NetCal-DTI: A Network Topology-Calibrated Hybrid Framework for High-Precision Inductive Drug–Target Interaction Prediction

All datasetsin benchmark test are available at our GitHub repository (https://github.com/JuFanbo/NetCal-DTI).We obtained DrugBank V6.0 and BIOSNAP from https://go.drugbank.com/releases/latestand https://snap.stanford.edu/biodata/, respectively. The subset of BindingDB with Kdvalues can be downloaded from https://www.kaggle.com/datasets/christang0002/bindingdb-for-dta. Thesingle-cell transcriptomic data for Tucidinostat-treated samples were retrieved from the public dataset GSE301188: https://www.ncbi.xyz/geo/query/acc.cgi?acc=GSE301188.
Results from 5 independent runs of our ablation study on DrugBank, BIOSNAP and BindingDB are provided in Supplementary Data 1. Details for LEsimilarity heatmap are provided in Supplementary Data 2. Prediction Score Comparison Between Calibrated Model and Purely Inductive Baseline on a test set are provided in Supplementary Data 3. Top100 and Last100 docking results of NetCal-DTI predictions on CDK2 and SERT are provided in Supplementary Data 4. Target screening results for Tucidinostat on 100 runs are provided in Supplementary Data 5.