ML based QSAR Modelling And Translation of Model to Deployable WebApps
- This Software was made with an intention to make QSAR/QSPR development more efficient and reproducible.
- 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...
Predicting Organic Reactions using Neural Networks.
The intend is to solve the forward-reaction prediction problem, where the reactants are known and the interest is in generating the reaction products using Deep learning. This Graphical User Interface takes simplified molecular-input line-entry system (SMILES) as an input and generates the product SMILE & molecule.
Beam search is used in Version 2, to generate top 5 predictions.
Maximum input length for the model is 15 (excluding spaces).