Assessing the ligand-binding affinity of chitinase inhibitors using steered-molecular simulations

Chemical Physics Letters, Journal Year: 2025, Volume and Issue: unknown, P. 141899 - 141899

Published: Jan. 1, 2025

Language: Английский

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Tripeptides inhibit dual targets AChE and BACE-1: a computational study

RSC Advances, Journal Year: 2025, Volume and Issue: 15(16), P. 12866 - 12875

Published: Jan. 1, 2025

Computational identification of tripeptides as promising dual AChE/BACE-1 inhibitors for Alzheimer's therapy.

Language: Английский

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Prediction of the small molecule selectivity index against influenza virus strain A/H1N1 using machine learning methods

Russian Chemical Bulletin, Journal Year: 2025, Volume and Issue: 74(3), P. 851 - 864

Published: March 1, 2025

Language: Английский

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In-silico approaches for identification of compounds inhibiting SARS-CoV-2 3CL protease

PLoS ONE, Journal Year: 2023, Volume and Issue: 18(4), P. e0284301 - e0284301

Published: April 14, 2023

The world has witnessed of many pandemic waves SARS-CoV-2. However, the incidence SARS-CoV-2 infection now declined but novel variant and responsible cases been observed globally. Most population received vaccinations, immune response against COVID-19 is not long-lasting, which may cause new outbreaks. A highly efficient pharmaceutical molecule desperately needed in these circumstances. In present study, a potent natural compound that could inhibit 3CL protease protein was found with computationally intensive search. This research approach based on physics-based principles machine-learning approach. Deep learning design applied to library compounds rank potential candidates. procedure screened 32,484 compounds, top five hits estimated pIC 50 were selected for molecular docking modeling. work identified two hit CMP4 CMP2, exhibited strong interaction using simulation. These demonstrated catalytic residues His41 Cys154 protease. Their calculated binding free energies MMGBSA compared those native inhibitor. Using steered dynamics, dissociation strength complexes sequentially determined. conclusion, comparative performance inhibitors as promising candidate. can be in-vitro experiment validation its inhibitory activity. Additionally, methods used identify sites enzyme target sites.

Language: Английский

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Utilizing Omics Technologies and Machine Learning to Improve Predictive Toxicology

Published: Aug. 26, 2024

The topic of predictive toxicology has been greatly influenced by recent progress in comprehending drug toxicity processes and enhancing medication development. integration omics technologies, such as transcriptomics, proteomics, metabolomics, with traditional toxicological assessments yielded extensive knowledge about the biological pathways implicated drug-induced toxicity. utilization a multi-omics method amplifies ability to identify biomarkers that can detect at an early stage, hence safety profile novel therapeutic medicines. Machine learning silico models, QSAR models multi-task deep algorithms, have become essential tools. They shown great accuracy predicting endpoints helped identification new targets. introduction microphysiological systems PBPK modeling enhanced transfer preclinical discoveries clinical results, providing more precise forecasts human reactions medications. Notwithstanding these progressions, obstacles diversity data complex nature require sophisticated computational techniques for efficient analysis. Continued cooperation established procedures are crucial fully utilize guaranteeing creation safer medicinal agents.

Language: Английский

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Crystallographic Data Collection Using a Multilayer Monochromator on an Undulator Beamline at the Shanghai Synchrotron Radiation Facility

Crystals, Journal Year: 2024, Volume and Issue: 14(2), P. 199 - 199

Published: Feb. 19, 2024

To resolve photons hungry for weak diffraction samples by the crystallographic method, a double-multilayer monochromator (DMM) was employed on an undulator beamline (BL17UM) at Shanghai Synchrotron Radiation Facility (SSRF) to provide focused sub-micron beam with high brightness macromolecular crystallography experiments. High-quality datasets from model protein crystal were collected and processed existing program structure solution refinement. The data quality compared normal silicon evaluate bandwidth of DMM effect these data. This experiment demonstrates that multilayer optics may play valuable role in satisfying demands structure-related research, which requires novel methods.

Language: Английский

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Recent Advances in Omics, Computational Models, and Advanced Screening Methods for Drug Safety and Efficacy

Toxics, Journal Year: 2024, Volume and Issue: 12(11), P. 822 - 822

Published: Nov. 16, 2024

It is imperative to comprehend the mechanisms that underlie drug toxicity in order enhance efficacy and safety of novel therapeutic agents. The capacity identify molecular pathways contribute drug-induced has been significantly enhanced by recent developments omics technologies, such as transcriptomics, proteomics, metabolomics. This enabled early identification potential adverse effects. These insights are further computational tools, including quantitative structure-activity relationship (QSAR) analyses machine learning models, which accurately predict endpoints. Additionally, technologies physiologically based pharmacokinetic (PBPK) modeling micro-physiological systems (MPS) provide more precise preclinical-to-clinical translation, thereby improving assessments. review emphasizes synergy between sophisticated screening silico modeling, data, emphasizing their roles reducing late-stage development failures. Challenges persist integration a variety data types interpretation intricate biological interactions, despite progress made. standardized methodologies predictive toxicology contingent upon ongoing collaboration researchers, clinicians, regulatory bodies. ensures pharmaceuticals effective safer.

Language: Английский

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ROC-guided virtual screening, molecular dynamics simulation, and bioactivity validation assessment Z195914464 as a 3CL Mpro inhibitor

Biophysical Chemistry, Journal Year: 2024, Volume and Issue: 317, P. 107357 - 107357

Published: Nov. 23, 2024

Language: Английский

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