Network Pharmacology, Machine Learning, and Molecular Docking Analysis of Changluo Tiaogan Dinghun Decoction for Coronary Heart Disease with Anxiety

Coronary heart disease (CHD) and anxiety disorder frequently coexist, highlighting the need to investigate their potentially shared molecular mechanisms. Conventional therapeutic approaches have limitations, while traditional Chinese medicine (TCM) offers a potential multitarget strategy. Changluo Tiaogan Dinghun decoction (CLTGDHD) was developed for qi stagnation and blood stasis. This computational study integrated network pharmacology, machine learning, and molecular docking to investigate the potential compounds and molecular targets of CLTGDHD relevant to CHD with anxiety disorder. Gene Expression Omnibus (GEO) datasets GSE100927 and GSE61672 were analyzed separately to identify disease-associated genes. GSE100927 comprises peripheral-artery atherosclerosis samples and was used as a surrogate for atherosclerosis-related processes relevant to CHD, whereas GSE61672 represents an anxiety disorder cohort. The resulting candidate genes were evaluated using a machine-learning prioritization workflow, which identified protein tyrosine phosphatase receptor type C (PTPRC) as the sole retained gene. Molecular docking using the AlphaFold-predicted PTPRC structure (AF-P08575-4-F1) yielded predicted binding affinities ranging from -5.985 to -6.827 kcal/mol for five selected compounds. These findings suggest potential molecular interactions relevant to the investigated disease processes but do not establish therapeutic efficacy or a CHD-specific mechanism. Because the transcriptomic datasets represent separate populations rather than a clinically defined cohort with both CHD and anxiety disorder, the findings remain exploratory and hypothesis-generating. Further validation using appropriately defined clinical datasets and experimental models is required.