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Drug Discovery: Overview01:26

Drug Discovery: Overview

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

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Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
Receptors are either membrane-spanning or intracellular proteins, which upon binding a ligand, get activated and transmit the signal downstream to elicit a response. Drugs bind receptors, either mimicking the action of endogenous ligands or blocking the receptor activity to bring about a modified response. Nearly 35% of approved drugs target the G...
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G Protein-coupled Receptors01:15

G Protein-coupled Receptors

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G Protein-Coupled Receptors or GPCRs are membrane-bound receptors that transiently associate with heterotrimeric G proteins and induce an appropriate response to sensory stimuli such as light, odors, hormones, cytokines, or neurotransmitters.
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Principles of Drug Action01:24

Principles of Drug Action

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Drugs are chemical substances that modify biological responses by interacting with macromolecular targets such as receptors, ion channels, transporters, and enzymes. Pharmacodynamics describes the course of action of drugs leading to the physiological effect at a specific site in the body.
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Related Experiment Video

Updated: Jan 16, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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From Gene Networks to Therapeutics: A Causal Inference and Deep Learning Approach for Drug Discovery.

Sudhir Ghandikota1, Anil G Jegga1,2

  • 1Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.

Pharmaceuticals (Basel, Switzerland)
|September 27, 2025
PubMed
Summary

This study introduces a computational framework combining causal inference and deep learning to accelerate drug discovery for complex diseases like idiopathic pulmonary fibrosis (IPF). It identifies novel gene targets and potential drug candidates, streamlining the development process.

Keywords:
deep learningdrug discoverydrug repositioningdrug repurposingidiopathic pulmonary fibrosismediation analysisnetwork analysis

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Area of Science:

  • Computational biology
  • Genomics
  • Pharmacology

Background:

  • Drug discovery is time-consuming and costly, especially for complex diseases.
  • Idiopathic pulmonary fibrosis (IPF) presents challenges due to heterogeneity and unknown mechanisms.

Purpose of the Study:

  • To develop a novel computational framework integrating network analysis, statistical mediation, and deep learning.
  • To identify causal target genes and repurposable small-molecule candidates for IPF.

Main Methods:

  • Weighted gene co-expression network analysis (WGCNA) and bidirectional mediation analysis (causal WGCNA) were applied to transcriptomic data from IPF patients.
  • Deep learning-based compound screening using the DeepCE model was performed on identified causal genes.

Main Results:

  • Seven significantly correlated modules and 145 causal genes were identified in IPF.
  • Five genes (ITM2C, PRTFDC1, CRABP2, CPNE7, NMNAT2) predicted IPF disease severity.
  • Telaglenastat, Merestinib, and Cilostazol emerged as promising drug candidates.

Conclusions:

  • Combining causal inference and deep learning is effective for drug discovery.
  • The framework identified novel IPF targets and therapeutic candidates.
  • This approach offers a scalable strategy for phenotype-driven drug discovery and repurposing.