Score the entire known therapeutic space — approved drugs, clinical-stage compounds, literature analogs — against the target conformation and patient or program context. Days, not months.
When the known space falls short, propose minimal-modification analogs of the closest scaffold — predicted profile, synthesis route, IP positioning. Hand-off ready for experimental validation.
When no precedent fits, generate novel matter against the target — virtual screen, generative chemistry, predicted ADMET and synthesizability — fed into the same hand-off package downstream engagements consume.
A putative target arrives with a thesis. We return a validation package — druggability, disease linkage, failure history, competitive set, and IP terrain — structured so the program can decide to commit, refocus, or kill before chemistry spend begins.
A discovery team has a target proposed by a biology group, an academic collaborator, or an in-licensing pitch — and the chemistry investment is large enough that the team wants an outside, structured validation read before the chemistry budget moves.
The biology group is pushing this kinase as our next program. Before we put a chemistry team on it for nine months, is the target druggable, is the disease linkage real, and are we second-, fifth-, or fifteenth-in-class on the public record?
A validated target needs hit matter. We run a computational hit-finding campaign — virtual screening, generative chemistry, and pharmacophore-driven similarity searches — and return a triaged hit list ready for synthesis and experimental validation through your CRO of choice.
A program has a validated target but no internal med-chem capacity, a hit list that's too thin, or a screening campaign that returned scaffolds the team wants to expand computationally before committing more screening spend.
Our DEL screen returned three scaffolds with sub-micromolar binding but ugly ADMET predictions. Can you expand each scaffold computationally, triage to a fifty-compound short-list with cleaner predicted profiles, and prepare the sourcing brief so our CRO can synthesise next month?
A scaffold is in hand and the program is grinding through SAR. We deliver a structured optimisation plan — structure-activity analysis from existing data, computational suggestions across the four developability axes, and a ranked analog list to test next.
A med-chem program is mid-optimisation, the easy moves have been made, and the team needs a structured outside read before committing the next twelve weeks of synthesis to a particular vector — or wants a competing computational hypothesis tested against the in-house plan.
We've made forty analogs and we're stuck at high single-digit nanomolar potency with a CYP3A4 problem we can't engineer out. Show us where the binding mode actually constrains us, propose ten analogs that resolve the CYP issue without giving back potency, and tell us which three to make first.
A discovery asset is on the table for in-licensing, partnership, or acquisition. We deliver an independent computational re-read of the asset's claimed activity, target rationale, developability profile, and competitive position — written to be defensible in a deal room.
A BD team or investment committee is being asked to take a position on someone else's discovery asset, and the seller's deck is the only computational read on the table. Brioche provides the second read — structured, sourced, and independent of the deal counterparty.
This seed-stage company is pitching us a pre-IND small molecule with a clean safety pharm package and aggressive potency claims. Re-validate the chemistry independently, tell us where the IP is thin, and price the three risks we'd carry if we in-licensed the asset at the proposed terms.
The proposed target is a serine/threonine kinase with high-resolution apo and ligand-bound structures deposited in the Protein Data Bank.[1] The OpenTargets association score for the indication of interest is mid-band, driven primarily by transcriptomic evidence rather than human genetics or rare-variant signal.[2] The Sponsor's working assumption, as captured in the kickoff brief, is that the structural tractability and the OpenTargets score together carry the validation burden. That assumption is partially, but not fully, supported by the evidence base.
On structural tractability, the read is unambiguous. The kinase has an ATP-competitive pocket with the canonical hinge geometry, multiple disclosed type-I and type-II inhibitor classes against orthologue or paralogue proteins in ChEMBL,[3] and at least one resolved allosteric site visible in the apo structures.[1] Hit rates for the chemotype family are in the expected band for kinase virtual screens; the structural argument carries.
On disease linkage, the picture is thinner. The GWAS Catalog returns no genome-wide-significant association between coding variants in this kinase and the indication of interest at p < 5×10⁻⁸.[4] The functional evidence in OpenTargets is dominated by bulk-tissue transcriptomic upregulation in disease vs. control comparisons — informative, but not causal. The mouse model evidence is mixed: two independent perturbation studies show partial rescue, one shows no effect, and the heterogeneity has not been resolved in the published record.
Two specific risks follow. First, causality: transcriptomic association does not establish that pharmacological inhibition will reverse the phenotype, and the mouse model heterogeneity is consistent with the kinase being a marker rather than a driver in a meaningful patient subset. Second, patient selection: if the underlying biology is heterogeneous, a Phase-2 readout against an unstratified population is at material risk of failure even with a clean potency and ADMET profile.
Our recommendation, captured in §2.4, is to gate the chemistry decision on a single human-tissue functional experiment that the program can run within the validation window, rather than treating the OpenTargets score as the validation. The competitive-density section (§3) makes the case that this resolution is also a moat-creating exercise, not just a risk-management one.
Honesty matters here. Brioche is a research practice in computational discovery, not a wet-lab operation. Synthesis, in-vitro pharmacology, ADME profiling, in-vivo studies, and analytical characterisation are coordinated through CRO partners — either yours or, where you'd prefer, partners we've worked with before. We do not run a lab and we do not pretend to.
Every Discovery Engine engagement holds to the same evidentiary discipline as Intelligence Studio — the same one a med-chem reviewer, a regulator, or an investment committee would expect. The five notes below describe the discipline and where it ends.
New engagements begin with a 90-minute scoping call. We don't ask you to fill in a form — we ask you to describe the program, the bottleneck, and the decision in front of you. We send back a one-page brief with the question framed in writing, the deliverable shape, and the hand-off path.