AIMC Topic: Drug Evaluation, Preclinical

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Discovery of novel ULK1 inhibitors through machine learning-guided virtual screening and biological evaluation.

Future medicinal chemistry Aug 15, 2024
Build a virtual screening model for ULK1 inhibitors based on artificial intelligence. Build machine learning and deep learning classification models and combine molecular docking and biological evaluation to screen ULK1 inhibitors from 13 million co...
Molecular Dynamics Simulation Drug Evaluation, Preclinical Molecular Docking Simulation Humans Drug Discovery Machine Learning Molecular Structure Protein Kinase Inhibitors Intracellular Signaling Peptides and Proteins Autophagy-Related Protein-1 Homolog
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E-pharmacophore and deep learning based high throughput virtual screening for identification of CDPK1 inhibitors of Cryptosporidium parvum.

Computational biology and chemistry Aug 13, 2024
Cryptosporidiosis, a prevalent gastrointestinal illness worldwide, is caused by the protozoan parasite Cryptosporidium parvum. Calcium-dependent protein kinase 1 (CpCDPK1), crucial for the parasite's life cycle, serves as a promising drug target due ...
Deep Learning Molecular Structure Drug Evaluation, Preclinical Protein Kinase Inhibitors Molecular Docking Simulation Protein Kinases Cryptosporidium parvum High-Throughput Screening Assays Pharmacophore Antiprotozoal Agents
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Combined structure-based virtual screening and machine learning approach for the identification of potential dual inhibitors of ACC and DGAT2.

International journal of biological macromolecules Jul 31, 2024
Acetyl-coenzyme A carboxylase (ACC) and diacylglycerol acyltransferase 2 (DGAT2) are recognized as potential therapeutic targets for nonalcoholic fatty liver disease (NAFLD). Inhibitors targeting ACC and DGAT2 have exhibited the capacity to reduce he...
Diacylglycerol O-Acyltransferase Acetyl-CoA Carboxylase Non-alcoholic Fatty Liver Disease Enzyme Inhibitors Binding Sites Molecular Dynamics Simulation Drug Evaluation, Preclinical Humans Machine Learning Molecular Docking Simulation Protein Binding
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In silico method and bioactivity evaluation to discover novel antimicrobial agents targeting FtsZ protein: Machine learning, virtual screening and antibacterial mechanism study.

Naunyn-Schmiedeberg's archives of pharmacology Jul 23, 2024
This research paper utilizes a fused-in-silico approach alongside bioactivity evaluation to identify active FtsZ inhibitors for drug discovery. Initially, ROC-guided machine learning was employed to obtain almost 13182 compounds from three libraries....
Drug Discovery Microbial Sensitivity Tests Staphylococcus aureus Animals Molecular Docking Simulation Cytoskeletal Proteins Bacillus subtilis Bacterial Proteins Drug Evaluation, Preclinical Humans Machine Learning Anti-Bacterial Agents Computer Simulation
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Machine learning in preclinical drug discovery.

Nature chemical biology Jul 19, 2024
Drug-discovery and drug-development endeavors are laborious, costly and time consuming. These programs can take upward of 12 years and cost US $2.5 billion, with a failure rate of more than 90%. Machine learning (ML) presents an opportunity to improv...
Humans Machine Learning Drug Evaluation, Preclinical Algorithms Drug Discovery
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HT_PREDICT: a machine learning-based computational open-source tool for screening HDAC6 inhibitors.

SAR and QSAR in environmental research Jul 15, 2024
Histone deacetylase 6 (HDAC6) is a promising drug target for the treatment of human diseases such as cancer, neurodegenerative diseases (in particular, Alzheimer's disease), and multiple sclerosis. Considerable attention is paid to the development of...
Humans Histone Deacetylase Inhibitors Histone Deacetylase 6 Support Vector Machine Neural Networks, Computer Drug Evaluation, Preclinical Quantitative Structure-Activity Relationship Machine Learning
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Docking Score ML: Target-Specific Machine Learning Models Improving Docking-Based Virtual Screening in 155 Targets.

Journal of chemical information and modeling Jul 3, 2024
In drug discovery, molecular docking methods face challenges in accurately predicting energy. Scoring functions used in molecular docking often fail to simulate complex protein-ligand interactions fully and accurately leading to biases and inaccuraci...
Ligands Humans Molecular Docking Simulation Machine Learning Drug Discovery User-Computer Interface Antineoplastic Agents Proteins Drug Evaluation, Preclinical
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Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.

BMC bioinformatics Apr 20, 2024
BACKGROUND: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online ...
Drug Delivery Systems Deep Learning Drug Interactions Drug Evaluation, Preclinical Binding Sites
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Detection of biomagnetic signals from induced pluripotent stem cell-derived cardiomyocytes using deep learning with simulation data.

Scientific reports Mar 27, 2024
The detection of spontaneous magnetic signals can be used for the non-invasive electrophysiological evaluation of induced pluripotent stem cell-derived cardiomyocytes (iPS-CMs). We report that deep learning with a dataset that combines magnetic signa...
Mice Cell Differentiation Drug Evaluation, Preclinical Animals Isoproterenol Deep Learning Myocytes, Cardiac Induced Pluripotent Stem Cells
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Deep Learning-Based construction of a Drug-Like compound database and its application in virtual screening of HsDHODH inhibitors.

Methods (San Diego, Calif.) Mar 20, 2024
The process of virtual screening relies heavily on the databases, but it is disadvantageous to conduct virtual screening based on commercial databases with patent-protected compounds, high compound toxicity and side effects. Therefore, this paper uti...
Enzyme Inhibitors Humans Databases, Chemical Deep Learning Neural Networks, Computer Databases, Pharmaceutical Drug Evaluation, Preclinical Molecular Docking Simulation
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