SandboxAQ
NexusAi Summary: REAQT is SandboxAQ’s LQM platform that combines physics-based models with LLM interfaces to deliver validated, quantitative predictions for drug discovery, materials, and enterprise scientific workflows.
NexusAi Overview
In practice, REAQT accepts a problem statement through an LLM interface, constructs a retrieval-augmented task plan, executes LQM modules (docking, DFT variants, molecular dynamics, Bayesian optimization), and returns a quantitative result with provenance. The platform emphasizes reproducibility, parameter transparency, and integration hooks for downstream validation and lab workflows.
Core capabilities
The platform is targeted at technical buyers and practitioners: computational chemists, materials scientists, ML engineers, and R&D teams who need scalable, auditable predictions. It’s valuable when experiments are costly or slow, when regulatory traceability is required, or when organizations must combine domain physics with AI workflows to de-risk designs and shorten iteration cycles.
- Combines physics-based simulations with LLM interfaces to produce quantitative predictions.
- Provides modular LQMs for chemistry, materials, and biomolecular simulation workflows.
- Integrates LLMs as user-facing agents while anchoring outputs in validated models.
- Supports workflows such as virtual screening, docking, and property prediction at scale.
- Designed for enterprise deployment with API access, orchestration, and proprietary datasets.
Why REAQT matters
Real-world positioning
SandboxAQ provides API endpoints and documentation for integrating REAQT into existing pipelines, plus SDKs and example workflows for virtual screening and materials prediction. Onboarding includes dataset mapping, parameter calibration sessions, and optional deployment options—cloud, hybrid, or on-prem—plus support for provenance logging and audit requirements to satisfy regulated workflows.
NexusAi: We treat LLMs as the conversational interface and LQMs as the decision engine to deliver experiment-grade outcomes.
Getting started
REAQT positions scientific modeling and validated data at the center of AI-driven decisioning. Its differentiators are physics-grounded LQMs, an LLM agent that manages workflows, and enterprise orchestration designed for reproducible, auditable predictions that map directly to experimental choices.
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SandboxAQ
SandboxAQ is a company combining AI and quantum-inspired methods to build large quantitative models for industry. The team focuses on high-fidelity simulation, enterprise deployment, and validated outcomes across pharma, energy, and defense.
REAQT pairs large quantitative models (LQMs) with an LLM-driven agent layer to convert human prompts into reproducible, simulation-backed results. It uses proprietary datasets, physics and chemistry modules, and orchestration tooling to run virtual screening, docking, property prediction, and material design at scale. The interface treats LLMs as a conversational control plane while ensuring outputs are grounded in validated computational models rather than freeform generation. REAQT is built for regulated, high-impact applications where experimental fidelity, auditability, and integration with enterprise pipelines matter.
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