AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Key computational techniques, including molecular docking, molecular dynamics, and QSAR modeling, which enhance the accuracy and efficiency of drug design are explored, emphasizing their role in accelerating drug development and improving therapeutic outcomes.
This chapter provides a comprehensive procedure focusing on a combination of structure-based and ligand-based approaches for discovering inhibitors for development of therapies.
Key QM applications-torsional profiles, spectra prediction, reactivity analysis, and modeling of non-covalent interactions-highlighting their impact and limitations are reviewed, illustrating the trade-off between speed and accuracy.
It is concluded that computational protein structure prediction plays a critical role in accelerating antibiotic drug discovery and offers substantial potential for addressing antimicrobial resistance through more efficient and data-driven therapeutic development strategies.
An integrated overview of target prediction, network pharmacology, molecular docking, and ADMET prediction within a unified computational workflow for natural product-based drug discovery is provided to support more efficient, systematic, and evidence-based natural product-driven drug discovery.
Recent developments in QM-based molecular docking and HTD are reviewed, focusing on cases where QM is used explicitly in docking workflows.
This chapter presents the perspective of computational chemists at Sygnature Discovery on the growing need to integrate quantum mechanics (QM) with artificial intelligence (AI) to improve the efficiency and effectiveness of modern drug design.
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