From Theory to Practice: Integrating Computational Approaches in Disease Understanding and Drug Discovery for Tropical Diseases

Medicinal chemistry has played an essential role in treating infectious tropical diseases such as malaria, dengue, and chikungunya, which makes drug design and discovery a crucial process. In this regard, computer-aided drug design is considered a successful approach for identifying drugs. A variety of methodologies, such as pharmacophore mapping, molecular docking, and virtual screening strategies employing drug-likeness studies and ADMET (absorption, distribution, metabolism, excretion, and toxicity) characteristics, molecular dynamics simulations, and free energy calculations, are used to search for a suitable agent against the respective diseases. Recently, with the development of deep learning approaches, researchers are increasingly employing Artificial Intelligence techniques in areas such as protein folding, molecular modelling, target protein structure prediction, bioactivity prediction, toxicity prediction, physiochemical property prediction, and so forth.

This Research Topic aims to investigate all aspects of the rapidly advancing field of AI and computer-aided drug design, ranging from ligand-based studies to structure-based studies for discovering drugs against tropical diseases. In addition, the scope of this special issue also incorporates all aspects of molecular modelling and computational chemistry relating to the design of drugs against tropical diseases, such as pharmacophore modelling, molecular docking, QSAR, and molecular dynamics simulations.

Therefore, this topic welcomes submissions relating to the following subthemes:

- Ligand-based studies: Exploration of pharmacophore modeling, quantitative structure-activity relationship (QSAR) analysis, and virtual screening strategies using drug-likeness studies and ADMET characteristics for identifying potential drug candidates against tropical diseases such as malaria, dengue fever, Zika virus, chikungunya, and leishmaniasis.

- Structure-based studies: Utilization of molecular docking, molecular dynamics simulations, and free energy calculations to investigate the interaction between drug molecules and target proteins/enzymes involved in tropical diseases like schistosomiasis, trypanosomiasis (African sleeping sickness), filariasis, tuberculosis, and leprosy.

- Artificial intelligence (AI) techniques: Novel applications of AI in protein structure prediction, bioactivity prediction, toxicity prediction, physicochemical property prediction, and other relevant areas to enhance drug discovery efforts for tropical diseases, including yellow fever, Japanese encephalitis, Buruli ulcer, and Chagas disease.

- Comparative computational studies: Comparative analyses of experimental findings and computational predictions of biological activity for compounds targeting tropical diseases, providing valuable insights and validation of computational approaches for diseases like Chagas disease, schistosomiasis, malaria, leishmaniasis, and dengue fever.

- Breakthroughs in AI for drug discovery: Contributions focusing on the use of AI in clinical trial design and monitoring, prediction of drug-drug interactions, identification of drug repurposing opportunities, and other emerging areas that have the potential to revolutionize the field of tropical disease drug discovery in diseases such as Ebola virus, Chagas disease, schistosomiasis, leishmaniasis, and African trypanosomiasis.