TopU-LBVS: A Realistic Multi Target Benchmark for Ligand Based Virtual Screening
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TL;DR - TopU-LBVS is a 93-target benchmark for ligand-based virtual screening that replaces easy random negatives with property-matched, structurally similar decoys. It aims to provide a more realistic and reproducible assessment of molecular models for early-stage drug discovery.
- Built from curated ChEMBL 35 data, covering seven protein classes with a fixed 1:40 active-to-decoy ratio.
- Includes full generalization, low-data learning, and compact seven-target protocols with fixed splits and standardized evaluation.
- Tests ten baselines spanning fingerprints, molecular GNNs, hybrid approaches, and modern molecular models.
- Baseline performance drops sharply when moving from random decoys to hard negatives, highlighting overestimation by conventional evaluations.
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TopU-LBVS: A Realistic Multi Target Benchmark for Ligand Based Virtual Screening
Public signals
N/A
TL;DR - TopU-LBVS is a 93-target benchmark for ligand-based virtual screening that replaces easy random negatives with property-matched, structurally similar decoys. It aims to provide a more realistic and reproducible assessment of molecular models for early-stage drug discovery.
- Built from curated ChEMBL 35 data, covering seven protein classes with a fixed 1:40 active-to-decoy ratio.
- Includes full generalization, low-data learning, and compact seven-target protocols with fixed splits and standardized evaluation.
- Tests ten baselines spanning fingerprints, molecular GNNs, hybrid approaches, and modern molecular models.
- Baseline performance drops sharply when moving from random decoys to hard negatives, highlighting overestimation by conventional evaluations.