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A large number of natural products, especially those used in ethnomedicine of malaria, have shown varying in-vitro antiplasmodial activities. Cheminformatics involves the organization, integration, curation, standardization, simulation, mining and transformation of pharmacology data (compounds and bioactivity) into knowledge that can drive rational and viable drug development decisions. This chapter will review the application of two cheminformatics techniques (including molecular scaffold analysis and bioactivity predictive modeling via Machine learning) to natural products with in-vitro and in-vivo antiplasmodial activities in order to facilitate their development into antimalarial drug candidates and design of new potential antimalarial compounds.

Cheminformatics techniques in antimalarial drug discovery and development from natural products 2: Molecular scaffold and machine learning approaches