• Abhinav Anand Department of Pharmacology, School of Pharmaceutical Sciences, Lovely Professional University, Punjab, India.
  • Neha Sharma Department of Pharmacology, School of Pharmaceutical Sciences, Lovely Professional University, Punjab, India.
  • Navneet Khurana Department of Pharmacology, School of Pharmaceutical Sciences, Lovely Professional University, Punjab, India.



Nil, Prediction of activity spectra of substances, Phytoconstituents, Prediction


Objective: To predict the biological activity of certain phytoconstituents for their anti-AD effects.

Methods: Several phytoconstituents were selected on the basis of reported literature. The anti-AD activities of selected phytoconstituents were predicted by employing canonical simplified molecular-input line-entry system obtained from PubChem using PASS online.

Results: Several phytoconstituents were predicted to have effects better than marketed drugs under some or the other out of the chosen six areas of pharmacological intervention. On the other hand, several new avenues were predicted in which the in vitro and in vivo evaluation of the phytoconstituents can be made on the basis of PASS predicted activities.

Conclusion: PASS is an important tool for virtually screening the compounds of interest for the biological activities of interest. This helps the researchers to streamline the research. However, PASS has its own share of limitations amidst a multitude of merits.


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Author Biography

Abhinav Anand, Department of Pharmacology, School of Pharmaceutical Sciences, Lovely Professional University, Punjab, India.

School of Pharmaceutical Sciences, Asst. Prof


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How to Cite

Anand, A., N. Sharma, and N. Khurana. “PREDICTION OF ACTIVITY SPECTRA OF SUBSTANCES ASSISTED PREDICTION OF BIOLOGICAL ACTIVITY SPECTRA OF POTENTIAL ANTI-ALZHEIMER’S PHYTOCONSTITUENTS”. Asian Journal of Pharmaceutical and Clinical Research, vol. 10, no. 16, Sept. 2017, pp. 13-21, doi:10.22159/ajpcr.2017.v10s4.21330.



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