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BackgroundStatistical potentials, also named knowledge-based potentials, are scoring functions derived from empirical data that can be used to evaluate the quality of protein folds and protein-protein interaction (PPI) structures. In previous works we deco ...
Structure-based antibody and antigen design has advanced greatly in recent years, due not only to the increasing availability of experimentally determined structures but also to improved computational methods for both prediction and design. Constant improv ...
Point clouds are among popular visual representations for immersive media. However, the vast amount of information generated during their acquisition requires effective compression for practical applications. Although relevant activities from standardizati ...
Predicting the effects of mutations on protein stability is a key problem in fundamental and applied biology, still unsolved even for the relatively simple case of small, soluble, globular, monomeric, two-state-folder proteins. Many articles discuss the li ...
The work presented in this thesis combines supervised and unsupervised machine learning to examine structure-property relationships in databases of materials. While either supervised learning or unsupervised learning alone can be a powerful tool for assess ...
Proteins are fundamental components in biological systems and crucial for a variety of biological functions. Over the last decades, significant progress in structural biology has facilitated the study of proteins and their interactions with molecular partn ...
Direct-coupling analysis (DCA) for studying the coevolution of residues in proteins has been widely used to predict the three-dimensional structure of a protein from its sequence. We present RADI/raDIMod, a variation of the original DCA algorithm that grou ...
Human trajectory forecasting in crowds, at its core, is a sequence prediction problem with specific challenges of capturing inter-sequence dependencies (social interactions) and consequently predicting socially-compliant multimodal distributions. In recent ...
Machine learning (ML) algorithms have undergone an explosive development impacting every aspect of computational chemistry. To obtain reliable predictions, one needs to maintain a proper balance between the black-box nature of ML frameworks and the physics ...
In this contribution, we propose an algorithm for replacing non-linear process simulation integrated in multi-level optimization of an energy system superstructure with surrogate models. With our approach, we demonstrate that surrogate models are a valid t ...