Active research.
Not slide decks.
Our work spans spatial genomics, single-cell multiomics, protein structure prediction, generative design, and SE(3)-equivariant scoring — all wired into reproducible, containerized pipelines.
Spatial Multiome Architecture
Mapping gene expression and chromatin accessibility in tissue space using Visium HD, MERSCOPE, and Xenium. We reconstruct tumor microenvironment architecture and cell-cell interaction networks at submicron resolution — going beyond counts to spatial context.
Cross-Modal Single-Cell Resolution
Integrated single-nucleus and single-cell RNA-seq analysis across tissue types — from UMAP clustering and cell state annotation to cross-species comparison and inter-donor variability quantification. We run snRNA-seq, bulk RNA-seq, and Visium spatial data through a single unified pipeline, delivering coherent cross-modal answers without batch artefacts.
End-to-End Co-Complex Structure Prediction
Running AlphaFold3, Chai-1, and Boltz-2 pipelines for joint prediction of protein-ligand, protein-nucleic acid, and antibody-antigen co-complexes at scale. Confidence-calibrated structural ensembles feed directly into our docking and free-energy perturbation workflows — from sequence to binding-ready pose in a single reproducible run.
De Novo Protein & Antibody Design
Leveraging RFdiffusion, ProteinMPNN, and Chai-1 to design high-affinity binders from scratch. We integrate structural biology with generative AI to bypass traditional screening — designing therapeutic proteins, antibody CDR loops, and peptide drugs with programmable specificity and in-silico-validated binding geometry.
SE(3)-Equivariant GNN Scoring Functions
Building and fine-tuning SE(3)-equivariant Graph Neural Networks for structure-aware molecular scoring — docking pose ranking, binding affinity prediction, and protein-ligand interaction geometry. Integrated with computer vision models for 3D pocket feature extraction and ensemble scoring across conformational states.
End-to-End Drug Discovery Pipeline
A full computational drug discovery stack — from target identification and hit discovery through lead optimization and ADMET prediction. Foundation models (ESM-3, ProtTrans, in-house architectures) combined with GNN scoring, generative design, and pharmacovigilance-linked safety profiling, all wrapped in reproducible, containerized pipelines.
Predictive Safety & ADR Modeling
Integrating genomic variants with large-scale pharmacovigilance data to predict Adverse Drug Reactions before compounds enter trials. We build deep learning architectures that map the intersection of genetic markers and chemical toxicity — surface ADR signals from GWAS-linked data before Phase I.
Cross-Modal Genomic Representation Learning
Pretraining and fine-tuning large biological foundation models — scFoundation, Geneformer, and in-house architectures — on paired transcriptomic, proteomic, and imaging datasets. Unified representations that generalize across cell types, tissues, and perturbation screens.
Epigenomic Reprogramming Maps
CUT&RUN, CUT&TAG, and scATAC profiling to map transcription factor binding dynamics, super-enhancer activation, and chromatin remodeling across oncogenic transformation and therapeutic response. Linking regulatory grammar to disease phenotype.
Full-Length Variant & Isoform Resolution
PacBio Revio and Oxford Nanopore R10 workflows for phased structural variant calling, full-length transcript isoform quantification, and direct base modification detection. Closing the gaps left by short-read sequencing in complex genomic regions and repeat elements.
$2.6 billion per approved drug.
Most of it lost after Phase I.
The simulation-to-clinic gap costs the industry north of a trillion dollars annually in failed trials, ADR-related withdrawals, and regulatory delays. The tooling hasn't caught up. Legacy platforms were built for a world without spatial transcriptomics, protein diffusion models, or patient-level variant resolution. We were built for this world.
Three forces converged.
We integrated all three.
Generative protein models crossed the utility threshold
AlphaFold3, Chai-1, Boltz-2, and RFdiffusion have moved from benchmarks to production. We run these in validated, reproducible pipelines — not demos.
Spatial transcriptomics reached single-cell resolution
Visium HD, Xenium, and MERSCOPE now resolve tissue architecture at submicron scale. Integrated with snRNA-seq, they answer questions that bulk sequencing never could.
Pharmacovigilance data became structurally accessible
Large-scale real-world safety data, linked to genomic variant databases, now enables ADR prediction before a compound enters a trial. We built the architecture to use it.
The companies that integrate spatial multiomics, generative structure prediction, and pharmacovigilance intelligence into a single reproducible platform in the next 24 months will define the next era of precision medicine. That integration is exactly what we are building.
Active builds.
Doing interesting
work? Let's make it boring.
We're always open to research collaborations, data partnerships, and advisory conversations.
inquiries@boringscience.bio