OUR APPROACH
Technology.
Connecting molecular design with physical simulation.
From a research question to candidate comparisons and experimental planning. Explore our process, computational methods and areas of work.
Platform§ 01From a hypothesis to a testable plan
Explore broadly.
Test what matters first.
We bring structure, molecular physics, AI and developability into the same decision process. The output is a prioritized hypothesis with a clear rationale and a plan for testing it—not a claim that the molecule already works.
- 01 / 04
Frame the question
Start with biological evidence, available structures or RNA context. Define the desired mechanism and what an experiment would have to show.
- 02 / 04
Design & compare
Compare suitable molecular approaches using structural analysis, simulation and property triage. Rank candidates against the question, not a score in isolation.
- 03 / 04
Challenge the ranking
Check reference cases, applicability and practical constraints. Preserve uncertainty, negative findings and the reasons a promising design may fail.
- 04 / 04
Prepare the handoff
Connect candidate rationale to proposed material specifications, readouts and decision criteria. Experimental results can then guide the next design cycle.
INSIDE THE PLATFORM
Different methods. A traceable decision.
Our technical architecture brings together 13 computational and experimental-interface modules. These are grouped below by scientific purpose; the methods needed are selected for each project.
How we decide
Rules we apply in projects—not reported results.
- When docking and simulation disagree
- We do not pick the more favorable score. We first check whether each method reproduces the reference ligand and whether the pose holds in molecular dynamics; if they still disagree, the conflict is recorded as uncertainty and reflected in the ranking.
- When a candidate is excluded
- Candidates outside a method's applicability domain, with high synthesis or property risk, or supported only by a model that failed on reference cases are removed from the ranking—and the reason is recorded.
- When confidence is low
- We propose the simplest measurement that could change the decision most. If the binding hypothesis is the uncertain part, for example, binding is confirmed by SPR or ITC before cellular degradation and mechanism experiments.
Structure & confidenceKnow what the starting model can support.
Use experimental or predicted structures with their provenance and confidence. AlphaFold-derived structures and complementary prediction approaches inform pocket assessment; agreement is not a substitute for structural validation.
- AlphaFold / UniProt
- RoseTTAFold2
- Pocket analysis
Molecular physics & dynamicsExamine conformations, energetics and model limits.
Molecular dynamics, quantum-chemical parameterization, enhanced sampling, pathway and committor analyses address different questions. Each requires suitable controls, convergence assessment and a defined applicability range.
- ORCA / Psi4
- GROMACS / PLUMED
- PathCV / committor analysis
- OpenFE / RBFE
Pose generation & comparisonCompare proposals rather than trust one score.
Geometry- and diffusion-based pose proposals can be compared with docking and energy-based rescoring. These are complementary computational stages—not independent experimental confirmations of binding.
- EquiBind
- DiffDock
- AutoDock Vina
- MM-GBSA
AI prediction & molecule proposalsBalance binding hypotheses with developability.
Graph-based affinity and ADMET models support prioritization alongside de novo proposal methods, including reinforcement learning. Predictions are interpreted against relevant controls, domain limits and downstream experimental requirements.
- GNN
- ADMET
- De novo design / RL
Specialist electronic-state calculationsUse advanced methods where the question warrants them.
TD-DFT and EOM-CCSD are specialist tools for suitable electronic and photophysical questions. They are not a universal final check of drug affinity, efficacy or safety.
- TD-DFT
- EOM-CCSD
Experimental interface & feedbackSpecify what must be measured next.
Plan binding or thermodynamic measurements such as SPR and ITC with experimental partners, then link suitable cellular readouts to the original hypothesis. Feeding results back into the next design cycle is the intended workflow—not a claim of completed experimental validation.
- SPR / ITC planning
- Cellular assay planning
- Design feedback
Platform briefing
What our AI platform does—and what it does not
A walkthrough of our computational research workflow, candidate prioritization and the boundary between predictions and experimental evidence.
Scope of our work
- Current capabilities
- Protein degraders · RNA/ASO · Small moleculesComputational design and candidate prioritization
- Collaboration scope
- ADC payloads & linkers · PeptidesComputational comparisons that rely on a partner's assets and laboratories
- Exploratory
- Neoantigen prioritization · Quantum computingMethods under review; no results are claimed
02 PLATFORM CAPABILITIES
One research question.
More than one way forward.
A target does not arrive with a preferred modality. We compare biological rationale, molecular feasibility and the evidence needed to choose the next approach.
Computational design
Design for proximity. Test for degradation.
Connect target recognition, linker design and E3-ligase recruitment in one design workflow. Structural plausibility helps prioritize a candidate; it does not establish protein degradation.
What we evaluate
- Target-binding features and linker attachment geometry
- Linker conformations and ternary-complex plausibility
- Physicochemical properties, permeability risks and design constraints
- Computational output
- Prioritized design hypotheses with structural rationale, known limitations and a proposed synthesis and assay brief.
- What experiments must establish
- Cellular degradation, concentration response, selectivity and mechanism-dependent activity.
Computational design
Start with RNA evidence. Match the mechanism.
Evaluate RNA and splicing hypotheses before choosing an antisense design. Splice modulation and RNA knockdown require different mechanisms, chemistry and experimental readouts.
What we evaluate
- RNA evidence, transcript context and strand-concordance checks
- Accessibility and transcriptome/genome off-target screening
- Mechanism–chemistry matching and suitable experimental controls
- Computational output
- Prioritized sequence-design hypotheses, a quality-control record and an experiment brief. Sequences and undisclosed targets remain confidential.
- What experiments must establish
- The intended RNA or isoform change, off-target effects and functional rescue—not just sequence compatibility.
Computational prioritization
Use more than a docking score.
Combine structural analysis, pose generation, molecular simulation and property prediction to compare candidates within an explicitly defined evaluation domain.
What we evaluate
- Pocket assessment, reference ligands and redocking controls
- Docking, molecular dynamics and calibrated free-energy comparisons
- ADMET flags, synthetic accessibility and candidate-generation hypotheses
- Computational output
- A ranked candidate set with method provenance, reference comparisons and uncertainty—not a claim of absolute binding affinity.
- What experiments must establish
- Binding, biochemical activity, cellular effects and experimental ADME or safety measurements.
Proposed collaboration
Complement biologics with small-molecule insight.
Explore payload and linker prioritization for antibody–drug conjugates, and property triage for peptides or macrocycles. Antibody assets and experimental development remain with the partner.
What we evaluate
- Payload binding hypotheses and physicochemical risk flags
- Linker properties, attachment feasibility and stability hypotheses
- Permeability and efflux triage relevant to payloads and larger molecules
- Computational output
- A bounded computational comparison for a partner-defined set of payloads, linkers or peptides. Scope and transferability are agreed before a pilot.
- What experiments must establish
- Conjugation, drug-to-antibody ratio, aggregation, payload release, cellular activity and any bystander effect. We do not offer de novo antibody design.
Exploratory prioritization
Prioritize immune hypotheses. Do not assume response.
Use class I peptide–MHC prediction to organize neoantigen candidates for follow-up. A binding or presentation prediction is only one part of an immune-response hypothesis.
What we evaluate
- Peptide–MHC class I compatibility and candidate ranking
- Reference comparisons and model applicability
- Evidence requirements for subsequent experimental selection
- Computational output
- A candidate-ranking rationale and a clear statement of what the prediction does—and does not—support.
- What experiments must establish
- Actual presentation, T-cell recognition and functional immune activity. Presentation is not proof of immunogenicity.