Neuralocity
Computationally prioritized molecule data for drug discovery
Neuralocity's own generative AI (LLM), built in-house, is purpose-built to generate molecules with drug-like properties, packaged into a searchable, scored catalog with target-class rankings, safety signals, drug-likeness scores, and full provenance, ready for your screening pipeline.
Intelligence layer on a generative catalog
Searchable molecule catalog
Browse and filter millions of computationally generated molecules by molecular weight, LogP, QED, SA score, and Activity Tier (A through E) for any target class and mechanism, plus safety classification, drug-drug interaction risk, and structural similarity search.
Filtered for practical use
Every candidate is screened for drug-like properties, synthesizability, and stability before it's scored, so what you browse is chemistry a chemist could actually work with, not just raw generative output.
Multi-dimensional safety overlay
A dedicated model for each toxicity endpoint, evaluated on held-out data, applied at catalog scale so teams start from molecules that already pass predicted computational safety screens.
Provenance & claim control
Every molecule carries a source identifier back to its origin in the catalog, and every score is expressed as a rank or documented tier. Purchases and exports are fully audit-logged.
From catalog search to licensed export
In-house generative AI
A generative AI model (LLM) we built ourselves, purpose-built to generate novel molecules with drug-like properties.
Target-class scoring
Dedicated trained models for every target class and mechanism, evaluated on held-out data: publishable triage signals, not one generic algorithm.
Licensed datasets
Cart-based purchasing with provenance metadata and entitlement controls.
Purpose-built models, not one algorithm
Dedicated models for every target and endpoint
20 target-class x mechanism potency models, 8 toxicity endpoint models, and 5 CYP450 drug-drug interaction models — each trained and evaluated independently, not one generic classifier reused everywhere.
Predicted solubility, out of the box
A graph neural network (AttentiveFP) predicts log S for every catalog molecule, pre-trained on a broad collection of measured solubility data, then fine-tuned on a smaller, more rigorously curated benchmark for its final accuracy.
Evaluated the way it matters
Scaffold-clustered holdout splits, not random ones, so reported performance reflects how these models generalize to genuinely new chemotypes rather than molecules that merely resemble ones already seen in training.
Take a candidate deeper, on demand
- 01
Predict the 3D complex
Paste a target sequence and Boltz-2 predicts the 3D structure of your molecule bound to it directly from sequence, no crystal structure required, along with confidence signals and, if requested, binding affinity.
- 02
Get an independent second opinion
AutoDock Vina, a physics-based docking engine, independently searches for the best-fitting pose against that same predicted structure. Agreement between the two methods, not either one's confidence alone, is the more meaningful signal.
- 03
Let AI interpret the evidence
An AI-generated assessment weighs structural confidence, pose agreement, and affinity signals into a plain-language read on how much the combined evidence supports the predicted interaction, an opinion, not a computed score.
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Know what you're actually making
The same AI-analysis approach also assesses synthesizability and storage/handling stability for any molecule, since a candidate that's only theoretically interesting to a model but impractical to make isn't much of a candidate at all.
How discovery teams use Neuralocity
- 01
Pick a target class or property thesis
Every candidate you browse already passed a filter for drug-like properties, synthesizability, and stability. Narrow further by kinase-like, GPCR-like, protease-like relevance, or by safety, drug-likeness, and novelty envelopes.
- 02
Review ranked candidates
Inspect molecule detail pages with structure, properties, class-relevance percentiles from dedicated held-out-evaluated models, safety signals, and provenance per dimension.
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Add candidates to your cart
Hold promising molecules in your cart before committing assay budget, out of general circulation until you decide.
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License and purchase
Purchase directly from your cart when you're ready, with full audit logging, and field registry metadata.