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Related Concept Videos

Ligand Binding Sites02:40

Ligand Binding Sites

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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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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

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Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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Cooperative Allosteric Transitions01:58

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Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...
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The Equilibrium Binding Constant and Binding Strength02:18

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The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
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Updated: Jan 18, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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Molecule generation for target protein binding with hierarchical consistency diffusion model.

Guanlue Li1, Chenran Jiang2, Ziqi Gao3

  • 1Data Science and Analytics, The Hong Kong University of Science and Technology (Guang Zhou) Guangzhou 511400 China guanlueli@gmail.com.

Chemical Science
|September 11, 2025
PubMed
Summary
This summary is machine-generated.

We developed a new AI model, the Atom-Motif Consistency Diffusion Model (AMDiff), for generating valid and novel drug molecules. This approach enhances structure-based drug design for specific protein targets.

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Area of Science:

  • Computational chemistry
  • Artificial intelligence in drug discovery
  • Molecular modeling

Background:

  • Generating valid and reliable 3D molecular structures is essential for drug discovery.
  • Current atom- and motif-wise models face challenges with molecular validity and reliability.
  • Bridging atom-view and motif-view approaches is needed for comprehensive molecular generation.

Purpose of the Study:

  • To develop an advanced AI model for effective 3D molecular structure generation.
  • To improve the validity and reliability of generated molecules for drug discovery.
  • To accelerate the design of target-specific drug candidates.

Main Methods:

  • Developed the Atom-Motif Consistency Diffusion Model (AMDiff) using a joint-training paradigm for multi-view learning.
  • Implemented a hierarchical diffusion architecture integrating atom- and motif-views.
  • Utilized classifier-free guidance and topological features as conditional inputs.

Main Results:

  • AMDiff demonstrated superior validity and novelty in generating molecules for diverse protein pockets compared to existing methods.
  • The model successfully generated molecules tailored to specific protein targets.
  • Case studies on Anaplastic Lymphoma Kinase (ALK) and Cyclin-dependent kinase 4 (CDK4) showed effectiveness in structure-based de novo drug design.

Conclusions:

  • AMDiff effectively integrates atom- and motif-level information for robust molecular generation.
  • The model enhances structure-based de novo drug design by improving molecule validity and novelty.
  • AMDiff accelerates the development of target-specific molecules in drug discovery.