ml-jku/clamp
Code for the paper Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language
Implements contrastive pre-training on molecule-bioassay pairs using CLIP-based assay encodings and Morgan fingerprints, enabling zero-shot activity prediction from natural language bioassay descriptions. Provides pretrained models (including Frequent Hitter baseline) and supports downstream evaluation via linear probing on MoleculeNet benchmarks. Includes utilities for dataset preprocessing (FS-Mol, PubChem with 500k+ assays) and assay description augmentation.
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Python
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Last pushed
Feb 26, 2026
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