A novel prediction approach for antimalarial activities of Trimethoprim, Pyrimethamine, and Cycloguanil analogues using extremely randomized trees

作者：Nattee Cholwich^*; Khamsemanan Nirattaya^*; Lawtrakul Luckhana^*; Toochinda Pisanu; Hannongbua Supa

来源：Journal of Molecular Graphics and Modelling, 2017, 71: 13-27.

DOI：10.1016/j.jmgm.2016.09.010

• Extremely randomized trees
• Decision trees
• Structure-activity relationship
• Molecular modeling
• Antimalarials
• Dihydrofolate reductase
• Trimethoprim
• Pyrimethamine
• Cycloguanil
• Hydrophobic feature

Malaria is still one of the most serious diseases in tropical regions. This is due in part to the high resistance against available drugs for the inhibition of parasites, Plasmodium, the cause of the disease. New potent compounds with high clinical utility are urgently needed. In this work, we created a novel model using a regression tree to study structure-activity relationships and predict the inhibition constant, K-i; of three different antimalarial analogues (Trimethoprim, Pyrimethamine, and Cycloguanil) based on their molecular descriptors. To the best of our knowledge, this work is the first attempt to study the structure-activity relationships of all three analogues combined. The most relevant descriptors and appropriate parameters of the regression tree are harvested using extremely randomized trees. These descriptors are water accessible surface area, Log of the aqueous solubility, total hydrophobic van der Waals surface area, and molecular refractivity. Out of all possible combinations of these selected parameters and descriptors, the tree with the strongest coefficient of determination is selected to be our prediction model. Predicted K-i, values from the proposed model show a strong coefficient of determination, R-2 = 0.996, to experimental K-i values. From the structure of the regression tree, compounds with high accessible surface area of all hydrophobic atoms (ASA_H) and low aqueous solubility of inhibitors (Log S) generally possess low K-i; values. Our prediction model can also be utilized as a screening test for new antimalarial drug compounds which may reduce the time and expenses for new drug development. New compounds with high predicted K-i should be excluded from further drug development. It is also our inference that a threshold of ASA_H greater than 575.80 and Log S less than or equal to -4.36 is a sufficient condition for a new compound to possess a low K-i.

• 出版日期2017-1