Rank compound libraries with SPRINT protein and ligand co-embeddings. Learn more
Input
Output
Configure inputs to begin
Set options on the left, then click “Submit job”.
What is SPRINT?
SPRINT is a learned protein-ligand screening method that places proteins and compounds in a shared embedding space. It ranks a compound library for one protein target by cosine similarity. Unlike AutoDock Vina or GNINA, it does not generate 3D binding poses or calculate a docking energy.
This implementation runs the published SPRINT ultrafast-embed and ultrafast-topk workflow. It creates an embedding for the target, creates embeddings for the submitted compound library, and returns the compounds with the highest native CosineSimi values.
SPRINT is best used to prioritize compounds for follow-up. Its cosine similarity is a model score, not a calibrated binding probability, binding affinity, or experimental measurement.
How does SPRINT work?
SPRINT maps protein and ligand features into a shared vector space. The published Top K command normalizes those embeddings through cosine similarity and ranks larger values first.
Protein embedding
The selected checkpoint uses SaProt features. You can submit either:
- A plain sequence containing the 20 standard amino acid letters. SPRINT inserts masked structure tokens for this input.
- A paired SaProt sequence that alternates an uppercase amino acid with a lowercase 3Di token or
#mask.
ProteinIQ does not predict a structure or generate 3Di tokens for a plain sequence. If you want the model to use known structural tokens, prepare and submit the paired SaProt representation yourself.
Compound embedding
The checkpoint uses 1024-bit Morgan fingerprints with radius 3. SPRINT projects these fingerprints into the same learned space as the protein representation.
Ranking
For each compound, SPRINT reports:
Higher values rank first. ProteinIQ does not transform this value into a probability.
How to use SPRINT online
Inputs
| Input | Description |
|---|---|
| Protein Sequence | Exactly one plain protein sequence or paired SaProt sequence, up to 1023 effective residues. FASTA and plain text are accepted. A FASTA header names the target-specific output files. |
| Compounds (SMILES) | One compound library. CSV and TSV files require a SMILES header and may include id plus additional columns. Conventional SMI files use SMILES name. Text input may use name<TAB>SMILES or one SMILES per line. |
Text input example:
aspirin CC(=O)Oc1ccccc1C(=O)O
ibuprofen CC(C)Cc1ccc(cc1)C(C)C(=O)O
caffeine Cn1cnc2c1c(=O)n(C)c(=O)n2CCSV input example:
id,SMILES,collection
aspirin,CC(=O)Oc1ccccc1C(=O)O,reference
ethanol,CCO,referenceAdditional CSV or TSV columns are preserved in the ranked output.
Settings
| Setting | Description |
|---|---|
| Return top K compounds | Return 1 to 1000 compounds, default 100. If K exceeds the library size, all compounds are returned. |
Results
The results table preserves the source library columns and adds the native similarity. ProteinIQ adds rank as a display field.
| Column | Description |
|---|---|
rank | Display rank, starting at 1 for the highest similarity |
id | Submitted identifier or an automatically generated identifier |
SMILES | Submitted compound structure |
CosineSimi | Native SPRINT cosine similarity, sorted from highest to lowest |
| Other columns | Any additional columns from a submitted CSV or TSV library |
Downloadable files include:
topk_mol_data_<query_id>.csv, the native ranked compound tabletopk_mol_embeddings_<query_id>.npy, embeddings for the returned compoundssprint_target_embeddings.npy, the target embeddingsprint_library_embeddings.npy, embeddings for the full submitted librarysprint_run.log, command output for reproducibility and troubleshootingsprint_provenance.json, pinned source, model, and runtime identifiers
Interpreting scores
Use CosineSimi for relative ranking within a run. A larger value means the model placed that compound closer to the target in its learned embedding space. The score has no universal probability threshold and should not be interpreted as a dissociation constant, inhibition constant, or docking energy.
Screening workflow
One practical sequence is:
- Filter compounds for drug-likeness using Lipinski's Rule of 5 or Molecular Descriptors
- Run SPRINT to prioritize the library by cosine similarity
- Assess top hits for toxicity and ADMET properties with ADMET-AI or eToxPred
- Validate promising candidates with structure-based docking (DiffDock, AutoDock Vina, or GNINA)
The published Morgan featurizer substitutes a zero fingerprint when RDKit cannot parse a SMILES value. Such rows may still receive a model score. Check the run log and remove invalid structures before interpreting or advancing hits.
Limitations
- SPRINT ranks learned interaction similarity. It does not return calibrated probabilities, binding affinities, poses, residue attention maps, or binding sites.
- Plain protein sequences use masked structure tokens, so they do not contain the same structural information as prepared amino-acid/3Di pairs.
- Performance can decrease for protein families or chemical scaffolds unlike the training data.
- A single embedding cannot represent every conformational state, binding site, protonation state, tautomer, or stereochemical uncertainty.
- Follow up high-ranking compounds with orthogonal computational methods and experimental assays.
Related tools
AF2BIND
AF2BIND predicts ligand-binding residues from a protein structure using AlphaFold2 pair representations and a 20-residue bait sequence.
SMRTnet
Deep learning framework for predicting small molecule-RNA interactions using RNA secondary structure. Combines language models, CNNs, and graph attention networks for binding prediction.
ADMET-AI
Predict ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties from SMILES strings using machine learning models trained on Therapeutics Data Commons datasets.
Admetica
Predict 22 ADMET properties from SMILES strings with the native Admetica Chemprop models from Datagrok.
Brenk filter
Identify toxic, reactive, and pharmacokinetically problematic molecular fragments using structural alert patterns
eToxPred
Predict toxicity and synthetic accessibility of small molecules using machine learning. eToxPred combines toxicity risk assessment with synthetic accessibility scoring to help prioritize drug candidates.
Lead-likeness filter
Screen for lead-like compounds using stricter molecular descriptor criteria than Lipinski or Veber rules for early-stage drug discovery
PAINS filter
Screen compounds for Pan-Assay Interference patterns that cause false positives in biological assays
QEPPI
Quantitative estimate for protein-protein interaction inhibitor potential. Evaluates drug-likeness for compounds targeting PPIs.
Structural alert screening
Screen compounds for structural toxicity alerts using PAINS, Brenk, and NIH filters. For focused screening, see PAINS Filter, Brenk Filter, or Veber's Rule.