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ML based QSAR Modelling And Translation of Model to Deployable WebApps
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- Published in ACS, Journal of Chemical Information and Modeling . Link : https://pubs.acs.org/doi/10.1021/acs.jcim.4c02372
- Simple to use and no compromise on essential features necessary to make reliable QSAR models.
- From Generating Reliable ML Based QSAR Models to Developing Your Own QSAR WebApp.
For any feedback or queries, contact
kabeermuzammil614@gmail.com
- Available on Windows and Linux
- Software Authorship - Muzammil Kabier
-If You are Facing Issues in Deployment to Streamlit, Try 'requirements.txt' in the Github repo or The Files Deposited Here.
"Information is data in action", and, consequently, having good quality data is essential. The AESTEL package contains two highly configurable applications for data management: A data loader and a reporting application, i.e. DataLoader and AEREA, respectively. The data loader application applies user-defined instructions to validate, process and load data. The reporting application provides a query builder and spreadsheet template designer.
Create lipid-bilayer models of arbitrary geometry.
LATEST VERSION NOW LOCATED AT http://git.durrantlab.com/jdurrant/lipidwrapper
As ever larger and more complex biological systems are modeled in silico, approximating physiological lipid bilayers with simple planar models becomes increasingly unrealistic. When building large-scale models of whole subcellular environments, models of lipid membranes with carefully considered, biologically relevant curvature are essential. LipidWrapper, written by Jacob Durrant in the lab of Rommie E. Amaro, is capable of creating curved membrane models with geometries derived from various possible sources, both experimental and theoretical. We are hopeful that this utility will be a useful tool for the computational-biology community.