DAVIS

The 'Davis' dataset is a benchmark used to evaluate drug-target affinity prediction models, containing data on the binding affinities between various drugs and their target proteins.

Papers using DAVIS (11)

Structure-free drug–target affinity prediction using protein and molecule language models2026 · 3 citesLaPro-DTA: Latent Dual-View Drug Representations and Salient Protein Feature Extraction for Generalizable Drug--Target Affinity Prediction2026Towards Precision Protein-Ligand Affinity Prediction Benchmark: A Complete and Modification-Aware DAVIS Dataset2025HiF-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 Design2025Conditional Chemical Language Models are Versatile Tools in Drug Discovery2025HCAF-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 citesGraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity prediction2023