Enterprise AI and physics technology leader SandboxAQ announced the general availability of AQPotency, a Large Quantitative Model (LQM) engineered to predict candidate molecule potency without requiring a solved 3D protein structure. Accessible directly on Anthropic’s Claude via the Model Context Protocol (MCP) and SandboxAQ’s portal, the computational tool allows biopharmaceutical research teams to virtually screen vast molecular libraries in seconds bypassing the slow, expensive laboratory testing traditionally required in early-stage drug development.
In conventional pharmaceutical research, prioritizing candidate compounds presents significant financial and operational risk. Established computer-aided virtual screening methods depend heavily on detailed crystal structures of disease targets, leaving many biologically significant targets inaccessible. AQPotency eliminates this bottleneck by running on standard computing hardware to evaluate molecule-target pairs in seconds at approximately $1 per 1,000 comparisons. Furthermore, the model delivers actionable confidence intervals and domain-of-applicability scores for every prediction, offering clear visibility into result reliability.
“SandboxAQ’s models have been very impactful for our work as we develop new treatments for Parkinson’s,” said Professor Dario R. Alessi, OBE, FMedSci, FRS, Director of the MRC Protein Phosphorylation Unit at the University of Dundee. “These models enable us to explore a much larger biochemical space in a short timeframe and improve both activity and selectivity. SandboxAQ’s unique datasets and models stand out in the industry for their impact.”
Dual-Directional Screening and Structural Flexibility
Beyond standard target-to-molecule scoring, AQPotency features reverse-screening capabilities. Researchers can evaluate a single promising compound against a wide panel of human proteins to generate ranked binding predictions, helping teams uncover mechanisms of action for uncharacterized molecules.
Key functional capabilities of AQPotency include:
Structure-Free Molecular Scoring: Evaluates compound potency across previously unmapped or complex protein targets, expanding discovery efforts beyond traditional 3D crystal structures.
Quantifiable Confidence Metrics: Provides explicit reliability scores alongside each prediction, allowing biopharma teams to allocate laboratory budgets exclusively to high-confidence leads.
Reverse Target Profiling: Scans individual molecules against broad protein panels to identify binding sites and clarify complex biological pathways.
Integrated Cloud Deployment: Connects directly into Claude via MCP, with upcoming enterprise distribution planned for Google Cloud Marketplace.
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“This collaboration with SandboxAQ highlights the power of combining advanced AI-enabled discovery with rigorous experimental validation to unlock novel opportunities against historically difficult membrane targets. By identifying selective SV2C binders from a broad commercial library, the work establishes a compelling foundation for the development of first-in-class small-molecule tools and future therapeutics aimed at Parkinson’s disease and other disorders of dopaminergic signaling,” said Dr. Gary W. Miller the Adrienne Block Professor of Environmental Health Sciences and the Vice Dean for Research Strategy and Innovation at the Columbia University Mailman School of Public Health.
Andrea Bortolato, Vice President of Drug Discovery, at SandboxAQ, said: “AQPotency has given us and our customers a faster, scalable and reliable way to prioritize compounds in the workflows we already run, without needing a 3D crystal structure of the target. This opens up programs that structure-based methods simply couldn’t reach. The confidence intervals make the output actionable for biopharma companies, and the model has already been successfully used in eight customer programs with experimentally validated impact.”
“What’s compelling about AQPotency is that it makes high-value discovery decisions faster and more practical,” said Robin Roehm, CEO and Co-Founder at Apheris, which offers federated data and AI networks for life science companies. “Researchers can prioritize the most promising compounds with greater confidence, focus experimental resources where they matter most, and expand discovery efforts to targets that have traditionally been harder to pursue.”
Commercial Availability
AQPotency is available immediately via Claude through MCP and SandboxAQ’s main platform, with deployment on Google Cloud Marketplace scheduled to follow. Concurrent with this launch, SandboxAQ has also released AQCat Adsorption Spin, a specialized LQM built to accelerate catalyst discovery within materials science and chemical engineering applications.