Q-Lead is a tool for predicting biological activity of untested or novel phytomolecules/derivatives/analogs to provide insight into relevant 2D & 3D chemical descriptors responsible for biological activity/cytotoxicity. For this, Q-Lead uses various in-house developed Quantitative Structure-Activity Relationship (QSAR) models developed through multiple linear regression method by using the training data set (Supervised learning approach under Artificial Intelligence/Machine Learning method) of reported in-vitro/in-vivo experimental bioactivity data of active compounds including natural/phytomolecules & its semi-synthetic derivatives or synthetic analogs. Q-Lead tool is a repository of in-house developed QSAR models for different chemical series & biological activities. These in-built QSAR models quantitatively correlates chemical descriptors (2D and 3D structural properties) with its in-vitro/in-vivo experimental biological activities (in -log form of bioactivity, as p-value e.g., pKi50, pKd50, pIC50, pEC50, pGI50 etc.) for a set of similar compounds. Q-Lead QSAR models are developed by using linear statistical methods such as Multiple Linear Regression (MLR) and Partial Least Square (PLS). Q-Lead tool can be used for potential lead identification and further lead optimization (virtual screening). Virtual screening can save the time, resources, wet lab economy and also save the life of small animals e.g., mice, rat, etc. or reduce the testing/sacrifice.

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