AI-native computational drug discovery

We design drug candidates for the targets others can't.

We design small-molecule candidates computationally and put them through independent validation gates — then take what survives to experimental confirmation with partners.

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Computational by design

Designed by computation

Candidates are designed and filtered in silico, so only what survives is worth the cost of a bench.

Validated, not asserted

Multi-layer discipline

Independent, orthogonal methods remove false positives at every stage.

Built for hard targets

A way around the wall

When one approach is blocked, we re-route — toward targets others call undruggable.

Why we exist

When a target fights back, we re-route.

Drug discovery is slow and expensive, and it stalls most often on the targets that matter most — the hard ones, the ones called undruggable. Many teams have a good target but no clear path to a molecule.

We route around that bottleneck with computational design and multi-layer validation. When one approach is blocked, we find another path — inhibition gives way to degradation, one chemistry to another — where conventional discovery stops.

We do not screen millions by brute force. We aim, carefully, at a small number of well-understood candidates. Precision is the strategy.

Inside the binding site

A docking pose is a single moment. We simulate what happens next — because binding that does not hold is not binding at all.

The platform

One pipeline, from a target to a validated candidate.

Our proprietary platform runs a single, sequential pipeline: generate novel molecules, filter them through independent computational gates, and accumulate what we learn. Each stage is built to remove the false positives the previous stage could not see.

01 — Generate

Create

Novel molecules designed directly in the target's binding site — not screened from known libraries.

02 — Cross-validate

Confirm

Independent, orthogonal methods at every stage, so one signal never decides.

03 — Accumulate

Compound

Every result — including negative ones — feeds the platform and sharpens the next design.

01Generative design Create novel candidate molecules for the target.
02AI binding prediction Rapidly triage promising binders from the rest.
03Structure-based docking Predict how a molecule sits in the binding site.
04Molecular dynamics Test whether the binding holds over time.
05Free-energy methods Quantify binding strength on a physical basis.
06ADMET & drug-likeness Evaluate absorption, metabolism, safety, and more.
07Synthesizability Confirm a real, viable route to make it.
08Novelty screening Check it is distinct enough to protect.
→ Validated candidate A molecule that has passed every independent gate.

Validation discipline

We trust no single signal.

A good docking score is not binding. Binding is not potency. Each layer must be independently confirmed — and this discipline is what separates a real candidate from a computational artifact.

Principle 01

Distrust single metrics

No one score decides. Binding prediction, dynamic stability, and free energy each say something different — and any of them can reverse the verdict.

Principle 02

Independent cross-validation

We confirm with methods built on different principles. Synthesizability is checked several ways; free energy is computed by two mathematical routes at once.

Principle 03

Prediction meets measurement

We compare predictions against real measurements and correct the next prediction with the gap. The platform grows more accurate the more it is used.

The real asset is not any single molecule. A candidate list ages; the ability to design better molecules — and the discipline to validate them — does not. Every project deepens the platform.

What the gates actually do

Three million molecules in. Nineteen out.

A recent multi-target campaign in neurodegeneration, stage by stage.

Structure-based generation 3,000,000
Passed novelty filter 424,387
Passed property & CNS suitability 34,847
Docked against all three targets 34,755
Strong binding at every target 1,645
Completed AI binding prediction 1,644
Top tier, carried forward to extended dynamics 19

Every stage is an independent gate, and each one uses a different principle from the last. The overall pass rate is 0.0006%. We treat that number as a measure of rigour, not a weakness — a pipeline that passes most of what enters it is not filtering anything.

We have found errors in our own pipeline, withdrawn the conclusions that rested on them, and recomputed. A validation system working does not mean errors are absent. It means errors are found.

Targeted protein degradation

It is a geometry problem, not a binding problem.

Inhibitors block a protein's function. Degraders remove the protein. That difference bypasses the feedback activation which limits inhibitors, and it is less vulnerable to resistance driven by target overexpression — and because degraders act catalytically, activity does not require sustained high occupancy.

The trade-off is difficulty. An inhibitor needs to bind one site. A degrader must hold the target and an E3 ligase at the same time, and the resulting ternary complex has to adopt a geometry that actually permits ubiquitin transfer. Lumina Bio has concentrated on treating that geometry problem computationally.

PROTAC — current focus Molecular glue — extension Antibody-based — out of scope
Ternary complex predictionDeep-learning co-folding of the bound complex.
Stability under dynamicsExtended simulation of the predicted structure.
Benchmarked against cryo-EMMeasured against published active-state complexes.
Exhaustive lysine scanUbiquitination sites mapped, not assumed.
Independent cross-checkConfirmed by a method built on other principles.
An honest boundary

We report these as geometric feasibility assessments, not verification. Computation evaluates whether a productive arrangement is possible; actual degradation efficiency is established experimentally, and we do not describe one as the other.

Services

Where our partners come in.

We develop our own pipeline — and we put the platform to work for other teams. Partners enter at different points; we meet them there.

Entry 01

Strong chemistry, no in-silico

You have an excellent medicinal chemistry team but no computational design capability. We design candidates for your target; you synthesize.

Entry 02

Capability, no compute

You have molecules but not the infrastructure to run large-scale simulation and precise calculation. We validate them on our own GPU pipeline.

Entry 03

Biology, meet computation

We connect target biology and molecular simulation in one integrated pipeline.

In-silico DesignNovel candidates designed for your target.
Multi-stage ValidationYour molecules, put through the full gauntlet.
Synthesis CoordinationSynthetic routes and external CRO synthesis, coordinated.
Hard-Target StrategyWorkaround design for targets that resist.
Milestone PartnershipMilestone-based, with joint-IP options.
Advisory RetainerOngoing in-silico support, month to month.
Membrane SimulationBilayer construction through equilibration, for membrane-anchored targets.
Your data

On-premise, private

We run our own on-premise GPU infrastructure — so your project is never bottlenecked by rented compute, and research data does not transit external cloud services at any point.

Your IP

Your target, your terms

Rights to what a commissioned campaign discovers belong to you. The platform, its methodology and its know-how remain ours. We set this out explicitly in contract, work under NDA, and never repurpose your target for anyone else.

No risk to start

Start with a free pilot.

New to us? We take your target and deliver an initial in-silico design pass at no cost. If you like it, we scope a paid engagement. Your structures stay yours — always under NDA.

Request a pilot

Pipeline

What the platform has produced.

These are in-silico validated assets — evidence that the platform carries a candidate all the way through. They are not clinical-stage programs; wet-lab validation lies ahead.

Program Disease area Stage
Program A Neurodegeneration In-silico validation complete — preparing patent filing
Program B Neurodegeneration (multi-target) In-silico validation ongoing
Program C Oncology In-silico validation ongoing

Stages: Design → Multi-stage validation → Free-energy → Synthesis → Binding assay → in-vivo PoC → Partnering

Shown as evidence of capability, not as approved or clinical-stage medicines. Targets, structures and figures are deliberately withheld until patent filings are in place — we will expand this table as they are.

Company

Built from the ground up, for precision.

We believe the next generation of medicines can be designed — computationally, rigorously, and for the targets the world has given up on. We build that capability, and prove it one validated molecule at a time.

Lumina Bio was founded by Jae Yong Choi, who built the platform and the on-premise infrastructure behind it himself — from the generative design stage through every validation gate that follows it.

CompanyLumina Bio Co., Ltd.
CEOChoi, Jae Yong
Location204 Munhwa-ro, Naju-si, Jeollanam-do, Republic of Korea
Business registration223-88-03724
FocusIn-silico small-molecule drug discovery

Contact

Have a hard target? Let's take a first pass.

For partnership, licensing, and scientific collaboration. Detailed compound data is exchanged only under NDA, and we reply within business days.

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Inquiries inquiry@luminabio.net
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