Rapid in silico target identification for anti-malarials:

Malaria is a threat to public health caused by parasitic protozoans of the family Plasmodium. Through the development of phenotypic assays and high throughput screening (HTS) thousands of compounds have been identified that inhibit the most serious malaria parasite, Plasmodium falciparum. In order to prioritize these hits compounds for the development of probe or lead molecules it is critical to determine their mode of action, i.e. the biological target protein. For rapid and efficient classification this study applies a Ligand-Based Computer Aided Drug Discovery (LB-CADD) profiling approach: Quantitative Structure Activity Relationships (QSAR) were established that for each compound provide a probability profile to act via inhibition of a range of malaria protein targets. All data for training the QSAR models were obtained from PubChem and curated according to associated validation and counter screens. The QSAR models were tested for profiling performance on an independent data set. This study shows that LB-CADD profiling can predict which protein target(s) is likely to be inhibited by a particular compound.