Healthcare & life sciences
Mechanism-of-action prediction for drug discovery
Multi-label models over gene-expression signatures that shortlist how a compound acts — narrowing wet-lab work before it is paid for.
This page describes a problem class and the architecture we deploy for it, written against a named public benchmark — the open dataset or competition where the world's data scientists have tested approaches to this exact problem against a hard metric. It is not a client engagement, and the benchmark's results, prizes and rankings belong to its host and participants, not to us. Client work is confidential and is only ever published with written permission.
The problem
What it costs when this goes unsolved.
Identifying which biological mechanisms a compound acts through is a bottleneck in target-based discovery and repurposing. Every mechanism hypothesis that reaches the lab costs assay time and budget; a model that reliably shortlists mechanisms from cellular response signatures moves that spend from confirming guesses to confirming candidates.
The data is high-dimensional cellular response — hundreds of gene-expression and viability readouts per treated sample, across doses and timepoints — against 200+ possible mechanism labels, most of them rare. Multi-label imbalance this severe breaks naive cross-validation, and models that ignore the dose/timepoint structure learn batch effects instead of biology.
How we build it
The architecture, stage by stage.
01
Multi-label architecture, not 200 binary models
Deep tabular ensembles — residual networks and attention-based architectures — that share representation across mechanisms, which is where the lift over independent classifiers comes from.
02
Label smoothing and transfer
Training regularised with label smoothing and transfer from auxiliary targets, the published recipe for rare-label stability on this problem class.
03
CV design that respects the biology
Multi-label stratification with drug-level grouping, so validation measures generalisation to new compounds rather than memorisation of replicates.
04
Ranked hypotheses, not verdicts
Output is a calibrated shortlist per compound for a discovery scientist to take to the bench — the model proposes, the assay disposes.
Model families on this problem class: Deep multi-label ensembles · TabNet / residual tabular nets · Multi-label CV design · Omics pipelines.
What you get
What an engagement hands over.
Everything below goes in the scope document before you sign it, with a fixed price or a rate with a ceiling — the same terms as every other engagement in the catalogue.
- A mechanism-prediction model over your screening platform's readouts
- Calibrated per-compound mechanism shortlists with confidence reporting
- A validation design your computational biology team can defend in review
- Pipeline integration with your compound-registration and assay systems
Provenance
The benchmark behind this page.
Mechanisms of Action (MoA) Prediction, run by Harvard LISH, Broad Institute Connectivity Map and NIH LINCS, is the public proving ground for this problem class. The figures below are the host's, cited as context for how seriously this problem is tested in the open — they are not our results and we do not claim them.
How to buy this
The services this build draws on.
More proof
Next step
Thirty minutes on whether this fits your problem.
Bring the constraint — the regulator, the data boundary, the latency budget. If your data cannot support this build yet, the call will conclude with what to fix first, not with a proposal.