Structural bioinformatics for rational drug design

Research output: Contribution to journal(Systematic) Review article peer-review

Abstract

A State of the Art lecture titled "structural bioinformatics technologies for rational drug design: from in silico to in vivo" was presented at the International Society on Thrombosis and Haemostasis (ISTH) Congress in 2024. Drug discovery remains a resource-intensive and complex endeavor, which usually takes over a decade and costs billions to bring a new therapeutic agent to market. However, the landscape of drug discovery has been transformed by the recent advancements in bioinformatics and cheminformatics. Key techniques, including structure- and ligand-based virtual screening, molecular dynamics simulations, and artificial intelligence-driven models are allowing researchers to explore vast chemical spaces, investigate molecular interactions, predict binding affinity, and optimize drug candidates with unprecedented accuracy and efficiency. These computational methods complement experimental techniques by accelerating the identification of viable drug candidates and refining lead compounds. Artificial intelligence models, alongside traditional physics-based simulations, now play an important role in predicting key properties such as binding affinity and toxicity, contributing to more informed decision-making, particularly early in the drug discovery process. Despite these advancements, challenges remain in terms of accuracy, interpretability, and the needed computational power. This review explores the state of the art in computational drug discovery, examining the latest methods and technologies, their transformative impact on the drug development pipeline, and the future directions needed to overcome remaining limitations. Finally, we summarize relevant data and highlight cases where various computational approaches were successfully applied to develop novel inhibitors, as presented during the ISTH 2024 Congress.
Original languageEnglish
Article numbere102691
Number of pages13
JournalResearch and practice in thrombosis and haemostasis
Volume9
Issue number1
DOIs
Publication statusPublished - 1 Jan 2025

Keywords

  • artificial intelligence
  • computer-aided molecular design
  • drug discovery
  • machine learning
  • molecular docking
  • molecular dynamics simulation
  • MACHINE LEARNING APPROACH
  • LIGAND BINDING-AFFINITY
  • MOLECULAR-DYNAMICS
  • C2 DOMAIN
  • PROTEIN
  • DOCKING
  • ATHEROSCLEROSIS
  • INHIBITORS
  • PREDICTION
  • HISTONES

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