Evaluation of algorithms and tools for the design of drug candidates: a case study on H5N1 and WNV viruses

• Grant number: 25/02517-8
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• Start date: May 01, 2025
• End date: April 30, 2029
• Field of knowledge: Biological Sciences - Biophysics - Molecular Biophysics
• Principal Investigator: Kathia Maria Honorio
• Grantee: Camila Fonseca Amorim da Silva
• Host Institution: Escola de Artes, Ciências e Humanidades (EACH). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 23/07746-0 - Drug discovery against human viral infectious diseases, AP.TEM
• Associated scholarship(s): 25/19549-0 - Computational drug discovery on canonical and alternative sites of influenza polymerases, BE.EP.DD

Abstract

Given the advancements in computational approaches and their role in healthcare - notably during the COVID-19 pandemic and in the interdisciplinary work of collaborative groups - a comparative analysis of computational (in silico) techniques and improvements in their respective implementations is timely, supporting the search for more effective and safer treatments for viral infectious diseases. However, when facing the increasing number of possibilities offered by computational methods, there is a challenge in identifying the most suitable methodologies, tools, and parameters for the workflow of projects of this nature - a crucial aspect for research outcomes and their implications for both the scientific community and society. In light of these gaps, this project aims to compare algorithms implemented in available tools for different stages of drug candidate design and to modify such algorithms based on case studies, including: the avian influenza subtype A(H5N1) virus, which has caused fatalities in more than half of recorded human cases, and the West Nile virus (WNV), which raises concerns due to its neurological sequelae. Initially, this project will assess the quality of available H5N1 and WNV protein sequence data, followed by a comparison of approaches for modeling viral targets. Machine learning techniques will be implemented for large-scale compound screening and protein-ligand affinity calculations. Additionally, quantitative structure-activity relationship (QSAR) models will be constructed based on the available dataset (samples and descriptors) related to drug candidates, alongside molecular dynamics simulations and validation of results through biological assays. Thus, this project aims to establish an interdisciplinary protocol integrating knowledge from computer science and medicinal chemistry to standardize procedures in related studies and enhance their future reproducibility.