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.

Live molecule generation
Loading generative model…
Mol. Weight
cLogP
QED
H-Bond Donors
H-Bond Acceptors
Rendering 2D structure…
Activity Tier
Toxicity
Drug-Drug Interaction

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 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.

Data product

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.

Workflow

How discovery teams use Neuralocity

  1. 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.

  2. 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.

  3. 03

    Add candidates to your cart

    Hold promising molecules in your cart before committing assay budget, out of general circulation until you decide.

  4. 04

    License and purchase

    Purchase directly from your cart when you're ready, with full audit logging, and field registry metadata.