AIMC Topic: Drug Evaluation, Preclinical

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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...
Acetyl-CoA Carboxylase Binding Sites Diacylglycerol O-Acyltransferase Non-alcoholic Fatty Liver Disease Enzyme Inhibitors Machine Learning Molecular Docking Simulation Humans Molecular Dynamics Simulation Drug Evaluation, Preclinical 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 Drug Evaluation, Preclinical Cytoskeletal Proteins Bacillus subtilis Bacterial Proteins Humans Anti-Bacterial Agents Computer Simulation Machine Learning
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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 Drug Discovery Drug Evaluation, Preclinical Machine Learning Algorithms
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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 Machine Learning Support Vector Machine Neural Networks, Computer Histone Deacetylase Inhibitors Histone Deacetylase 6 Drug Evaluation, Preclinical Quantitative Structure-Activity Relationship
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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 Proteins Drug Evaluation, Preclinical Machine Learning Molecular Docking Simulation Antineoplastic Agents User-Computer Interface Drug Discovery Humans
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Integrated machine learning-based virtual screening and biological evaluation for identification of potential inhibitors against cathepsin K.

Molecular diversity Apr 25, 2024
Cathepsin K is a type of cysteine proteinase that is primarily expressed in osteoclasts and has a key role in the breakdown of bone matrix protein during bone resorption. Many studies suggest that the deficiency of cathepsin K is concomitant with a s...
Humans Machine Learning Drug Evaluation, Preclinical Cathepsin K Cysteine Proteinase Inhibitors Molecular Docking Simulation Molecular Dynamics Simulation
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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 ...
Binding Sites Deep Learning Drug Interactions Drug Evaluation, Preclinical Drug Delivery Systems
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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...
Induced Pluripotent Stem Cells Isoproterenol Myocytes, Cardiac Animals Deep Learning Cell Differentiation Drug Evaluation, Preclinical Mice
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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...
Databases, Pharmaceutical Humans Neural Networks, Computer Drug Evaluation, Preclinical Molecular Docking Simulation Enzyme Inhibitors Deep Learning Databases, Chemical
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GraphormerDTI: A graph transformer-based approach for drug-target interaction prediction.

Computers in biology and medicine Mar 18, 2024
The application of Artificial Intelligence (AI) to screen drug molecules with potential therapeutic effects has revolutionized the drug discovery process, with significantly lower economic cost and time consumption than the traditional drug discovery...
Drug Discovery Neural Networks, Computer Drug Evaluation, Preclinical Artificial Intelligence
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