Benchmarks

Mutation effect predictionQuiet

ProteinGym mutation effects

Predict the fitness effects of mutations across 217 deep mutational scans (placeholder)

1. #2assay-calibration0.4733
2. #3msa-coevolution0.4689

Breast MRI lesion classificationQuiet

ODELIA breast MRI

Lesion classification on multi-centre breast MRI, over seven cross-validation folds (placeholder)

1. #2focal-reweight0.893
2. #3multiseq-attention0.889

Predict human plasma protein binding rate (%) from a SMILES string

Plasma protein binding rate from structure

Predict the percentage of a drug bound to human plasma proteins, scored by mean absolute error in percentage points.

Predict lipophilicity (log-ratio) from a SMILES string

Lipophilicity from structure

Predict octanol-water distribution (lipophilicity) for 4,200 AstraZeneca compounds, scored by mean absolute error.

Classify human intestinal absorption from a SMILES string

Human intestinal absorption from structure

Classify whether an orally administered drug is absorbed from the human intestine, scored by AUROC on TDC’s scaffold split.

Classify hERG channel blockade from a SMILES string

hERG channel blockade from structure

Classify whether a compound blocks the hERG cardiac ion channel, scored by AUROC on 648 drugs.

Predict human drug half-life from a SMILES string

Drug half-life from structure

Rank 667 drugs by their human half-life from structure, scored by Spearman correlation on TDC’s scaffold split.

Classify drug-induced liver injury from a SMILES string

Drug-induced liver injury from structure

Classify whether a drug can cause liver injury, scored by AUROC on an FDA-derived set of 475 drugs.

Classify CYP3A4 inhibition from a SMILES string

CYP3A4 inhibition from structure

Classify whether a compound inhibits the CYP3A4 enzyme, scored by AUPRC over 12,328 compounds.

Predict Caco-2 cell effective permeability from a SMILES string

Caco-2 cell permeability from structure

Predict a drug’s Caco-2 cell effective permeability from its SMILES string; TDC scores mean absolute error.

Classify blood-brain barrier penetration from a SMILES string

Blood-brain barrier penetration from structure

Classify whether a compound crosses the blood-brain barrier, scored by AUROC on TDC’s scaffold split of 1,975 drugs.

Predict aqueous solubility (log mol/L) from a SMILES string

Aqueous solubility from structure

Predict aqueous solubility in log mol/L for 9,982 compounds from AqSolDB, scored by mean absolute error.

Classify Ames mutagenicity from a SMILES string

Ames mutagenicity from structure

Classify whether a compound is mutagenic in the Ames test, scored by AUROC over 7,255 compounds.

Predict rat acute oral toxicity LD50 from a SMILES string

Acute oral toxicity (LD50) from structure

Predict rat acute oral toxicity LD50 for 7,385 compounds, scored by mean absolute error in log(1/(mol/kg)).

Score genes by how spatially variable their expression is

Detecting spatially variable genes

Rank genes by spatial variability in 50 simulated spatial transcriptomics datasets where the true variability is known.

Simulate realistic spatial transcriptomics data

Realistic simulators for spatial transcriptomics

Simulate spatial transcriptomics data that match ten real datasets on 23 distributional and downstream-analysis metrics.

Estimate per-spot cell-type proportions from a single-cell reference

Cell-type decomposition of spatial transcriptomics spots

Estimate the cell-type proportions in each spot of simulated spatial transcriptomics data, given a single-cell reference.

Predict one modality's profile from another in held-out cells

Predicting one single-cell modality from another

Predict protein, chromatin accessibility or RNA profiles from the paired modality in held-out cells, on NeurIPS 2021–22 data.

Predict differential expression for held-out compound–cell type pairs

Predicting drug responses across blood cell types

Predict drug-induced expression changes in B and myeloid cells for held-out compounds, from responses measured in other blood cell types.

Project cell-type labels from labelled batches onto a held-out batch

Cell-type label projection from a reference atlas

Train a cell-type classifier on labelled batches and annotate a held-out batch, across six CELLxGENE atlases.

Reduce scRNA-seq data to a 2D embedding for visualisation

Two-dimensional embeddings for single-cell visualisation

Embed single-cell expression data in two dimensions while preserving neighbourhoods, distances and densities.

Denoise scRNA-seq count matrices

Denoising single-cell RNA-seq counts

Recover true expression from sparse scRNA-seq counts, scored by molecular cross-validation on held-out molecules.

Prioritise ligand–target cell type interactions

Inferring cell-cell communication from single-cell data

Rank ligand–target cell type interactions in a breast cancer atlas against a ground truth inferred from cytokine activity.

Integrate scRNA-seq batches while conserving biological variation

Batch integration of single-cell atlases

Remove batch effects from six single-cell atlases while keeping cell-type variation, scored on 13 metrics against control methods.

Design sequences that fold into the 100 Eterna100-V2 target structures

Eterna100-V2: designing RNA sequences for 100 target folds

Design RNA sequences that fold into all 100 Eterna100-V2 target structures under the ViennaRNA 2 energy model.