The work describes the development of novel software supporting synchronous distant collaboration between scientists involved in drug discovery and development projects. The program allows to visualize and share data as well as to interact in real time using standard intranets and Internet resources. Direct visualization of 2D and 3D molecular structures is supported and original tools for facilitating remote discussion have been integrated. The software is multiplatform (MS-Windows, SGI-IRIX, Linux), allowing for a seamless integration of heterogeneous working environments. The project aims to support collaboration both within and between academic and industrial institutions. Since confidentiality is very important in some scenarios, special attention has been paid to security aspects. The article presents the research carried out to gather the requirements of collaborative software in the field of drug discovery and development and describes the features of the first fully functional prototype obtained. Real-world testing activities carried out on this prototype in order to guarantee its adequacy in diverse environments are also described and discussed.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1023/a:1023884712617 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Vernonolide A, a Sesquiterpene Lactone with a Unique Carbon Skeleton from .
Org Lett
January 2025
Department of Pharmaceutical Sciences, Daniel K. Inouye College of Pharmacy, University of Hawai'i at Hilo, Hilo, Hawai'i 96720, United States.
A novel sesquiterpene lactone derivative, vernonolide A (), featuring an unprecedented carbon skeleton, along with its plausible biosynthetic precursor, vercinolide I (), and eight known sesquiterpene lactones (-) were isolated and characterized from the whole plants of (L.). The structures of and were elucidated using nuclear magnetic resonance spectroscopic analysis and calculated and experimental electronic circular dichroism spectra.
View Article and Find Full Text PDFImproving 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 PDFIntegrated Network Pharmacology and Metabolomics to Investigate the Effects and Possible Mechanisms of Ginsenoside Rg Glycine Ester Derivative Against Hypoxia.
Biomed Chromatogr
February 2025
School of Pharmaceutical Sciences, Jilin University, Changchun, People's Republic of China.
Previous studies have suggested that ginsenoside Rg glycine ester derivative (RG) exhibits therapeutic potential in mitigating hypoxia. This study aimed to elucidate the potential mechanism of RG in hypoxia injury through a combined approach of metabolomics and network pharmacology. Initially, a CoCl-induced cell hypoxia model was established, and the therapeutic impact of RG on biochemical indices was evaluated.
View Article and Find Full Text PDFRoadmap to discovery and early development of an mRNA loaded LNP formulation for liver therapeutic genome editing.
Expert Opin Drug Deliv
January 2025
Advanced Drug Delivery, Pharmaceutical Sciences, R&D, AstraZeneca, Macclesfield, UK.
Introduction: mRNA therapeutics were a niche area in drug development before COVIDvaccines. Now they are used in vaccine development, for non-viral therapeuticgenome editing, chimericantigen receptor T (CAR T) celltherapies and protein replacement. mRNAis large, charged, and easily degraded by nucleases.
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 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!