KIBA

The KIBA dataset is a benchmark that contains drug-target affinity data used to evaluate the performance of predictive models in drug discovery.

Papers using KIBA (12)

Structure-free drug–target affinity prediction using protein and molecule language models2026 · 3 citesHiF-DTA: Hierarchical Feature Learning Network for Drug-Target Affinity Prediction2025MSCoD: An Enhanced Bayesian Updating Framework with Multi-Scale Information Bottleneck and Cooperative Attention for Structure-Based Drug Design2025DrugMCTS: a drug repurposing framework combining multi-agent, RAG and Monte Carlo Tree Search2025Conditional Chemical Language Models are Versatile Tools in Drug Discovery2025Conformal Prediction for Uncertainty Estimation in Drug-Target Interaction Prediction2025HCAF-DTA: drug-target binding affinity prediction with cross-attention fused hypergraph neural networks2025ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion2024 · 7 citesDrug-target affinity prediction method based on consistent expression of heterogeneous data2022 · 2 citesHGTDP-DTA: Hybrid Graph-Transformer with Dynamic Prompt for Drug-Target Binding Affinity Prediction2024 · 1 citesGraphPrint: Extracting Features from 3D Protein Structure for Drug Target Affinity Prediction2024 · 1 citesGraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity prediction2023