About the tool
You need to enter the canonical SMILES notation of a compound. The tool uses this to compute predictions.
The tool predicts the likely biological activity category of a compound (antiviral, anti-inflammatory, antioxidant, antimicrobial, or anticancer) along with its ADME (Absorption, Distribution, Metabolism, Excretion) properties.
The tool is powered by machine learning models trained on verified experimental datasets. While predictions are reliable for in silico screening, they should be validated through laboratory experiments.
Yes. If you have a new or untested compound, you can input its SMILES structure to obtain preliminary biological and pharmacokinetic insights.
Properties such as molecular weight, hydrogen bond donors/acceptors, LogP (iLOGP, XLogP3, WLogP, etc.), solubility, and bioavailability score are included.
Absolutely. It is designed to support both academic researchers and pharmaceutical or nutraceutical industries in the early stages of compound screening.
No, all inputs are processed in real-time, and no user-submitted SMILES data is stored on the server.
Anyone interested in phytochemical research, including students, educators, pharmacologists, and drug discovery professionals.
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Developed by Naveena Sakthivel
Department of Biotechnology,
Vivekanandha College of Arts and Sciences for Women (Autonomous)
Tiruchengode, Namakkal, Tamil Nadu, India.
Under the Supervision of Dr. Gnanendra Shanmugam
Department of Biotechnology,
Vivekanandha College of Arts and Sciences for Women (Autonomous)
Tiruchengode, Namakkal, Tamil Nadu, India.