Researchers at Kent have established a computational protocol that could accelerate the development of more effective treatments for life-threatening parasitic infections such as Chagas disease, by enabling scientists to accurately identify reactions that can result in successful drug candidates without the need for trial and error.
Approximately 8 million people worldwide, mostly in Latin America, are estimated to be infected with Trypanosoma cruzi, the parasite that causes Chagas disease, with around 100 million people considered at risk of infection. While the disease can be cured in its early, acute phase, untreated infections can become chronic, leading to severe complications affecting the heart, digestive system, and nervous system.
Despite the potentially fatal consequences of infection, parasitic diseases often affect lower-income and underserved communities, reducing the commercial incentive for pharmaceutical companies to invest in new treatments. Therefore, improving the efficiency and cost-effectiveness of early-stage drug development is essential to making new therapies more achievable.
This is where computational chemistry comes in. By modelling and simulating how potential drug compounds behave before they are tested in the laboratory, researchers are able to reduce costs and speed up discoveries.
In this recent study, Kent researchers focused on naphthoquinones, a class of compounds with known activity against parasitic diseases, particularly Chagas disease. The team investigated approaches for selectively modifying these compounds using a ruthenium-based catalyst, which allows scientists to systematically ‘edit’ the compounds and fine-tune properties such as effectiveness, stability, and selectivity, all of which are important for successful drug candidates.
To predict which modifications are most likely to succeed, the researchers benchmarked nine widely used quantum-chemical approaches against a highly accurate reference method. They identified a protocol that closely reproduces high-level computational results, while also demonstrating that a lower-cost method can be used for much faster screening without losing mechanistic insights.
With sufficient accuracy to support more efficient and targeted design, scientists can model these chemical modifications while reducing expensive trial-and-error in the laboratory, prioritising the most promising compounds earlier, and making the drug discovery process significantly faster and more affordable.
Lead author Dr Felipe Fantuzzi, Lecturer in Chemistry in the School of Natural Sciences, explains: ‘For diseases such as Chagas disease and other neglected tropical parasitic diseases, where commercial incentives for drug development are often weaker, methods that reduce trial-and-error and help prioritise the most promising compounds are especially valuable. They do not replace experiments, but they can help focus experimental effort where it is most likely to be productive.’
As drug discovery moves towards faster and more predictive strategies, artificial intelligence is becoming more widely used, and as Dr Fantuzzi explains, the approach used in this study acts as a natural partner to it.
‘Physics-based computational chemistry remains essential when the goal is to understand, in chemically interpretable terms, how a catalytic reaction actually works. AI is playing an increasingly important role in identifying patterns, prioritising candidates, and exploring chemical space more efficiently, but it is most effective when combined with robust mechanistic modelling of the kind used here.’
The research was conducted as part of the NUBIAN Project, an international collaboration between the UK, Brazil, and Sierra Leone supported by the Royal Society. The project focuses on tackling neglected tropical diseases through interdisciplinary and international research, with the aim of improving treatment options for some of the world’s most vulnerable communities.
The article (University of Kent: Esther R. S. Paz, Cauê P. Souza and Felipe Fantuzzi), which appeared on the front cover of ChemistryOpen, is available at https://doi.org/10.1002/open.202500465.
This front cover recognition highlights the impact of theoretical and computational chemistry in addressing global health challenges and underscores the contribution of the Supramolecular, Interfacial and Synthetic Chemistry (SISC) group at the University of Kent.