AIMC Topic: Drug Evaluation, Preclinical

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Deep learning pipeline for accelerating virtual screening in drug discovery.

Scientific reports Nov 16, 2024
In the race to combat ever-evolving diseases, the drug discovery process often faces the hurdles of high-cost and time-consuming procedures. To tackle these challenges and enhance the efficiency of identifying new therapeutic agents, we introduce Vir...
Ligands Molecular Docking Simulation Drug Discovery Antiviral Agents Drug Evaluation, Preclinical Deep Learning Humans Software
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Screening for Potential Antiviral Compounds from Cyanobacterial Secondary Metabolites Using Machine Learning.

Marine drugs Nov 5, 2024
The secondary metabolites of seawater and freshwater blue-green algae are a rich natural product pool containing diverse compounds with various functions, including antiviral compounds; however, high-efficiency methods to screen such compounds are la...
HIV-1 Biological Products Drug Evaluation, Preclinical Humans Machine Learning Cyanobacteria Molecular Docking Simulation Antiviral Agents
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AlzyFinder: A Machine-Learning-Driven Platform for Ligand-Based Virtual Screening and Network Pharmacology.

Journal of chemical information and modeling Oct 31, 2024
Alzheimer's disease (AD), a prevalent neurodegenerative disorder, presents significant challenges in drug development due to its multifactorial nature and complex pathophysiology. The AlzyFinder Platform, introduced in this study, addresses these cha...
User-Computer Interface Drug Evaluation, Preclinical Ligands Network Pharmacology Alzheimer Disease Humans Machine Learning Drug Discovery
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Machine Learning-Driven Data Valuation for Optimizing High-Throughput Screening Pipelines.

Journal of chemical information and modeling Oct 23, 2024
In the rapidly evolving field of drug discovery, high-throughput screening (HTS) is essential for identifying bioactive compounds. This study introduces a novel application of data valuation, a concept for evaluating the importance of data points bas...
Drug Discovery Drug Evaluation, Preclinical High-Throughput Screening Assays Machine Learning
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Pre-training strategy for antiviral drug screening with low-data graph neural network: A case study in HIV-1 K103N reverse transcriptase.

Journal of computational chemistry Oct 22, 2024
Graph neural networks (GNN) offer an alternative approach to boost the screening effectiveness in drug discovery. However, their efficacy is often hindered by limited datasets. To address this limitation, we introduced a robust GNN training framework...
Machine Learning HIV-1 Humans Molecular Docking Simulation Reverse Transcriptase Inhibitors Neural Networks, Computer Anti-HIV Agents HIV Reverse Transcriptase Molecular Dynamics Simulation Drug Evaluation, Preclinical
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Enhanced drug classification using machine learning with multiplexed cardiac contractility assays.

Pharmacological research Oct 11, 2024
Cardiac screening of newly discovered drugs remains a longstanding challenge for the pharmaceutical industry. While therapeutic efficacy and cardiotoxicity are evaluated through preclinical biochemical and animal testing, 90 % of lead compounds fail ...
Cells, Cultured Drug Evaluation, Preclinical Myocytes, Cardiac Humans Machine Learning Tissue Engineering Myocardial Contraction
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An artificial intelligence accelerated virtual screening platform for drug discovery.

Nature communications Sep 5, 2024
Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding p...
Artificial Intelligence Molecular Docking Simulation Protein Binding Drug Evaluation, Preclinical Drug Discovery Humans Ubiquitin-Protein Ligases Small Molecule Libraries NAV1.7 Voltage-Gated Sodium Channel Ligands Crystallography, X-Ray
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Innovative virtual screening of PD-L1 inhibitors: the synergy of molecular similarity, neural networks and GNINA docking.

Future medicinal chemistry Sep 4, 2024
Immune checkpoint inhibitors targeting PD-L1 are crucial in cancer research for preventing cancer cells from evading the immune system. This study developed a screening model combining ANN, molecular similarity, and GNINA 1.0 docking to target PD-L1...
Molecular Structure Molecular Docking Simulation Humans Immune Checkpoint Inhibitors Drug Evaluation, Preclinical B7-H1 Antigen Neural Networks, Computer
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Multitask Learning on Graph Convolutional Residual Neural Networks for Screening of Multitarget Anticancer Compounds.

Journal of chemical information and modeling Aug 28, 2024
Recently, various modern experimental screening pipelines and assays have been developed to find promising anticancer drug candidates. However, it is time-consuming and almost infeasible to screen an immense number of compounds for anticancer activit...
Drug Screening Assays, Antitumor Neural Networks, Computer Neoplasms Drug Evaluation, Preclinical Machine Learning Antineoplastic Agents Humans
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FitScore: a fast machine learning-based score for 3D virtual screening enrichment.

Journal of computer-aided molecular design Aug 16, 2024
Enhancing virtual screening enrichment has become an urgent problem in computational chemistry, driven by increasingly large databases of commercially available compounds, without a commensurate drop in in vitro screening costs. Docking these large d...
Ligands Humans Binding Sites Proteins Molecular Docking Simulation Drug Evaluation, Preclinical Drug Discovery Protein Binding Machine Learning Software
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