Quantitative Computational Validation of Nanoscale Interactions between Drug Molecules and Diabetic Wound-Related Proteins for Drug Discovery

Diabetic wounds remain challenging due to complex nanoscale
dysregulation
under hyperglycemic conditions. A computational nanomedicine pipeline
that coupled large language model (LLM)-powered literature mining
for qualitative insights with multistage molecular simulations for
quantitative validation was presented to investigate drug–protein
nanointeractions. The pipeline first mapped 2989 existing drugs against
8739 diabetic wound-related protein targets and utilized an LLM-based
analysis to qualitatively evaluate each drug–protein regulatory
effect from literature evidence. A cheminformatic clustering with
greedy coverage then distilled this vast search space down to 35 candidate
drugs and 50 key proteins. These candidates were subsequently subjected
to sequential molecular docking, molecular dynamics (MD), and quantum
chemistry (QC) simulations to quantify their nanoscale binding interactions.
By combining the AI-derived regulatory insights with physics-based
binding metrics, the pipeline ranked all candidates using a composite
(anti)therapeutic score. Folic acid emerged as the top candidate,
consistent with pro-regenerative effects reported in the literature
and exhibiting a strong interaction energy to fibroblast growth factor
(ΔEinteraction= −78.1 kcal/mol) in simulations. In vitro scratch wound assays confirmed that folic acid
accelerated wound closure to 134.90% of the untreated control (p < 0.001), in agreement with the in silico predictions. Overall, this integrated AI-guided nanoscale modeling
approach shortened the literature-to-experiment cycle by over 70%
compared to conventional methods and demonstrated a translational
strategy that bridges nanoscale molecular interactions with therapeutic
outcomes. These findings exemplify how combining AI-driven literature
mining with quantitative nanoscale modeling can accelerate drug repurposing
for diabetic wound care and other complex diseases.