Artificial intelligence-driven hit identification for neglected tropical diseases

Only ~1% of all drug candidates against Neglected Tropical Diseases (NTDs) have reached clinical trials in the last decades, underscoring the need for new, safer and effective treatments for these debilitating group of diseases. Artificial intelligence (AI) is a cutting-edge area of computational research that allows rapid identification of potentially active compounds with appropriate pharmacokinetic and toxicological properties, shortening the drug discovery process while leading to a higher success rate and reducing costs. Thus, combining drug discovery and AI approaches has the potential to transform drug discovery from a slow, sequential and high-risk process to a fast, integrated model with diminished risk of failure. In this talk, we will present the development and application of AI and computational approaches such as structure-based drug design (SBDD) and ligand-based drug design (LBDD) to accelerate drug discovery for the treatment of NTDs and Emerging Diseases, such as Zika and COVID-19, by the identification of hits suitable for optimization.