Sold by: SandboxAQ
Deployed on AWS
A fast CPU proteochemometric ML model for potency prediction in high-throughput screening and in silico drug discovery. It enables drug discovery teams to perform fast compound triage and early safety screening to identify protein-ligand pairs that are most likely to interact.
Overview
AQPotency is an ultrafast CPU proteochemometric ML model for potency prediction in high-throughput screening and in silico drug discovery. It enables drug discovery teams to rank and prioritize the most promising small-molecule ligands for a given protein target, or identify the targets most likely to interact with a given compound.
AQPotency can be used as a large-scale triage tool ahead of a virtual screening or reverse virtual screening workflow. Computational chemists can rank libraries of millions of compounds within hours, but also examine security liability, off-target panels, and selectivity of a compound early in their exploration in real-time, before committing to more expensive computational or experimental methods.
For each input protein-ligand pair (as UniProt id and SMILES), AQPotency returns ranked potency predictions together with uncertainty and applicability signals that help drug discovery teams tell stronger opportunities apart from lower-confidence results.
SandboxAQ does not collect any metadata about your targets or small molecules.
Please see our onboarding guide for more information and concrete examples.
Extended customer support including direct access to an engineer (8am-8pm ET) is available for this product for a yearly fee. Please contact us at support.aisim@sandboxaq.com for more information.
Highlights
- Accelerating Drug Discovery: Span large chemical design spaces and account for off-target and safety panel screening as well as selectivity profiling in the early steps of virtual screening. Focus on the most promising candidates and discard the rest. Screen millions of small molecules in parallel without babysitting a job, within hours.
- Isolated, Secure Workloads: Run calculations with absolute data privacy. Because the model deploys via Amazon SageMaker inside your own isolated AWS tenant, your input data, inference payloads, protein targets and small-molecule compounds are never exposed to external networks, never shared with SandboxAQ, and never used for retraining. Your IP is protected.
- Ready to go out of the box: the model was trained on a set of UniProt targets spanning the human proteome and is therefore not target-specific. No need to retrain the model. Deploy the model, get your first predictions within minutes.
Details
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Delivery method
Latest version
Deployed on AWS
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Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost |
|---|---|---|
ml.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge instance type, batch mode | $900.00/host/hour |
inference.count.m.i.c Inference Pricing | inference.count.m.i.c Inference Pricing | $0.001/request |
Dimensions summary
You pay for AQPotency based on usage, with two independent dimensions. The first charges by the hour for running batch model inference on the ml.m5.2xlarge instance type. Your cost here scales with how long you run the instance. The second charges per inference request, so your cost scales with the number of protein-ligand pairs you score. These dimensions bill separately as you use them. There is no upfront commitment or fixed term. The more you run or the more requests you submit, the more you pay.
Top-of-mind questions for buyers
One request is a scored protein-ligand pair. AQPotency ranks pairs by predicted binding strength (pIC50) and returns an uncertainty estimate and applicability-domain score with each. Your request charge scales directly with how many pairs you submit for scoring.
The two dimensions bill independently and appear on the same invoice. The hourly charge accrues for the time the ml.m5.2xlarge instance runs in batch mode. The request charge accrues per scored pair. Together they add up; neither replaces the other.
The hourly dimension meters time the ml.m5.2xlarge instance runs in batch mode. Charges accrue while the instance is active, not by request volume. The per-request charge applies separately each time you score pairs, so stopping the instance ends the hourly accrual.
www.sandboxaq.com
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Fixed behaviour of real-time scan to ensure full human proteome scan is covered.
Additional details
Inputs
- Summary
Depends on chosen mode, please check https://github.com/sandbox-quantum/marketplace-public-docs/blob/main/aqpotency/aws-marketplace/aqpotency-sample.ipynb
- Limitations for input type
- tbd
- Input MIME type
- application/json, text/csv
https://github.com/sandbox-quantum/marketplace-public-docs/blob/main/aqpotency/aws-marketplace/aqpotency-sample.ipynb
https://github.com/sandbox-quantum/marketplace-public-docs/blob/main/aqpotency/aws-marketplace/screen-workflow/sample_data/input_batch_sample.csv
Support
Vendor support
Support resources
For all support inquiries - including model deployment assistance, inference troubleshooting, accuracy questions, billing issues or more generally getting started - contact the SandboxAQ AQCat team at support.aisim@sandboxaq.com
Please consult our product's documentation listed under the Resources section before reaching out.
Refund policy
All charges and subscription fees for this SaaS offering are final and non-refundable. You may cancel your subscription at any time to prevent future billing; however, your cancellation will take effect at the end of your current billing cycle. We do not provide prorated refunds or credits for any partial months, unused time, or downgraded plans.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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