Computer-Aided Drug Design for Immunosuppressive Tumor Microenvironment Reprogramming: A Critical Review
5 affiliations
- 1Department of Data Science, Duksung Women’s University, Seoul 01369, Republic of Korea
- 2Department of AI-based Drug Development, Duksung Women’s University, Seoul 01369, Republic of Korea
- 3College of Pharmacy, Duksung Women’s University, Seoul 01369, Republic of Korea
- 4Duksung Innovative Drug Center, Duksung Women’s University, Seoul 01369, Republic of Korea
- 5Department of Statistics, Duksung Women’s University, Seoul 01369, Republic of Korea
Abstract
How can cellular interactions in solid tumors guide molecular interventions against immune suppression? This critical review examines how computer-aided drug design (CADD) connects immunosuppressive tumor microenvironment (TME) states to testable molecular hypotheses. We distinguish observational artificial intelligence (AI) predictions from molecular intervention hypotheses and examine the experimental evidence required to evaluate them. We reviewed patient-derived models, spatial and multi-omics profiling, molecular design, simulation, and pharmacology using the literature identified through to 15 October 2026. Adenosine-receptor antagonist discovery and generative protein-binder design illustrated how computational methods contributed to experimentally tested molecular candidates. Native-state structural studies, stromal delivery, and protein degradation broadened the approaches considered. We organized these findings into a proposed framework encompassing in vivo and patient-derived systems, multi-omics mapping, phenotyping of TME states, actionable AI integration, combination-therapy prioritization, and translational testing (IMPACT). A retrospective analysis of a phase III trial illustrated how the framework connects clinical outcomes to questions about mechanism, patient selection, and exposure. The synthesis clarifies the evidence required to connect molecular binding, target engagement, and exposure to patient benefit. IMPACT provides a structure for relating biological context to molecular design and specifying hypotheses for prospective evaluation.
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BibTeX
@article{moon2026computeraided,
title = {{Computer-Aided Drug Design for Immunosuppressive Tumor Microenvironment Reprogramming: A Critical Review}},
author = {Jihoon Moon and Jehyeok Rew and Hyun Kil Shin and Kyoung Song and Hyosun Cho and Joohee Jung and Minjung Kyung},
journal = {International Journal of Molecular Sciences},
year = {2026},
month = oct,
doi = {10.3390/ijms27219766},
url = {https://www.mdpi.com/1422-0067/27/21/9766},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Computer-Aided Drug Design for Immunosuppressive Tumor Microenvironment Reprogramming: A Critical Review
AU - Jihoon Moon
AU - Jehyeok Rew
AU - Hyun Kil Shin
AU - Kyoung Song
AU - Hyosun Cho
AU - Joohee Jung
AU - Minjung Kyung
PY - 2026
DA - 2026/10/30
JO - International Journal of Molecular Sciences
DO - 10.3390/ijms27219766
UR - https://www.mdpi.com/1422-0067/27/21/9766
AB - How can cellular interactions in solid tumors guide molecular interventions against immune suppression? This critical review examines how computer-aided drug design (CADD) connects immunosuppressive tumor microenvironment (TME) states to testable molecular hypotheses. We distinguish observational artificial intelligence (AI) predictions from molecular intervention hypotheses and examine the experimental evidence required to evaluate them. We reviewed patient-derived models, spatial and multi-omics profiling, molecular design, simulation, and pharmacology using the literature identified through to 15 October 2026. Adenosine-receptor antagonist discovery and generative protein-binder design illustrated how computational methods contributed to experimentally tested molecular candidates. Native-state structural studies, stromal delivery, and protein degradation broadened the approaches considered. We organized these findings into a proposed framework encompassing in vivo and patient-derived systems, multi-omics mapping, phenotyping of TME states, actionable AI integration, combination-therapy prioritization, and translational testing (IMPACT). A retrospective analysis of a phase III trial illustrated how the framework connects clinical outcomes to questions about mechanism, patient selection, and exposure. The synthesis clarifies the evidence required to connect molecular binding, target engagement, and exposure to patient benefit. IMPACT provides a structure for relating biological context to molecular design and specifying hypotheses for prospective evaluation.
SN - 1422-0067
ER -