Drug mechanisms

Su1295: Chemically defined peptide-based matrices enabling the development of colorectal organoid models for therapeutic applications and disease modeling

Year: 2025 Venue: Gastroenterology Authors: Dana Alhattab, Duna Barakeh, Basma Khoja, Ahmad Elhadi, Jameel Miro, Saleh A. Alessy, Ahmed Alharbi, Manal Bokhary, May Alzahrani, Saga Ali, Wadha Almohamdi, Lama Hefni, Manola Moretti, Abeer Abdullah, Waleed Alomaim, Robert Hoehndorf, Charlotte Hauser, Saleh A. Alqahtani DOI: 10.1016/s0016-5085(25)02643-5 Topics Bioengineering · Drug mechanisms

Integration of knowledge for personalized medicine: a pharmacogenomics case-study

Drug mechanisms Ontology engineering

Year: 2012 Venue: Proceedings of the Virtual Physiological Human Conference 2012 (VPH2012) Authors: Robert Hoehndorf, Michel Dumontier, Georgios V. Gkoutos Topics Drug mechanisms · Ontology engineering

SIDEKICK: A Semantically Integrated Resource for Drug Effects, Indications, and Contraindications

Drug mechanisms Neuro-Symbolic AI

Year: 2026 Venue: The Semantic Web -- ESWC 2026 Authors: Mohammad Ashhad, Olga Mashkova, Ricardo Henao, Robert Hoehndorf DOI: 10.1007/978-3-032-25159-6_14 Abstract Pharmacovigilance and clinical decision support systems utilize structured drug safety data to guide medical practice. However, existing datasets frequently depend on terminologies such as MedDRA, which limits their semantic reasoning capabilities and their interoperability with Semantic Web ontologies and knowledge graphs. To address this gap, we developed SIDEKICK, a knowledge graph that standardizes drug indications

Mouse model phenotypes provide information about human drug targets

Drug mechanisms Rare disease

Year: 2013 Venue: Bioinformatics Authors: Robert Hoehndorf, Tanya Hiebert, Nigel W. Hardy, Paul N. Schofield, Georgios V. Gkoutos, Michel Dumontier Abstract Motivation: Methods for computational drug target identification utilize information from diverse information sources to predict or prioritize drug targets for known drugs. One set of resources that has been relatively neglected for drug repurposing are animal model phenotypes.Results: We investigate the use of mouse model phenotypes for drug target identification. To achieve this goal, we first integrate mouse model phenotypes and drug

Semantic Systems Biology: Formal Knowledge Representation in Systems Biology for Model Construction, Retrieval, Validation and Discovery

Applied Ontology Drug mechanisms

Year: 2013 Venue: Systems Biology Authors: Michel Dumontier, Leonid L Chepelev, Robert Hoehndorf Abstract With the publication of the human genome, scientists worldwide opened champagne and let out a collective cheer for progress in biology. After all, the untold number of interactions of tens of thousands of genes, a greater number of their products and product derivatives, and tens of thousands of chemicals came much closer to complete characterization. Paradoxically however, while individual efforts produced important biological results, an integrated view of biology from systems

Identifying aberrant pathways through integrated analysis of knowledge in pharmacogenomics

Drug mechanisms Biomedical Informatics

Year: 2012 Venue: Bioinformatics Authors: Robert Hoehndorf, Michel Dumontier, Georgios V. Gkoutos Abstract Motivation: Many complex diseases are the result of abnormal pathway functions instead of single abnormalities. Disease diagnosis and intervention strategies must target these pathways while minimizing the interference with normal physiological processes. Large scale identification of disease pathways and chemicals that may be used to perturb them requires the integration of information about drugs, genes, diseases and pathways. This information is currently distributed over several

Linking PharmGKB to phenotype studies and animal models of disease for drug repurposing

Drug mechanisms Rare disease Applied Ontology

Venue: Pacific Symposium on Biocomputing (PSB) Authors: Robert Hoehndorf, Anika Oellrich, Dietrich Rebholz-Schuhmann, Paul N. Schofield, Georgios V. Gkoutos Abstract The investigation of phenotypes in model organisms has the potential to reveal the molecular mechanisms underlying disease. The large-scale comparative analysis of phenotypes across species can reveal novel associations between genotypes and diseases. We use the PhenomeNET network of phenotypic similarity to suggest genotype--disease association, combine them with drug--gene associations available from the PharmGKB database, and

Causal knowledge graph analysis identifies adverse drug effects

Drug mechanisms

Year: 2025 Venue: Bioinformatics Authors: Sumyyah Toonsi, Paul N Schofield, Robert Hoehndorf DOI: 10.1093/bioinformatics/btaf661 Abstract The data is available through https://github.com/bio-ontology-research-group/Mediation-Analysis-using-Causal-Knowledge-Graph. Topics Drug mechanisms

Nanodesigner: resolving the complex-CDR interdependency with iterative refinement

bioengineering Drug mechanisms

Year: 2025 Venue: Journal of Cheminformatics Authors: Melissa Maria Rios Zertuche, Senay Kafkas, Dominik Renn, Magnus Rueping, Robert Hoehndorf DOI: 10.1186/s13321-025-01069-2 Abstract Abstract Camelid heavy-chain only antibodies consist of two heavy chains and single variable domains (VHHs), which retain antigen-binding functionality even when isolated. The term “nanobody” is now more generally used for describing small, single-domain antibodies. Several antibody generative models have been developed for the sequence and structure co-design of the complementarity-determining regions (CDRs)

Similarity-based search of model organism, disease and drug effect phenotypes

Semantic similarity Rare disease Drug mechanisms

Year: 2015 Venue: Journal of Biomedical Semantics Authors: Robert Hoehndorf, Michael Gruenberger, Georgios Gkoutos, Paul Schofield DOI: 10.1186/s13326-015-0001-9 Abstract BACKGROUND:Semantic similarity measures over phenotype ontologies have been demonstrated to provide a powerful approach for the analysis of model organism phenotypes, the discovery of animal models of human disease, novel pathways, gene functions, druggable therapeutic targets, and determination of pathogenicity.RESULTS:We have developed PhenomeNET 2, a system that enables similarity-based searches over a large repository of

DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug interactions

Drug mechanisms Neuro-Symbolic AI

Year: 2021 Venue: Bioinformatics Authors: Tilman Hinnerichs, Robert Hoehndorf DOI: 10.1093/bioinformatics/btab548 Abstract Supplementary data are available at Bioinformatics online. Topics Drug mechanisms · Neuro-symbolic AI

Ranking Adverse Drug Reactions With Crowdsourcing

Drug mechanisms Biomedical Informatics

Year: 2015 Venue: J Med Internet Res Authors: Assaf Gottlieb, Robert Hoehndorf, Michel Dumontier, B. Russ Altman DOI: 10.2196/jmir.3962 Abstract Background: There is no publicly available resource that provides the relative severity of adverse drug reactions (ADRs). Such a resource would be useful for several applications, including assessment of the risks and benefits of drugs and improvement of patient-centered care. It could also be used to triage predictions of drug adverse events. Objective: The intent of the study was to rank ADRs according to severity. Methods: We used Internet-based

Molecular basis and cellular effects of Janus-class–driven cytoplasmic PYK2 coacervates

Drug mechanisms bioengineering

Year: 2026 Venue: Communications Biology Authors: Giovanni Colombo, Israa Salem, Kacper Szczepski, Piao Yu, Shaden Alfaiyz, Francisco Javier Guzman-Vega, Ahmed Abogosh, Maxat Kulmanov, Samah Al-Harthi, Gress Kadare, Robert Hoehndorf, Jean-Antoine Girault, Łukasz Jaremko, Afaque A. Momin, Stefan T. Arold DOI: 10.1038/s42003-025-09463-0 Abstract Kinase activity is increasingly linked to biomolecular phase separation. Focal adhesion kinase (FAK) forms membrane-associated condensates with paxillin to promote adhesion. Here we show that its paralogue, proline-rich tyrosine kinase 2 (PYK2)

LEP-AD: language embedding of proteins and attention to drugs predicts drug-target interactions

Drug mechanisms Neuro-Symbolic AI

Year: 2026 Venue: Journal of Cheminformatics Authors: Reem Alsulami, Robert Lehmann, Anuj Daga, Sumeer A. Khan, Raik Gr\"unberg, Ahmed Abogosh, David Gomez Cabrero, Stefan T. Arold, Robert Hoehndorf, Jesper Tegner, Narsis A. Kiani DOI: 10.1186/s13321-026-01167-9 Abstract INTRODUCTION: Predicting drug-target interactions remains a significant challenge in drug development and lead optimization. Recent advances have leveraged machine learning algorithms to model drug-target interactions from molecular and sequence data. MATERIALS AND METHODS: In this work, we use Evolutionary Scale Modeling (ESM

Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications

Drug mechanisms Neuro-Symbolic AI

Year: 2022 Venue: PeerJ Authors: Mona Alshahrani, Abdullah Almansour, Asma Alkhaldi, Maha A. Thafar, Mahmut Uludag, Magbubah Essack, Robert Hoehndorf DOI: 10.7717/peerj.13061 Abstract Biomedical knowledge is represented in structured databases and published in biomedical literature, and different computational approaches have been developed to exploit each type of information in predictive models. However, the information in structured databases and literature is often complementary. We developed a machine learning method that combines information from literature and databases to predict drug

DDIEM: drug database for inborn errors of metabolism

Drug mechanisms Rare disease

Year: 2020 Venue: Orphanet Journal of Rare Diseases Authors: Marwa Abdelhakim, Eunice McMurray, Ali Raza Syed, Senay Kafkas, Allan Anthony Kamau, Paul N Schofield, Robert Hoehndorf DOI: 10.1186/s13023-020-01428-2 Abstract Abstract Background Inborn errors of metabolism (IEM) represent a subclass of rare inherited diseases caused by a wide range of defects in metabolic enzymes or their regulation. Of over a thousand characterized IEMs, only about half are understood at the molecular level, and overall the development of treatment and management strategies has proved challenging. An overview of

DeepViral: prediction of novel virus–host interactions from protein sequences and infectious disease phenotypes

Drug mechanisms Biomedical Informatics

Year: 2021 Venue: Bioinformatics Authors: Wang Liu-Wei, Senay Kafkas, Jun Chen, Nicholas J Dimonaco, Jesper Tegner, Robert Hoehndorf DOI: 10.1093/bioinformatics/btab147 Abstract Infectious diseases caused by novel viruses have become a major public health concern. Rapid identification of virus–host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms

Data integration and ontologies for microbial cell factories

Mon, May 18 2026

Applied Ontology Drug mechanisms Microbial communities Neuro-Symbolic AI

Industrial biotechnology depends on integrating data from sources that were never designed to be combined: enzyme and reaction databases, protein-family resources, pathway maps, strain collections, phenotypic screens, and the experimental literature itself. Each uses different identifiers, different vocabularies, and different levels of granularity, and naive integration produces brittle pipelines that break whenever a source is updated. The Microbial Cell Factories initiative at KAUST's Computational Bioscience Research Center (CBRC, now dissolved), led by Vladimir Bajic with Hoehndorf as CoI

KAUST Center of Excellence for Generative AI (Health and Wellness, BCB theme)

Mon, Jan 1 2024

Research

Drug mechanisms Neuro-Symbolic AI Rare disease

The KAUST Center of Excellence for Generative AI has a Health and Wellness theme, and within that theme the Bioinformatics and Computational Biology (BCB) workstream is responsible for the bio-side problems that generic generative models cannot solve on their own: protein and antibody design, population-specific drug development, and clinical foundation models that can actually reason with structured biomedical knowledge. The BORG group leads the neuro-symbolic and ontology-based components of this work (Sub-WBS FCC/1/5940-07-02), in partnership with Xin Gao's group on AI/drug design and the