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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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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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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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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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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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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Deep generative models for ligand-based de novo design applied to multi-parametric optimization.

Quentin Perron1, Olivier Mirguet2,3, Hamza Tajmouati1

  • 1Iktos, Paris, France.

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|February 26, 2022
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) accelerates drug discovery by designing novel molecules for multi-parameter optimization (MPO). AI-generated compounds achieved an 86% success rate across 11 objectives, significantly outperforming initial molecules.

Keywords:
artificial intelligencedrug discoverylead-optimizationmultiparameter optimization

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Multi-parameter optimization (MPO) is a critical challenge in new chemical entity (NCE) drug discovery.
  • Deep learning generative models show promise for de novo molecular design, but their application in real-world MPO challenges remains underexplored.

Purpose of the Study:

  • To evaluate the efficacy of a ligand-based deep learning generative model for accelerating the discovery of lead compounds meeting multiple biological activity objectives simultaneously.
  • To demonstrate the practical benefits of applying AI technology to address MPO in an actual drug discovery project.

Main Methods:

  • Quantitative Structure-Activity Relationship (QSAR) models were developed for 11 biological activity objectives, achieving moderate to high performance.
  • A deep learning (DL)-based AI de novo design algorithm was employed, coupled with the QSAR models, to generate virtual compounds.
  • Synthesized and experimentally validated AI-designed compounds and compared their performance against initial molecules.

Main Results:

  • The AI de novo design approach successfully generated 150 virtual compounds predicted to be active across all 11 objectives.
  • Synthesized AI-designed compounds met an average of 9.5 objectives (86% success rate), significantly higher than the initial molecules' average of 6.4 objectives (58% success rate).
  • One AI-designed molecule exhibited activity across all 11 objectives, and two others were active on 10 objectives.

Conclusions:

  • AI-driven de novo design, integrated with QSAR modeling, effectively accelerates the identification of drug leads with improved multi-parameter optimization.
  • The AI algorithm identified novel chemical scaffolds and functional groups beneficial for MPO, expanding beyond the initial dataset.
  • This study validates the significant value of AI technology in addressing complex MPO challenges within practical drug discovery projects.