Predict ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties from SMILES strings using machine learning models trained on Therapeutics Data Commons datasets.
Predict 22 ADMET properties from SMILES strings with the native Admetica Chemprop models from Datagrok.
AF2BIND predicts ligand-binding residues from a protein structure using AlphaFold2 pair representations and a 20-residue bait sequence.
Identify toxic, reactive, and pharmacokinetically problematic molecular fragments using structural alert patterns
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.
Screen for lead-like compounds using stricter molecular descriptor criteria than Lipinski or Veber rules for early-stage drug discovery
Predict protein solubility and usability for E. coli expression using ESM protein language models
Screen compounds for Pan-Assay Interference patterns that cause false positives in biological assays
Quantitative estimate for protein-protein interaction inhibitor potential. Evaluates drug-likeness for compounds targeting PPIs.
Rank a compound library against one protein target with SPRINT protein and ligand co-embeddings and native cosine similarity.
Predict ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties from SMILES strings using machine learning models trained on Therapeutics Data Commons datasets.
Predict 22 ADMET properties from SMILES strings with the native Admetica Chemprop models from Datagrok.
AF2BIND predicts ligand-binding residues from a protein structure using AlphaFold2 pair representations and a 20-residue bait sequence.
Identify toxic, reactive, and pharmacokinetically problematic molecular fragments using structural alert patterns
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.
Screen for lead-like compounds using stricter molecular descriptor criteria than Lipinski or Veber rules for early-stage drug discovery
Predict protein solubility and usability for E. coli expression using ESM protein language models
Screen compounds for Pan-Assay Interference patterns that cause false positives in biological assays
Quantitative estimate for protein-protein interaction inhibitor potential. Evaluates drug-likeness for compounds targeting PPIs.
Rank a compound library against one protein target with SPRINT protein and ligand co-embeddings and native cosine similarity.
Configure inputs to begin
Set options on the left, then click “Submit job” — or start from an example.
Alcohol dehydrogenase with ethanol
Specific enzyme-substrate pairs
DLKcat predicts enzyme turnover numbers (kcat values) from protein sequences and substrate structures. The turnover number represents how many substrate molecules an enzyme can convert to product per second under saturating conditions—a fundamental kinetic parameter for understanding enzyme efficiency.
Developed at Chalmers University of Technology and published in Nature Catalysis (2022), DLKcat combines a convolutional neural network (CNN) for processing protein sequences with a graph neural network (GNN) for analyzing substrate molecular structures. This dual-network architecture allows the model to learn patterns in enzyme-substrate interactions that correlate with catalytic rates.
The model was trained on over 16,000 experimentally measured kcat values from the BRENDA and SABIO-RK enzyme databases, covering both wild-type and engineered enzymes across diverse species.
DLKcat processes enzyme-substrate pairs through two parallel neural networks:
The outputs from both networks are concatenated and passed through fully connected layers to predict log2(kcat). Training used radius-2 substrate subgraphs and 20-dimensional vector embeddings.
ProteinIQ runs the pinned DLKcat 1.0.0 prediction program on managed compute without requiring a local installation.
| Input | Description |
|---|---|
Enzyme sequence(s) | FASTA format protein sequences, up to 2,500 residues each. Up to 10 enzymes are supported per job. |
Substrate(s) | SMILES strings or compound names, one per line, with up to 10 substrates per job. Every enzyme is paired with every substrate. |
For specific enzyme-substrate pairs, switch to paired TSV input and provide one row per pair:
| Column | Description |
|---|---|
Substrate Name | Compound name used for labeling and PubChem lookup when SMILES is blank |
Substrate SMILES | Substrate SMILES, recommended for reliable predictions |
Protein Sequence | Enzyme amino acid sequence |
| Setting | Description |
|---|---|
Input mode | Choose separate enzyme/substrate inputs for all combinations, or paired TSV rows for specific enzyme-substrate pairs. |
The output table contains predicted kcat values for each enzyme-substrate pair:
| Column | Description |
|---|---|
Substrate Name | The substrate label |
Substrate SMILES | The substrate structure used for prediction |
Protein Sequence | The full input protein sequence |
Kcat value (1/s) | Turnover number in reactions per second |
Error | Why a row has no prediction, when applicable |
Each job is limited to 100 enzyme-substrate pairs and 250,000 aggregate residue-pairs. Pricing is one credit per 10 prediction pairs, with a one-credit minimum. The downloadable files include output.tsv, the source run log, and a provenance record with the exact source commit and model checksums.
DLKcat predicts log2(kcat), then converts to linear scale. Predictions span a wide range:
| kcat (1/s) | Interpretation |
|---|---|
| > 1000 | Fast enzyme, typical of metabolic enzymes |
| 100–1000 | Moderate catalytic rate |
| 10–100 | Slow enzyme |
| < 10 | Very slow, may indicate poor substrate match |
The publication reports Pearson's r ≈ 0.71 and RMSE 1.06 on held-out test data. Performance is best for enzymes similar to the training set—predictions become less reliable as sequence similarity to training enzymes decreases.
DLKcat has important constraints to consider:
Sequence similarity dependence: The model performs well when test enzymes share >70% sequence identity with training examples. For dissimilar enzymes, predictions may not outperform simple kcat averages.
No environmental factors: DLKcat was not trained with temperature or pH condition data, so predictions are based on enzyme sequence and substrate structure.
Mutant enzymes: While trained on some mutant data, predictions for novel mutations—especially those affecting catalytic residues—should be validated experimentally.
Substrate coverage: Predictions are most reliable for substrates similar to those in the BRENDA/SABIO-RK training data.
Single-fragment substrates: The published prediction program does not score disconnected multi-fragment SMILES such as salts. Those rows are retained in the result table with a None kcat value and an explanatory error.
Compound-name lookup: Substrate names are resolved through PubChem before prediction. If no SMILES can be resolved, the row is retained with a None kcat value.
Enzyme turnover predictions support several research applications:
