Miran Aswad1, Mahmoud Rayan2, Saleh Abu-Lafi3, Mizied Falah1, Jamal Raiyn3, Ziyad Abdallah2, Anwar Rayan4,5. 1. Eliachar Research Laboratory, Western Galilee Hospital, 22100, Nahariya, Israel. 2. Institute of Applied Research-Galilee Society, 20200, Shefa-Amr, Israel. 3. Faculty of Pharmacy, Al-Quds University, P. Box 20002, Abu-Deis, Palestine. 4. Institute of Applied Research-Galilee Society, 20200, Shefa-Amr, Israel. a_rayan@qsm.ac.il. 5. Drug Discovery Informatics Lab, QRC-Qasemi Research Center, Al-Qasemi Academic College, 3010, Baka EL-Garbiah, Israel. a_rayan@qsm.ac.il.
Abstract
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 anti-inflammatory 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 anti-inflammatory 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 anti-inflammatory activity.
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 anti-inflammatory 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 anti-inflammatory 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 anti-inflammatory activity.
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