AIMC Topic: Drug Discovery

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FGFR1Pred: an artificial intelligence-based model for predicting fibroblast growth factor receptor 1 inhibitor.

Molecular diversity Aug 11, 2023
Fibroblast growth factor receptors (FGFRs) are a family of cell surface receptors that bind to fibroblast growth factor (FGF) and mediate various cellular functions (translocating proteins, tissue repair, cell proliferation, development, and differen...
Receptor, Fibroblast Growth Factor, Type 1 Humans Artificial Intelligence Drug Discovery Antineoplastic Agents Protein Kinase Inhibitors
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A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies.

Chemical research in toxicology Aug 10, 2023
Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches hav...
Genomics Drug Interactions Drug-Related Side Effects and Adverse Reactions Drug Discovery Deep Learning Animals
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Development and validation of machine learning models for the prediction of SH-2 containing protein tyrosine phosphatase 2 inhibitors.

Molecular diversity Aug 8, 2023
Discovery and development of a new drug to the market is a highly challenging and resource consuming process. Although, modern drug discovery technologies have enabled the rapid identification of lead compounds, translation of the lead compounds into...
Drug Discovery Models, Molecular Machine Learning Reproducibility of Results Protein Tyrosine Phosphatase, Non-Receptor Type 11 Enzyme Inhibitors Small Molecule Libraries Humans
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Streamlining Large Chemical Library Docking with Artificial Intelligence: the PyRMD2Dock Approach.

Journal of chemical information and modeling Aug 8, 2023
The present contribution introduces a novel computational protocol called PyRMD2Dock, which combines the Ligand-Based Virtual Screening (LBVS) tool PyRMD with the popular docking software AutoDock-GPU (AD4-GPU) to enhance the throughput of virtual sc...
Artificial Intelligence Ligands Small Molecule Libraries Drug Discovery Proteins Software Molecular Docking Simulation
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Improving drug discovery with a hybrid deep generative model using reinforcement learning trained on a Bayesian docking approximation.

Journal of computer-aided molecular design Aug 8, 2023
Generative approaches to molecular design are an area of intense study in recent years as a method to generate new pharmaceuticals with desired properties. Often though, these types of efforts are constrained by limited experimental activity data, re...
Machine Learning Deep Learning Bayes Theorem Computer Simulation Drug Design Drug Discovery
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Hit Identification Driven by Combining Artificial Intelligence and Computational Chemistry Methods: A PI5P4K-β Case Study.

Journal of chemical information and modeling Aug 7, 2023
Computer-aided drug design (CADD), especially artificial intelligence-driven drug design (AIDD), is increasingly used in drug discovery. In this paper, a novel and efficient workflow for hit identification was developed within the drug discovery pla...
Drug Discovery Computational Chemistry Artificial Intelligence Drug Design
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Recent advances in measurement of metabolic clearance, metabolite profile and reaction phenotyping of low clearance compounds.

Expert opinion on drug discovery Aug 1, 2023
INTRODUCTION: Low metabolic clearance is usually a highly desirable property of drug candidates in order to reduce dose and dosing frequency. However, measurement of low clearance can be challenging in drug discovery. A number of new tools have recen...
Artificial Intelligence Pharmaceutical Preparations Humans Drug Discovery Hepatocytes
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Macrocyclization of linear molecules by deep learning to facilitate macrocyclic drug candidates discovery.

Nature communications Jul 28, 2023
Interest in macrocycles as potential therapeutic agents has increased rapidly. Macrocyclization of bioactive acyclic molecules provides a potential avenue to yield novel chemical scaffolds, which can contribute to the improvement of the biological ac...
Janus Kinase Inhibitors Deep Learning Macrocyclic Compounds Molecular Docking Simulation Drug Discovery
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AndroPred: an artificial intelligence-based model for predicting androgen receptor inhibitors.

Journal of biomolecular structure & dynamics Jul 26, 2023
Androgen receptor (AR), a steroid receptor, plays a pivotal role in the pathogenesis of prostate cancer (PCa). AR controls the transcription of genes that help cells avoid apoptosis and proliferate, thereby contributing to the development of PCa. Und...
Quantitative Structure-Activity Relationship Androgen Receptor Antagonists Prostatic Neoplasms Receptors, Androgen Drug Discovery Models, Molecular Humans Algorithms Artificial Intelligence Male Machine Learning
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A drug molecular classification model based on graph structure generation.

Journal of biomedical informatics Jul 21, 2023
Molecular property prediction based on artificial intelligence technology has significant prospects in speeding up drug discovery and reducing drug discovery costs. Among them, molecular property prediction based on graph neural networks (GNNs) has r...
Neural Networks, Computer Artificial Intelligence Drug Discovery Technology Learning
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