Work

Decoding small cell lung cancer vulnerabilities: the AI pathway to next-gen therapeutics

Year: 2026

Type: article

Abstract: Small cell lung cancer (SCLC) is aggressive, treatment-sensitive, and prone to relapse. This Review evaluates how generative AI, graph neural networks, molecular language and diffusion models, structure-based design, and multimodal learning can exploit SCLC dependencies for small-molecule discovery. It integrates biomarker-guided patient stratification, resistance, combination therapy, medicinal chemistry, pharmacology, safety, manufacturing, and clinical translation, emphasizing benchmarked, uncertainty-aware, prospectively validated design–build–test–learn workflows.

Source: npj Precision Oncology

DOI: 10.1038/s41698-026-01705-6

Authors: Mu Li, Lei Zhang, Zhenglun Yu, Jun Li, Shuo Wang, Simeng Zhang

Institutions: Sheng Jing Hospital, First Hospital of China Medical University

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $4,090