How new drug R&D can solve intractable scientific problems and meet clinical needs: insights from analysis of typical use cases
Year: 2026
Type: article
Abstract: New drug research and development (R&D) faces critical challenges such as drug resistance, ineffectiveness of single-target interventions, “undruggable” targets, long cycles, and high costs. This article analyzes five typical use cases to illustrate breakthrough strategies for addressing these issues. Innovations include the identification of dual-target antibiotics (paenimicin) and phospholipid-targeting antifungals (mandimycin) that overcome microbial resistance via mining silent biosynthetic gene clusters; development of “molecular glue” compounds (D927 and BBO-10203) that regulate protein-protein interactions, thus addressing insulin resistance and tumor drug resistance; AI-driven discovery of the TNIK inhibitor rentosertib for reversing pulmonary fibrosis; use of protein interface remodeling to develop the KRAS G12D inhibitor zoldonrasib for “undruggable” targets; and repurposing of existing drug combinations (letrozole plus irinotecan), on the basis of multi-omics data for Alzheimer’s disease. These use cases demonstrate how interdisciplinary technologies (e.g., AI and structural biology) and strategic innovations (multi-target design and drug repurposing) can drive a transformation from experience-driven to precision and intelligence-driven R&D, and provides a reference for meeting intractable clinical needs.
Source: Acta Materia Medica
Authors: Mei Wang, Kai Song, Haiyong Jia, Peng Zhan, Yuning Song
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7119055826