Background: Discovery of apicoplast as a drug target offers a new direction in the development
of novel anti-malarial compounds, especially against the drug-resistant strains. Drugs such as
azithromycin were reported to block the apicoplast development that leads to unusual phenotypes affecting
the parasite. This phenomenon suggests that identification of new apicoplast inhibitors will aid in the
anti-malarial drug discovery. Therefore, in this study, we developed a computational model to predict
apicoplast inhibitors by applying state-of-the-art machine learning techniques.
Methods: We have used two high-throughput chemical screening data (AID-504850, AID-504848) from
PubChem BioAssay database and applied machine learning techniques. The performance of the models
were assessed on various types of binary fingerprints.
Results: In this study, we developed a robust computational algorithm for the prediction of apicoplast inhibition.
We observed 73.7% sensitivity and 84% specificity along with 81.4% accuracy rate only on 41
PubChem fingerprints on 48 hrs dataset. Similarly, an accuracy rate of 75.8% was observed for 96 hrs
dataset. Additionally, we observed that our model has ~70% positive prediction rate on the independent
dataset obtained from ChEMBL-NTD database. Furthermore, the fingerprint analysis suggested that
compounds with at least one heteroatom containing hexagonal ring would most likely belong to the antimalarial
category as compared to simple aliphatic compounds. We also observed that aromatic compounds
with oxygen and chlorine atoms were preferred in inhibitors class as compared to sulphur. Additionally,
the compounds with average molecular weight >380Da and XlogP>4 were most likely to belong
to the inhibitor category.
Conclusion: This study highlighted the significance of simple interpretable molecular properties along
with some preferred substructure in designing the novel anti-malarial compounds. In addition to that, robustness
and accuracy of models developed in the present work could be utilized to screen a large chemical
library. Based on this study, we developed freely available software at http://deepaklab. com/capi.
This study would provide the best alternative for searching the novel apicoplast inhibitors against Plasmodium.