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Related Concept Videos

Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal assumptions,...
Dose-Response Relationship: Overview01:03

Dose-Response Relationship: Overview

Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...

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Related Experiment Video

Updated: May 26, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

A Scalable Sign-Aware Multi-Omics Knowledge Graph Foundation Model for Mechanistic Drug Action and Clinical Response

Mohammadsadeq Mottaqi1, Shuo Zhang2, Ian Adoremos3

  • 1Ph.D. Program in Biochemistry, The Graduate Center, The City University of New York, 365 Fifth Avenue, New York, NY, 10016, United States.

Biorxiv : the Preprint Server for Biology
|May 25, 2026
PubMed
Summary

This study introduces SIGMA-KG and FLASH, a signed knowledge graph and graph neural network model. FLASH accurately predicts drug actions and repurposing opportunities by capturing biological directionality, outperforming existing methods.

Keywords:
deep learningdrug discoverydrug repurposingdrug-drug interactiongraph foundation modelgraph neural networkmachine learningsigned multi-omics knowledge graph

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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Last Updated: May 26, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

  • Computational Biology
  • Pharmacology
  • Bioinformatics

Background:

  • Predicting drug action necessitates understanding activating vs. inhibitory interactions.
  • Current biomedical knowledge graphs and graph neural networks (GNNs) often use unsigned associations, limiting mechanistic insights and chemical coverage.
  • Regulatory logic and directionality are obscured in existing unsigned models.

Purpose of the Study:

  • To develop a novel signed knowledge graph and graph foundation model for mechanistic drug action prediction.
  • To integrate diverse biological data types while preserving polarity and directionality.
  • To enable scalable and explainable mechanistic reasoning in drug discovery.

Main Methods:

  • Introduction of SIGMA-KG (Signed Multi-omics Atlas Knowledge Graph), integrating chemogenomic, transcriptomic, proteomic, and clinical data.
  • Development of FLASH (Fast Lightweight Architecture for Signed Heterogeneous GNN), a graph foundation model pretrained on SIGMA-KG using self-supervised learning.
  • FLASH utilizes structural balance principles to preserve polarity across multi-hop pathways for mechanistic reasoning.

Main Results:

  • FLASH outperforms or matches nine state-of-the-art graph baselines on drug mode-of-action, clinical response, and drug-drug interaction prediction without task-specific fine-tuning.
  • The model demonstrates substantial improvements in computational efficiency.
  • FLASH achieved a 69.6% external clinical validation success rate for explainable inductive drug repurposing across four complex diseases.

Conclusions:

  • The proposed SIGMA-KG and FLASH model provide a powerful framework for signed, multi-omics knowledge graph representation learning.
  • FLASH enables accurate and efficient mechanistic prediction of drug action and facilitates drug repurposing.
  • This approach advances the field of GNNs for biomedical applications by incorporating biological directionality.