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Related Concept Videos

Ligand Binding Sites02:40

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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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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Different monodentate and polydentate ligands are used as complexing agents in complexometric titration reactions. The formation of complexes by mono- and bidentate ligands involves two or more intermediate steps, limiting their use as complexing agents. In comparison, polydentate ligands can form complexes with metal ions in a single-step process, facilitating sharper end points. This means polydentate ligands, such as amino carboxylic acid derivatives, are most commonly employed in...
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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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Related Experiment Video

Updated: Nov 13, 2025

Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors
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Adapting the DeepSARM approach for dual-target ligand design.

Atsushi Yoshimori1, Huabin Hu2, Jürgen Bajorath3

  • 1Institute for Theoretical Medicine, Inc., 26-1 Muraoka-Higashi 2-chome, Fujisawa, Kanagawa, 251-0012, Japan.

Journal of Computer-Aided Molecular Design
|March 13, 2021
PubMed
Summary
This summary is machine-generated.

The Structure-Activity Relationship (SAR) Matrix (SARM) methodology and its extension, DeepSARM, enable novel analog design. DeepSARM is adapted for designing dual-target compounds, illustrated with anti-cancer drug discovery.

Keywords:
Deep generative modelingDual-target compound designMolecular grid mapsSAR matrixStructure–activity relationships

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Area of Science:

  • Medicinal Chemistry
  • Computational Drug Discovery
  • Pharmacology

Background:

  • The Structure-Activity Relationship (SAR) Matrix (SARM) methodology organizes compound series and visualizes SAR patterns.
  • DeepSARM enhances analog design using deep learning and generative modeling, incorporating information from related targets for increased structural novelty.

Purpose of the Study:

  • To present the foundational SARM methodology.
  • To discuss the adaptation of DeepSARM for designing dual-target compounds in polypharmacology.
  • To illustrate a computational proof-of-concept for designing dual-target anti-cancer inhibitors.

Main Methods:

  • Development and application of the SARM methodology for compound series extraction and organization.
  • Extension of SARM with DeepSARM, integrating deep learning and generative modeling.
  • Computational proof-of-concept focusing on dual-target inhibitor design for anti-cancer agents.

Main Results:

  • Demonstration of SARM's capability to extract and organize structurally related compound series.
  • Adaptation of DeepSARM for novel analog design, considering related targets.
  • Successful illustration of DeepSARM for designing candidate dual-target inhibitors against prominent anti-cancer targets.

Conclusions:

  • The SARM methodology provides a robust framework for SAR analysis and analog design.
  • DeepSARM significantly advances target-based analog design, enabling exploration of novel chemical space.
  • DeepSARM shows promise for accelerating polypharmacology-oriented drug discovery, particularly for dual-target agents.