Objectives The aim was to index natural products for less
expensive preventive or curative anti-inflammatory therapeutic
drugs.
Materials A set of 441 anti-inflammatory drugs representing
the active domain and 2892 natural products
representing the inactive domain was used to construct a
predictive model for bioactivity-indexing purposes.
Method The model for indexing the natural products for
potential anti-inflammatory activity was constructed using
the iterative stochastic elimination algorithm (ISE). ISE is
capable of differentiating between active and inactive antiinflammatory
molecules.
Results By applying the prediction model to a mix set of
(active/inactive) substances, we managed to capture 38%
of the anti-inflammatory drugs in the top 1% of the
screened set of chemicals, yielding enrichment factor of 38.
Ten natural products that scored highly as potential antiinflammatory
drug candidates are disclosed. Searching the
PubMed revealed that only three molecules (Moupinamide,
Capsaicin, and Hypaphorine) out of the ten were tested and
reported as anti-inflammatory. The other seven phytochemicals
await evaluation for their anti-inflammatory
activity in wet lab.
Conclusion The proposed anti-inflammatory model can be
utilized for the virtual screening of large chemical databases
and for indexing natural products for potential antiinflammatory
activity.