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Molecular Modeling in Drug Design

by Rebecca C. Wade

Methods and challenges in computational drug design

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AlbakiReads Editorial

Methods and challenges in computational drug design

This collection brings together eight original research articles and five reviews on molecular modeling in drug discovery. Its topics range from virtual screening and pharmacophore modeling to artificial intelligence and machine learning in chemoinformatics. Other contributions examine molecular flexibility in drug–receptor interactions, including through molecular dynamics and enhanced sampling, as well as solvation, hydrogen bonding, and polarization. The volume also addresses design efforts involving protein–protein interfaces and membrane-protein receptors. Alongside recent methodological developments, the articles discuss challenges in applying these computational approaches to drug discovery.

Good fit for readers who enjoy

  • Researchers and students studying computational chemistry or molecular modeling.
  • Readers interested in computational methods used in drug discovery.
  • Those seeking a collection that combines original research with review articles.

Themes

Molecular modeling in drug designVirtual screening and pharmacophore modelingAI and machine learning in chemoinformaticsMolecular flexibility and drug–receptor interactionsMethodological challenges in drug discovery

Based on publisher information and book metadata.

Book overview

Since the first attempts at structure-based drug design about four decades ago, molecular modelling techniques for drug design have developed enormously, along with the increasing computational power and structural and biological information of active compounds and potential target molecules. Nowadays, molecular modeling can be considered to be an integral component of the modern drug discovery and development toolbox. Nevertheless, there are still many methodological challenges to be overcome in the application of molecular modeling approaches to drug discovery. The eight original research and five review articles collected in this book provide a snapshot of the state-of-the-art of molecular modeling in drug design, illustrating recent advances and critically discussing important challenges. The topics covered include virtual screening and pharmacophore modelling, chemoinformatic applications of artificial intelligence and machine learning, molecular dynamics simulation and enhanced sampling to investigate contributions of molecular flexibility to drug-receptor interactions, the modeling of drug-receptor solvation, hydrogen bonding and polarization, and drug design against protein-protein interfaces and membrane protein receptors.

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