Drug mechanisms

Drug mechanisms

Pharmacology sits at the intersection of molecules, cells, organs, and patient outcomes, and understanding the mechanism of a drug requires reasoning across all of these scales. We apply ontologies, knowledge graphs, and semantic representations to model drug-target interactions, drug indications, and adverse drug reactions, linking molecular biology to systems-level physiology through causal structures over biomedical knowledge. The aim is to move beyond statistical associations to mechanistic, machine-readable models of drug action that can support repurposing, anticipate adverse effects

NanoDesigner

Iterative refinement framework for nanobody/CDR design that explicitly models the antigen–CDR interdependence; companion code to the NanoDesigner paper. Get it GitHub: https://github.com/bio-ontology-research-group/NanoDesigner ★ 16 Developed in projects Disease Models from Patient-derived Leukemic Cells in Biomimetic Peptide Scaffolds for Precision Medicine Applications Category: Knowledge Graphs & Drug Discovery

PathoPhenoDB

Curated database of pathogens and the disease phenotypes they cause, distributed as an OWL ontology and an interactive web application. Get it GitHub: https://github.com/bio-ontology-research-group/pathophenodb ★ 8 Developed in projects Computational methods for functional metagenomics: from protein functions to multi-scale interactions Category: Knowledge Graphs & Drug Discovery

DeepMOCCA

Graph neural network for cancer survival analysis that integrates multi-omics (mutation, expression, methylation, CNV) with a curated cancer knowledge graph. Get it GitHub: https://github.com/bio-ontology-research-group/DeepMOCCA ★ 14 Developed in projects Bio2Vec: Smart analytics infrastructure for the life sciences Category: Knowledge Graphs & Drug Discovery

Multi-Drug Embedding

Drug repurposing method that learns joint embeddings of drugs, targets and diseases from biomedical knowledge graphs and the scientific literature. Get it GitHub: https://github.com/bio-ontology-research-group/multi-drug-embedding ★ 36 Developed in projects Bio2Vec: Smart analytics infrastructure for the life sciences Category: Knowledge Graphs & Drug Discovery

SmuDGE

Semantic disease-gene embeddings; integrates phenotype, function and pathway ontologies into a unified vector space for downstream prediction. Get it GitHub: https://github.com/bio-ontology-research-group/SMUDGE ★ 12 Developed in projects Bio2Vec: Smart analytics infrastructure for the life sciences CompleX: Variant Prioritization in Complex Disease Category: Knowledge Graphs & Drug Discovery

Data integration and ontologies for microbial cell factories

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 Computational Bioscience Research Center's Microbial Cell Factories initiative, led by Vladimir Bajic with Hoehndorf as CoI heading the data-integration
coe genai bcb

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

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