3D based generative PROTAC linker design with reinforcement learning

PROTAC technology: From drug development to probe technology for target deconvolution

Drug development remains a critical focus within the global pharmaceutical industry. To date, more than 80 % of disease targets are considered difficult to target. The emergence of PROTAC technology has, to some extent, alleviated this challenge. Since introduction, PROTAC technology has evolved through the peptide E3 ligase ligand phase and the small molecule E3 ligase ligand phase. Currently, multiple PROTAC molecules are in the clinical research phase, showing promising potential for addressing drug resistance, disease recurrence, and intractable targets. Target deconvolution is a crucial step in the drug discovery and development process. Due to the exceptional targeting ability and specificity of PROTAC, it is widely used and promoted as an innovative technology for discovering new drug targets, leading to significant breakthroughs. The use of PROTAC probe requires only a catalytic dose and weak interaction with the target protein to achieve target degradation. Thus, it offers substantial advantages over traditional probes, particularly in identifying new targets that are low-abundance or difficult to target. This review provides a comprehensive overview of the advancements made by PROTAC technology in drug development and drug target discovery, while also systematically reviewing the workflow of PROTAC probe. With the ongoing development of PROTAC technology, PROTAC probe is poised to become a key research area in future drug target deconvolution.

A Top-Down Design Approach for Generating a Peptide PROTAC Drug Targeting Androgen Receptor for Androgenetic Alopecia Therapy

While large-scale artificial intelligence (AI) models for protein structure prediction and design are advancing rapidly, the translation of deep learning models for practical macromolecular drug development remains limited. This investigation aims to bridge this gap by combining cutting-edge methodologies to create a novel peptide-based PROTAC drug development paradigm. Using ProteinMPNN and RFdiffusion, we identified binding peptides for androgen receptor (AR) and Von Hippel-Lindau (VHL), followed by computational modeling with Alphafold2-multimer and ZDOCK to predict spatial interrelationships. Experimental validation confirmed the designed peptide's binding ability to AR and VHL. Transdermal microneedle patching technology was seamlessly integrated for the peptide PROTAC drug delivery in androgenic alopecia treatment. In summary, our approach provides a generic method for generating peptide PROTACs and offers a practical application for designing potential therapeutic drugs for androgenetic alopecia. This showcases the potential of interdisciplinary approaches in advancing drug development and personalized medicine.

Revolutionizing Drug Targeting Strategies: Integrating Artificial Intelligence and Structure-Based Methods in PROTAC Development

PROteolysis TArgeting Chimera (PROTAC) is an emerging technology in chemical biology and drug discovery. This technique facilitates the complete removal of the target proteins that are "undruggable" or challenging to target through chemical molecules via the Ubiquitin-Proteasome System (UPS). PROTACs have been widely explored and outperformed not only in cancer but also in other diseases. During the past few decades, several academic institutes and pharma companies have poured more efforts into PROTAC-related technologies, setting the stage for several major degrader trial readouts in clinical phases. Despite their promising results, the formation of robust ternary orientation, off-target activity, poor permeability, and binding affinity are some of the limitations that hinder their development. Recent advancements in computational technologies have facilitated progress in the development of PROTACs. Researchers have been able to utilize these technologies to explore a wider range of E3 ligases and optimize linkers, thereby gaining a better understanding of the effectiveness and safety of PROTACs in clinical settings. In this review, we briefly explore the computational strategies reported to date for the formation of PROTAC components and discuss the key challenges and opportunities for further research in this area.