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Related Experiment Video

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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Ring-System-Based Exhaustive Structure Generation for Inverse-QSPR/QSAR.

Tomoyuki Miyao1, Hiromasa Kaneko1, Kimito Funatsu2

  • 1Department of Chemical System Engineering, Graduated of School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan phone: (+81) 3-5841-7751, fax: (+81) 3-5841-7771.

Molecular Informatics
|August 4, 2016
PubMed
Summary
This summary is machine-generated.

This study presents a novel method for generating chemical structures with desired properties using a ring-system-based approach. This strategy efficiently designs molecules by assembling fragments, considering the applicability domain for targeted drug discovery.

Keywords:
ChemoinformaticsDrug designInverse QSPR/QSARStructure generation

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Area of Science:

  • Computational chemistry
  • cheminformatics
  • Drug discovery

Background:

  • Quantitative Structure-Property Relationship (QSPR) and Quantitative Structure-Activity Relationship (QSAR) models predict molecular properties but do not generate structures.
  • The inverse problem, generating structures from desired properties, is computationally challenging.

Purpose of the Study:

  • To develop an efficient methodology for inverse QSPR/QSAR problems.
  • To enable the generation of novel chemical structures with specific, desired properties or activities.

Main Methods:

  • An exhaustive ring-system-based structure generation methodology was developed.
  • The applicability domain (AD) was integrated, considering both local and universal AD.
  • Structures were enumerated by assembling fragments and atomic elements in a tree-like manner.

Main Results:

  • The method successfully generated chemical structures with desired properties for a specific target (ADR2A_HUMAN).
  • Structures were efficiently generated within a pre-defined chemical space, increasing the likelihood of desired activity.

Conclusions:

  • The proposed method offers an efficient solution for inverse QSPR/QSAR problems.
  • This approach facilitates targeted ligand design and drug discovery by generating molecules with high probability of desired activity.