Virtual screening can significantly save experimental time and costs for early drug discovery. Drug multi-classification can speed up virtual screening and quickly predict the most likely class for a drug. In this study, 1019 drug molecules with actual therapeutic effects are collected from multiple databases and documents, and molecular sets are grouped according to therapeutic effect and mechanism of action. Molecular descriptors and molecular fingerprints are obtained through SMILES to quantify molecular structures. After using the Kennard-Stone method to divide the data set, a better combination can be obtained by comparing the combined results of five classification algorithms and a fusion method. Furthermore, for a specific data set, the model with the best performance is used to predict the validation data set. The test set shows that prediction accuracy can reach 0.862 and kappa coefficient can reach 0.808. The highest classification accuracy of the validation set is 0.873. The more reliable molecular set has been found, which could be used to predict potential attributes of unknown drug compounds and even to discover new use for old drugs. We hope this research can provide a reference for virtual screening of multiple classes of drugs at the same time in the future.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9369618 | PMC |
| http://dx.doi.org/10.3390/molecules27154807 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
In silico evaluation of bisphosphonates identifies leading candidates for SARS-CoV-2 RdRp inhibition.
J Mol Graph Model
January 2025
School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA 6027, Australia; Department of Health Sciences, University of York, York, YO10 5DD, UK. Electronic address:
The novel coronavirus disease (COVID-19) pandemic has resulted in 777 million confirmed cases and over 7 million deaths worldwide, with insufficient treatment options. Innumerable efforts are being made around the world for faster identification of therapeutic agents to treat the deadly disease. Post Acute Sequelae of SARS-CoV-2 infection or COVID-19 (PASC), also called Long COVID, is still being understood and lacks treatment options as well.
View Article and Find Full Text PDFDiscovery of anti-Ebola virus multi-target inhibitors from traditional Chinese medicine database using molecular screening, biophysical investigation, and binding free energy calculations.
J Infect Public Health
December 2024
Department of Pharmaceutical Sciences, College of Pharmacy, QU Health, Qatar University, P.O. Box 2713, Doha, Qatar. Electronic address:
Article Synopsis
- Ebola virus (EBOV) is a highly deadly RNA virus that currently lacks effective treatments or vaccines, necessitating the urgent need for new therapeutic solutions.
- In this study, researchers used in silico methods to evaluate natural products from traditional Chinese medicine against four critical EBOV proteins, employing molecular docking to assess their potential effectiveness.
- The findings identified eight promising compounds with strong inhibitory effects on EBOV proteins, indicating their potential as antiviral agents due to their favorable interaction with protein residues and acceptable pharmacokinetic profiles.
Improving Molecular Design with Direct Inverse Analysis of QSAR/QSPR Model.
Mol Inform
January 2025
Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan.
Recent advances in machine learning have significantly impacted molecular design, notably the molecular generation method combining the chemical variational autoencoder (VAE) with Gaussian mixture regression (GMR). In this method, a mathematical model is constructed with X as the latent variable of the molecule and Y as the target properties and activities. Through direct inverse analysis of this model, it is possible to generate molecules with the desired target properties.
View Article and Find Full Text PDFCOX-2 Inhibitor Prediction With KNIME: A Codeless Automated Machine Learning-Based Virtual Screening Workflow.
J Comput Chem
January 2025
Pharmaceutical Chemistry Research Laboratory 1, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India.
Cyclooxygenase-2 (COX-2) is an enzyme that plays a crucial role in inflammation by converting arachidonic acid into prostaglandins. The overexpression of enzyme is associated with conditions such as cancer, arthritis, and Alzheimer's disease (AD), where it contributes to neuroinflammation. In silico virtual screening is pivotal in early-stage drug discovery; however, the absence of coding or machine learning expertise can impede the development of reliable computational models capable of accurately predicting inhibitor compounds based on their chemical structure.
View Article and Find Full Text PDFBest Practice Recommendations for Endometrial Intraepithelial Neoplasia/Atypical Endometrial Hyperplasia in the Military Health System.
Mil Med
January 2025
Division of Gynecologic Oncology, Department of Gynecologic Surgery & Obstetrics, Tripler Army Medical Center, Honolulu, HI 96859, USA.
Endometrial cancer is the most prevalent gynecologic cancer in the United States and has rising incidence and mortality. Endometrial intraepithelial neoplasia or atypical endometrial hyperplasia (EIN-AEH), a precancerous neoplasm, is surgically managed with hysterectomy in patients who have completed childbearing because of risk of progression to cancer. Concurrent endometrial carcinoma (EC) is also present on hysterectomy specimens in up to 50% of cases.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!