Papers, Manuscripts, and More
Papers
This page serves as an introduction to my published and peer reviewed academic work in the fields of computational drug design and neuroscience. Within these areas, I have studied the advantages and disadvantages of deep learning techniques to either develop novel approaches to drug discovery and or to elucidate process in the brain. Please take advantage of the links provided to gain access to these publications and explore the cutting-edge research within these fields.
Illuminating Chemical Space
The paper "Illuminating Elite Patches of Chemical Space" by Jonas Verhellen and Jeriek van den Abeele describes an approach to generate a diverse set of molecules with desirable properties in computational drug design. The authors use a quality-diversity algorithm to explore chemical space and identify high-quality and diverse molecules.
Molecular Pareto Optimisation
The paper "Graph-Based Molecular Pareto Optimisation" by Jonas Verhellen presents an approach to optimize multiple molecular properties while ensuring chemical diversity in drug design. The paper demonstrates the potential of graph-based approaches in optimizing complex molecular properties while incorporating chemical diversity.
Isometric Representations in Neural Networks
The paper "Isometric Representations in Neural Networks Improve Robustness" by Kosio Beshkov, Jonas Verhellen, and Mikkel Elle Lepperød proposes a method for training neural networks with isometric constraints. The paper presents a theoretical analysis of the method and demonstrates its effectiveness on image classification tasks.