From target to chemical matter — faster, and with the science to back it.
HAZEJONES LTDA is your computational drug-design partner. We turn targets into validated, synthesizable starting points and optimized leads using the full modern CADD toolkit — structure- and ligand-based modeling, machine learning, generative AI, and rigorous free-energy methods.
We support every stage of early discovery, from asking whether a target is tractable to delivering a prioritized, developability-aware lead series. Each engagement is tailored: we match the method to your target class, your timeline, and your budget, and we work hand-in-hand with your medicinal chemists so that computation translates into molecules worth making.
Service 01
Target & Druggability Assessment
Decide where to invest with structural rationale, not a hunch.
Target & Druggability Assessment
Every successful campaign starts with knowing whether — and where — a target can be drugged. We assess druggability, locate and characterize binding sites (including cryptic and allosteric pockets), and analyze likely binding modes, so you commit resources to tractable opportunities with a clear structural rationale rather than a hunch. When no experimental structure exists, we build and validate models to give you a reliable starting point.
Druggability scoring and tractability assessment
Binding-site, cryptic-pocket, and allosteric-site detection
Hotspot mapping and binding-mode / pose analysis
Structure preparation from crystal, cryo-EM, AlphaFold, or homology models
Molecular-dynamics-based pocket and conformational analysis
Service 02
Hit Identification
Chemical starting points, across the full spectrum of modern approaches — method matched to your target, timeline, and budget.
ML-Guided Virtual Screening of Ultralarge Libraries
Make-on-demand chemical spaces now run into the tens of billions of compounds — far beyond what brute-force docking can reach. Our machine-learning-accelerated workflows triage these ultralarge libraries intelligently, learning as they go to surface the most promising, synthesizable hits while keeping compute tractable. You get a focused, diverse hit list drawn from spaces orders of magnitude larger than a conventional screen.
ML-accelerated docking at billion-compound scale
Screening of enumerated make-on-demand and REAL-type spaces
ML scoring, diversity selection, and synthesizability filtering
Docking & 3D Pharmacophore Screening
We rank and prioritize libraries with molecular docking and 3D pharmacophore screening — grounded either in your target's binding-site structure or in the shared features of known active compounds. The result is a defensible, structure- or ligand-based prioritization you can take straight to assay.
Structure-based molecular docking and pose triage
3D pharmacophore model building and screening
Shape and electrostatic similarity searching
Scaffold hopping into new chemotypes
Generative AI Hit Generation
Go beyond what any catalogue contains. Pocket-aware generative models design novel, IP-friendly chemical matter from scratch — conditioned on your binding site and shaped toward the property profile you need. It's a route into white-space chemistry on targets where existing libraries fall short.
Structure-conditioned (pocket-aware) de novo design
We design and curate focused fragment libraries, then run crystallographic fragment screens with a specialist CRO partner to identify and validate genuine hits with experimentally resolved binding poses. From there, we grow, merge, or link them into high-quality leads — keeping a close eye on ligand efficiency so potency is built in the right way from the start.
Fragment library design and curation
Crystallographic fragment screening through CRO partners
Structure-guided fragment growing, linking, and merging
Ligand-efficiency tracking throughout optimization
Service 03
Hit-to-Lead Optimization
Turning hits into balanced, developable leads — optimizing potency and properties together.
ADME-Informed Generative AI Design
Potency alone doesn't make a drug. Our generative optimization steers every design cycle with predicted ADMET — solubility, permeability, metabolic stability, hERG and other liabilities — so the molecules you choose to make are balanced for potency and developability from the outset. Fewer dead-end analogues; more designs worth synthesizing.
Multi-parameter optimization (potency + ADMET in one objective)
Free-energy perturbation brings near-experimental accuracy to potency prediction — routinely on the order of ~1 kcal/mol — letting you rank design ideas and prioritize synthesis before committing to the bench. We set up, validate, and interpret rigorous relative binding free-energy calculations, compressing your design-make-test-analyze cycles and helping you spend synthesis effort where it counts.
Relative binding free-energy (RBFE) calculations
Perturbation-map design and assay-calibrated ranking
Activity-cliff detection and selectivity assessment
Pre-synthesis triage to reduce make-test iterations
Scope
Built for diverse targets and modalities
Our methods adapt to your biology. We work across target classes and across the modalities reshaping modern discovery — so the approach fits the problem, not the other way around.
We pick the right technique for your target instead of forcing every problem through one workflow.
Designed for the bench.
Every output is synthesizable and testable — computation that med-chem teams can act on.
Senior-scientist-led.
You work directly with the person doing the science, not a handoff chain.
Flexible engagement.
Standalone studies or embedded partnership across a full campaign.
Engagement
How we work
A clear path from question to actionable molecules.
01
Scope & feasibility
We define the question, review available data and structures, and agree on success criteria.
02
Workflow design
We select and configure the right methods for your target and goals.
03
Compute & iterate
Calculations run, with checkpoints and tight feedback loops with your team.
04
Prioritized deliverables
Ranked compounds, structural rationale, and clear reports you can act on.
05
Ongoing partnership
Optional continued support across design-make-test cycles.
Contact
Interested in a collaboration?
Reach out to schedule an introductory call. Once a confidentiality agreement is in place, we can discuss your research program and outline how computation could accelerate it.
