Diseases caused by parasites have an overwhelming impact on public health throughout the world, particularly in the tropics and subtropics. Malaria and leishmaniasis are two such widely known neglected parasitic diseases. The current global situation indicates more than one million deaths from these two diseases every year despite several efforts by WHO to combat them. Vectors for carrying and transmitting these parasites are arthropods. Use of insect repellents is a vital countermeasure in reducing these arthropod-related diseases. However, despite access to many available drugs for treatment of these diseases, their growing resistance poses serious concerns and necessitates development of novel countermeasures. The present chapter discusses how the in silico methodologies can be utilized to develop pharmacophore models to identify novel antimalarials, antileishmanial, and insect repellents. The models presented in this chapter not only provided important molecular insights to better understand the “interaction pharmacophores” but also guided generation of templates for virtual screening of compound databases to identify novel bioactive agents. The pharmacophore models presented here demonstrated a new computational approach for organizing molecular characteristics that were both statistically and mechanistically significant for potent activity and useful for identification of novel analogues as well.
Keywords: In Silico pharmacophore models, CATALYST methodology, parasites, malaria, leishmaniasis, arthropods, insect repellents, virtual screening, compound database, quantum chemical (QM) calculations, stereo-electronic properties, molecular electrostatic potentials (MEPs), drug design, drug discovery, novel compounds.