Detecting remote homologs, proteins that share evolutionary ancestry despite low sequence similarity, remains a central challenge in computational biology. The Structural Classification of Proteins extended (SCOPe) database organizes protein domains into superfamilies based on structural and functional evidence of common origin, making it a widely used benchmark for remote homology detection. In this study, we investigate the effectiveness of protein language model (PLM) embeddings for predicting SCOPe superfamilies directly from sequence. We introduce DOMCLASS, a deep learning framework that combines supervised contrastive learning with a distance-weighted K-NN classifier to learn and exploit an embedding space aligned with SCOPe superfamily annotations. Our empirical results show that general-purpose PLM embeddings already outperform sequence similarity- and profile-based methods for remote homology detection, and that contrastive learning can further improve performance, even in challenging low sequence identity settings.

Emerging infectious diseases such as COVID-19, caused by the SARS-CoV-2 virus, require systematic strategies to assist in the discovery of effective treatments. Drug repositioning, the process of finding new therapeutic indications for commercialized drugs, is a promising alternative to the development of new drugs, with lower costs and shorter development times. In this paper, we propose a recommendation system called geometric confidence non-negative matrix factorization (GcNMF) to assist in the repositioning of 126 broad spectrum antiviral drugs for 80 viruses, including SARS-CoV-2. GcNMF models the non-Euclidean structure of the space using graphs, and produces a ranked list of drugs for each virus. Our experiments reveal that GcNMF significanlty outperforms other matrix decomposition methods at predicting missing drug-virus associations. Our analysis suggests that GcNMF could assist pharmacological experts in the search for effective drugs against viral diseases.

The current drug development pipelines are characterised by long processes with high attrition rates and elevated costs. More than 80% of new compounds fail in the later stages of testing due to severe side-effects caused by unknown biomolecular targets of the compounds. In this work, we present a measure that can predict shared targets for drugs in DrugBank through large scale analysis of the biomedical literature. We show that using MeSH ontology terms can accurately describe the drugs and that appropriate use of the MeSH ontological structure can determine pairwise drug similarity.

Chagas disease is a parasitic disease, endemic in South America. As of today, there is no effective treatment in its chronic stage. We have recently identified 134 FDA approved drugs with potential antitrypanosomal activity. In this paper, we propose a novel method for selecting combinations of drugs (drug cocktails), to provide a more effective treatment against Chagas disease. We define three measures to evaluate the predicted performance of a cocktail, establishing in this way a mathematical foundation for its analysis. This allows us to model the drug cocktail selection as a multi-objective optimisation problem, that we show can be solved efficiently with state-of-the-art evolutionary algorithms. Our analysis retrieves 57 drug cocktails containing between 2 and 6 drugs. We discuss the improvement of the cocktail selection given by our method, and the application of this approach to the identification of cocktails against other parasitic diseases.

The amount and quality of available data on different organisms varies greatly. While model organisms benefit from extensive experimental studies, there is often a lack of detailed experimental data for more specific organisms. Additionally, even among model organisms there are noticeable differences in the amount and type of data available, due to the different suitability of experiments in different organisms. The combination of interactomes for closely related species, represents a viable tool to increase the amount of protein-protein interaction data for a given organism. The Human-Mouse case of study is particularly relevant, as many experiments cannot be carried out on humans. This paper describes a general method to construct a combined interactome from different organisms. The construction is achieved through the integration of data from different sources and formats, including gene-protein relations, protein homology classes and protein-protein interactions. We show that the Human-Mouse combined interactome increase the mouse gene coverage by over 150% and the interaction coverage by over 430%. We also provide a novel mathematical formalisation for the interactome combination.

The growing productivity gap between investment in drug research and development (R&D) and the number of new medicines approved by the US Food and Drug Administration (FDA) in the past decade is concerning. This productivity problem raises the need for innovative approaches for drug-target prediction and a deeper understanding of the interplay between drugs and their target proteins. Chemogenomics is the interdisciplinary field which aims to predict gene/protein/ligand relationships. The predictions are based on the assumption that chemically similar compounds should share common targets. Here, we exploit our understanding of the network-based representation of the protein-protein interaction (PPI network) to introduce a distance between drug-targets and could verify whether it correlates with their chemical similarity. We build a fully connected graph composed of US Food and Drug Administration (FDA) - approved drugs using the Tanimoto 2D similarity based on fingerprints from the SMILES representation of the chemical structure. Our analysis of 1165 FDA-approved drugs indicates that the chemical similarity of drugs predicts closeness of their targets in the human interactome.

We introduce linear relational embedding as a means of learning a distributed representation of concepts from data consisting of binary relations between these concepts. The key idea is to represent concepts as vectors, binary relations as matrices, and the operation of applying a relation to a concept as a matrix-vector multiplication that produces an approximation to the related concept. A representation for concepts and relations is learned by maximizing an appropriate discriminative goodness function using gradient ascent. On a task involving family relationships, learning is fast and leads to good generalization.