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Related Concept Videos

Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower Kd...
Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
G Protein-coupled Receptors01:15

G Protein-coupled Receptors

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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Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Drug-Receptor Bonds01:25

Drug-Receptor Bonds

Drug-receptor bonds are formed through various chemical forces when drugs interact with target cells. Covalent bonds, strong and irreversible, are exemplified by DNA-alkylating anticancer agents that inhibit cell division. However, such irreversible drug binding lacks selectivity and can modify the DNA of the surrounding healthy cells. Covalent binding often contributes to tissue toxicity, as seen with chloroform and paracetamol metabolites binding to the liver, causing hepatotoxicity.
In...
The Two-State Receptor Model01:29

The Two-State Receptor Model

The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
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Related Experiment Video

Updated: Jul 6, 2026

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

A Unified Molecular Graph and Protein Language Model Framework for Predicting Human Drug-Hormone Receptor

Hamza Zahid1, Maryam1, Kil To Chong2

  • 1Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea.

Journal of Chemical Information and Modeling
|July 5, 2026
PubMed
Summary

A new deep learning model accurately predicts drug-hormone receptor interactions (DHRI) by integrating drug structure and hormone receptor sequence information. This receptor-aware approach enhances prediction reliability for drug efficacy and endocrine safety.

Related Experiment Videos

Last Updated: Jul 6, 2026

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

Area of Science:

  • Pharmacology
  • Computational Biology
  • Artificial Intelligence

Background:

  • Hormones are critical regulators of biological processes, and their signaling pathways are often targeted by therapeutics.
  • Understanding drug-hormone receptor interactions (DHRI) is vital for drug efficacy and endocrine safety.
  • Existing computational models for DHRI prediction lack specificity and do not fully leverage receptor information.

Purpose of the Study:

  • To develop a novel deep learning framework for predicting DHRI that explicitly incorporates hormone receptor-specific features.
  • To improve the accuracy and biological relevance of computational DHRI prediction models.
  • To assess the model's performance and generalization capabilities across different data splitting strategies.

Main Methods:

  • A receptor-aware deep learning framework was developed, integrating structural drug features (Morgan fingerprints, graph transformers) and hormone receptor sequence information (ESM2 protein language model).
  • Drug and receptor features were fused and processed through a multilayer neural network to predict interaction probabilities.
  • Model performance was rigorously evaluated using random, cold-drug, and scaffold-based split strategies on an independent dataset.

Main Results:

  • The model achieved high performance metrics, including 0.93 accuracy, 0.94 sensitivity, 0.92 specificity/precision, and 0.93 F1-score under random split.
  • Comparable performance was maintained under more stringent cold-drug and scaffold-based settings, demonstrating strong generalization ability.
  • Feature importance analysis identified key drug features and highlighted receptor-dependent contributions, with molecular docking supporting biological relevance.

Conclusions:

  • Incorporating hormone receptor-specific sequence information significantly enhances the reliability and biological interpretability of DHRI predictions.
  • The developed receptor-aware deep learning framework offers a promising tool for predicting DHRI, aiding in drug development and safety assessment.
  • This approach advances computational methods for understanding complex drug-target interactions within endocrine systems.