Deep Learning for Anticancer Drug Discovery Targeting Non-Apoptotic Regulated Cell Death Mechanisms.

Area of Science:

• Biochemistry and Molecular Biology
• Computational Biology and Bioinformatics
• Pharmacology and Drug Discovery

Background:

• Non-apoptotic regulated cell death (RCD) pathways, including ferroptosis and cuproptosis, present novel therapeutic targets for overcoming resistance to conventional antitumor treatments.
• Deep learning (DL) offers advanced computational capabilities for identifying bioactive molecules by integrating multi-source data.

Purpose of the Study:

• To review deep learning methodologies and application advancements in the study of non-apoptotic RCD mechanisms.
• To establish a preliminary technical framework for DL applications in non-apoptotic RCD research.
• To discuss current and future directions for DL in targeting non-apoptotic RCD.

Main Methods:

• Delineation of core DL methodologies: end-to-end molecular representations, multimodal fusion, dataset partitioning, and DL frameworks.
• Analysis of DL applications across direct, adjacent, and speculative dimensions within non-apoptotic RCD.
• Identification of key challenges and future research priorities.

Main Results:

• A comprehensive overview of DL techniques applicable to non-apoptotic RCD research is presented.
• Current applications of DL in targeting ferroptosis, cuproptosis, and other RCD modalities are discussed.
• A roadmap for future research, including database construction and AI method development, is proposed.

Conclusions:

• Deep learning provides a powerful framework for advancing the study of non-apoptotic RCD and accelerating drug discovery.
• Future efforts should focus on high-quality data, explainable AI, few-shot learning, and dynamic models for robust translational research.
• Integrating computational prediction with experimental validation is crucial for clinical translation.