Drug discovery is risky. We make success more likely.

Specialized computational discovery teams.
From target evidence to lead optimization.

6+ clinical & advanced candidates 35+ patents co-invented 30+ programs delivered

Discuss your program See our track record

Computational predictions that inform your next move

Every stage of a discovery program demands a call on incomplete data. We add the computational evidence that makes those calls defensible.

01 Target Validation

Biological literature combined with quantitative analysis, pathway modeling, and assessment of target relevance, mechanism, and therapeutic potential.

02 Strategy & Modality

Detailed structural and mechanistic evidence to define strategy and prioritize modalities — small molecules, degraders, molecular glues, peptides, ADCs, and antibodies.

03 Optimization

Physics-based modeling and ML approaches to optimize potency, selectivity, stability, and generate higher-confidence predictions for exposure, target engagement, and therapeutic performance.

04 IP & Differentiation

Thorough assessment of chemical, biological, and competitive landscape to identify differentiation opportunities and guide design with a defensible scientific position.

Offerings

The right method for the question you're asking

Quantitative Biology

Translating biological complexity into quantitative, predictive models. We build mechanistic and systems-level representations of disease biology, pharmacology, and drug behavior — from pathway dynamics to patient-level outcomes.

When the biology is too complex for intuition alone, quantitative models provide the framework to ask better questions.

Techniques used

  • Knowledge Graphs
  • Pathway modeling
  • PK/PD simulation
  • Systems biology
  • Network pharmacology

Molecule Design

From hit generation through lead optimization. AI/ML-augmented virtual screening, de-novo design, and multi-parameter optimization that reduces synthesis rounds while addressing potency, selectivity, and ADMET simultaneously.

We design compounds with IP awareness from day one — 35+ co-invented patents as proof that computational design and freedom-to-operate go hand in hand.

Techniques used

  • FEP+ / free energy
  • Generative chemistry
  • De novo design
  • Scaffold hopping
  • ML-ADMET prediction
  • Covalent docking
  • 3D-QSAR

Protein-Protein Interactions

Targeting PPIs requires a different computational toolkit — larger interfaces, shallower binding sites, and allosteric mechanisms that traditional docking misses. We model protein complexes, map hotspots, and design molecules that disrupt or stabilize specific interactions.

This includes molecular glues, PROTACs, and macrocyclic peptides — modalities designed to exploit the biology of protein interfaces.

Techniques used

  • PPI hotspot mapping
  • MD simulations
  • Protein-protein docking
  • PROTAC ternary modeling
  • Macrocycle design
  • Molecular glue prediction
All offerings

Track record

A team that has shaped investment decisions,
clinical programs, and company trajectories

6+ clinical and advanced candidates, 35+ co-invented patents, and programs across RNA, oncology, GPCR, kinase, and radiopharmaceuticals.

RNA / Splicing

Splicing Modulators

Full lead optimization campaign. MoA elucidation, selectivity optimization over related targets, multi-parameter property optimization across multiple synthesis rounds.

Advanced candidate: ATM5292

RNA / RBP

RNA Binding Protein Inhibitor

Lead optimization of a novel target. Structure-guided design addressing potency, selectivity, and ADMET through iterative computational-experimental cycles.

Advanced candidate: RBX6610

Oncology

First-in-Class Covalent PARG Inhibitors

Patent filed + AACR 2024 poster

GPCR / Metabolic

GPCR Agonist for Metabolic Syndrome

Optimized lead series delivered

All case studies

Want to de-risk your next discovery decision?

Tell us about the problem you are solving for and we'll tell you what computation and simulation can contribute and where we'd start.

Discuss your program