Resumo:

Neglected tropical diseases (NTDs) impose a substantial burden in low- and middle-income countries while attracting limited investment and innovation. Digital transformation has enabled in silico approaches that accelerate discovery by integrating structural biology, cheminformatics, and machine learning. This chapter analyzes how computational pipelines support target identification, virtual screening, molecular dynamics, pharmacophore and QSAR modeling, as well as ADMET prediction, with case studies in Chagas disease, leishmaniasis, schistosomiasis, and arboviruses. We discuss the role of open databases and collaborative platforms, including TDR Targets, ChEMBL, DrugBank, and open-source initiatives, and outline future directions where AI and multi-omics integration can reduce time, cost, and risk in public-health-oriented drug discovery.