How new drug R&D can solve intractable scientific problems and meet clinical needs: insights from analysis of typical use cases

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.

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Publication Details

Journal
Acta Materia Medica
Published
2026-01-01
DOI
https://doi.org/10.15212/amm-2025-0054
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

How new drug R&D can solve intractable scientific problems and meet clinical needs: insights from analysis of typical use cases

Haiyong Jia, Peng Zhan, Mei Wang, Yuning Song et al.
Acta Materia Medica
Computational Drug Discovery Methods
article

How new drug R&D can solve intractable scientific problems and meet clinical needs: insights from analysis of typical use cases

Haiyong Jia, Peng Zhan, Mei Wang, Yuning Song, Kai Song
article en

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.

Acta Materia MedicaVol. 5(1)
Openalex Percentile: Top 93%
Computational Drug Discovery Methods
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How new drug R&D can solve intractable scientific problems and meet clinical needs: insights from analysis of typical use cases — Haiyong Jia, Peng Zhan, et al. · Acta Materia Medica (2026) | TGRS Research Map | TGRS