery

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Computational discovery of novel PI3KC2α inhibitors using structure-based pharmacophore modeling, machine learning and molecular dynamic simulation.

Journal of molecular graphics & modelling Mar 15, 2025
PI3KC2α is a lipid kinase associated with cancer metastasis and thrombosis. In this study, we present a novel computational workflow integrating structure-based pharmacophore modeling, machine learning (ML), and molecular dynamics (MD) simulations to...
Humans Molecular Dynamics Simulation Molecular Docking Simulation Machine Learning Ligands Drug Discovery Pharmacophore Protein Binding Protein Kinase Inhibitors Phosphatidylinositol 3-Kinases Phosphoinositide-3 Kinase Inhibitors
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Enhancing clinical trial outcome prediction with artificial intelligence: a systematic review.

Drug discovery today Mar 15, 2025
Clinical trials are pivotal in drug development yet fraught with uncertainties and resource-intensive demands. The application of AI models to forecast trial outcomes could mitigate failures and expedite the drug discovery process. This review discus...
Clinical Trials as Topic Drug Discovery Drug Development Humans Artificial Intelligence
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Employing Automated Machine Learning (AutoML) Methods to Facilitate the ADMET Properties Prediction.

Journal of chemical information and modeling Mar 14, 2025
The rationale for using ADMET prediction tools in the early drug discovery paradigm is to guide the design of new compounds with favorable ADMET properties and ultimately minimize the attrition rates of drug failures. Artificial intelligence (AI) in ...
Drug Discovery Pharmaceutical Preparations Automation Algorithms Computer Simulation Machine Learning
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Multitarget Natural Compounds for Ischemic Stroke Treatment: Integration of Deep Learning Prediction and Experimental Validation.

Journal of chemical information and modeling Mar 14, 2025
Ischemic stroke's complex pathophysiology demands therapeutic approaches targeting multiple pathways simultaneously, yet current treatments remain limited. We developed an innovative drug discovery pipeline combining a deep learning approach with exp...
Biological Products Ischemic Stroke Quantitative Structure-Activity Relationship PC12 Cells Rats Neuroprotective Agents Drug Discovery Deep Learning Animals Molecular Docking Simulation
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Molecular Generation for CNS Drug Discovery and Design.

ACS chemical neuroscience Mar 13, 2025
Computational drug design is a rapidly evolving field, especially the latest breakthroughs in generative artificial intelligence (GenAI) to create new compounds. However, the potential of GenAI to address the challenges in designing central nervous s...
Drug Discovery Central Nervous System Agents Artificial Intelligence Animals Blood-Brain Barrier Drug Design Humans
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Rapid traversal of vast chemical space using machine learning-guided docking screens.

Nature computational science Mar 13, 2025
The accelerating growth of make-on-demand chemical libraries provides unprecedented opportunities to identify starting points for drug discovery with virtual screening. However, these multi-billion-scale libraries are challenging to screen, even for ...
Algorithms Molecular Docking Simulation Humans Machine Learning Ligands Small Molecule Libraries Drug Discovery Receptors, G-Protein-Coupled
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Application of Machine Learning (ML) approach in discovery of novel drug targets against Leishmania: A computational based approach.

Computational biology and chemistry Mar 12, 2025
Molecules with potent anti-leishmanial activity play a crucial role in identifying treatments for leishmaniasis and aiding in the design of novel drugs to combat the disease, ultimately protecting individuals and populations. Various methods have bee...
Machine Learning Humans Drug Discovery Antiprotozoal Agents Leishmaniasis Leishmania
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GramSeq-DTA: A Grammar-Based Drug-Target Affinity Prediction Approach Fusing Gene Expression Information.

Biomolecules Mar 12, 2025
Drug-target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that...
Drug Discovery Humans Neural Networks, Computer Pharmaceutical Preparations
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Leveraging machine learning for drug repurposing in rheumatoid arthritis.

Drug discovery today Mar 11, 2025
Rheumatoid arthritis (RA) presents a significant challenge in clinical management because of the dearth of effective drugs despite advances in understanding its mechanisms. Drug repurposing has emerged as a promising strategy to address this gap, off...
Humans Drug Discovery Drug Repositioning Machine Learning Antirheumatic Agents Arthritis, Rheumatoid Animals
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Multitask Deep Learning Models of Combined Industrial Absorption, Distribution, Metabolism, and Excretion Datasets to Improve Generalization.

Molecular pharmaceutics Mar 7, 2025
The optimization of absorption, distribution, metabolism, and excretion (ADME) profiles of compounds is critical to the drug discovery process. As such, machine learning (ML) models for ADME are widely used for prioritizing the design and synthesis o...
Drug Discovery Deep Learning Pharmaceutical Preparations Humans Neural Networks, Computer
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