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Guided structure-based ligand identification and design via artificial intelligence modeling.

Juan I Di Filippo1,2,3, Claudio N Cavasotto1,2,3

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Expert Opinion on Drug Discovery
|September 21, 2021
PubMed
Summary
This summary is machine-generated.

Artificial Intelligence (AI) is increasingly used in drug discovery (DD). This review covers AI applications in structure-based virtual screening and de novo drug design from 2019 to present, highlighting key findings and future directions.

Keywords:
Artificial intelligencedrug discoverymachine learningmolecular dockingstructure-based virtual screening

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Area of Science:

  • Computational chemistry
  • Medicinal chemistry
  • Artificial intelligence

Background:

  • Artificial Intelligence (AI) methodologies are rapidly advancing in drug discovery (DD).
  • AI offers novel frameworks for identifying ligands and designing new molecules for therapeutic targets.
  • Structure-based drug discovery (SBVS) is a key area benefiting from AI integration.

Purpose of the Study:

  • To review AI applications in structure-based virtual screening (SBVS) and de novo drug design from 2019 to present.
  • To analyze studies based on objectives, databases, methodologies, inputs/outputs, and results.
  • To provide insights into the validity and future trends of AI in structure-based DD.

Main Methods:

  • Review of scientific literature published between 2019 and the present.
  • Analysis of AI applications in molecular docking (binding pose prediction, hit identification).
  • Examination of machine learning (ML) generative models for de novo drug design.
  • Evaluation of AI model validation in structure-based screening.

Main Results:

  • AI methods are increasingly applied to molecular docking and de novo ligand design.
  • Studies vary in objectives, databases, and methodologies, impacting AI model validation.
  • The review synthesizes key findings from recent AI-driven structure-based DD research.

Conclusions:

  • AI is transforming structure-based drug discovery, offering powerful tools for ligand identification and design.
  • Future research should focus on structure-based generative models and standardized validation guidelines.
  • Further analysis is needed to fully understand the validity and applicability of AI in structure-based DD.