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<p>PROTACs, also called proximity-inducing agents, are chimeric molecules composed of a ligand for protein of interest (POI), an E3 ligase ligand and a linker connecting them. PROTACs have transformed the therapeutic landscape by enabling an event-driven strategy to degrade disease-associated proteins previously regarded as undruggable. The unique event-driven mechanism of PROTACs allows selective protein degradation with greater potency and lower drug resistance than conventional occupancy-based inhibitors. Despite their advantages, challenges such as high molecular weight, low permeability, poor pharmacokinetic properties restrict their clinical applications. To overcome these limitations, AI-driven technologies are being utilised to generate novel, chemically valid PROTACs. This review highlights the drawbacks of conventional computational methods and explores emerging AI-driven tools applied to multiple areas of PROTAC research, such as target (POI) selection (DeepUSI, DrugnomeAI), linker generation (AIMLinker, DiffLinker), activity prediction (AI-DPAPT, DeepPROTAC), POI degradability assessment (PrePROTAC, MAPD), ternary complex modelling (ProFlow), PROTAC generation (PROTAC-RL), and ADME property estimation (MT-GNN). It also outlines current challenges such as data scarcity, reproducibility issues, inadequate model generalizability, emphasizing the need for hybrid models or integrated AI techniques to mitigate these limitations.</p>

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Integrating AI into next-generation PROTAC Engineering: a comprehensive toolkit for rational PROTAC design

  • 期刊论文

  • 发表日期：

    2026年5月3日

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  • Pitam Ghosh,
  • Ryena Dhir,
  • Dinki Sharma,
  • Vivek Asati

摘要

PROTACs, also called proximity-inducing agents, are chimeric molecules composed of a ligand for protein of interest (POI), an E3 ligase ligand and a linker connecting them. PROTACs have transformed the therapeutic landscape by enabling an event-driven strategy to degrade disease-associated proteins previously regarded as undruggable. The unique event-driven mechanism of PROTACs allows selective protein degradation with greater potency and lower drug resistance than conventional occupancy-based inhibitors. Despite their advantages, challenges such as high molecular weight, low permeability, poor pharmacokinetic properties restrict their clinical applications. To overcome these limitations, AI-driven technologies are being utilised to generate novel, chemically valid PROTACs. This review highlights the drawbacks of conventional computational methods and explores emerging AI-driven tools applied to multiple areas of PROTAC research, such as target (POI) selection (DeepUSI, DrugnomeAI), linker generation (AIMLinker, DiffLinker), activity prediction (AI-DPAPT, DeepPROTAC), POI degradability assessment (PrePROTAC, MAPD), ternary complex modelling (ProFlow), PROTAC generation (PROTAC-RL), and ADME property estimation (MT-GNN). It also outlines current challenges such as data scarcity, reproducibility issues, inadequate model generalizability, emphasizing the need for hybrid models or integrated AI techniques to mitigate these limitations.