Sold by: Insilico MedicineÂ
Deployed on AWS
Nach01 is a language foundation model designed for AI-driven drug discovery, enabling fine-tuning and efficient molecular property prediction to accelerate your DMTA cycle.
Overview
Nach01 is an innovative foundation model for drug discovery that uniquely combines natural language processing with advanced chemical intelligence. By integrating text-based chemical representations and 3D molecular structures through its dual-component architecture - LLM component (supporting textual reasoning in chemical science) and PC-Encoder (featuring spatial awareness) - the system delivers robust, high-quality performance in molecular property prediction and generative design tasks.
This versatile model enables pharmaceutical research teams to streamline critical R&D processes and enhance computational efficiency. Nach01's ability to process instruction-tuned prompts, handle multimodal inputs, and integrate seamlessly with existing workflows makes it a transformative solution for the pharmaceutical industry, supporting diverse applications from hit identification to lead optimization, all while meeting established benchmarks with high performance.
Highlights
- Trained on Diverse Biological and 3D Molecular Data: Nach01 has been trained on a comprehensive combination of text-based biological information and 3D structural data, including small molecules, proteins, and peptides. By combining disease annotations, chemical representations, gene interactions, and molecular properties, the model captures both biological context and spatial molecular insights to advance drug discovery workflows.
- Tailored Predictions with Nach01: Train Nach01 with your proprietary data to meet specific drug design goals. Fine-tune the model for individual molecular properties or apply multitask learning to tackle diverse predictive challenges, unlocking the full potential of your data.
Details
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Delivery method
Latest version
Deployed on AWS
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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/host/hour |
|---|---|---|
ml.p3.2xlarge Inference (Batch) Recommended | Model inference on the ml.p3.2xlarge instance type, batch mode | $29.80 |
ml.p3.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.p3.2xlarge instance type, real-time mode | $29.80 |
ml.g6e.2xlarge Training Recommended | Algorithm training on the ml.g6e.2xlarge instance type | $25.10 |
ml.g4dn.xlarge Inference (Batch) | Model inference on the ml.g4dn.xlarge instance type, batch mode | $29.80 |
ml.g4dn.2xlarge Inference (Batch) | Model inference on the ml.g4dn.2xlarge instance type, batch mode | $29.80 |
ml.g4dn.4xlarge Inference (Batch) | Model inference on the ml.g4dn.4xlarge instance type, batch mode | $29.80 |
ml.g4dn.8xlarge Inference (Batch) | Model inference on the ml.g4dn.8xlarge instance type, batch mode | $29.80 |
ml.g4dn.16xlarge Inference (Batch) | Model inference on the ml.g4dn.16xlarge instance type, batch mode | $29.80 |
ml.g5.xlarge Inference (Batch) | Model inference on the ml.g5.xlarge instance type, batch mode | $29.80 |
ml.g5.2xlarge Inference (Batch) | Model inference on the ml.g5.2xlarge instance type, batch mode | $29.80 |
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the 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
This version introduces the predictive capabilities of Nach01, enabling users to fine-tune the model using their own data for tailored applications in drug discovery.
Additional details
Inputs
- Summary
The training and test data should be provided as CSV files and must include the following columns:
molecule: Contains the molecular information. input_format: Specifies the format of molecular representation, with "smiles" as the default. input_description: Describes the input data, with "small molecule" as the default. task_description: Provides the name of the property being predicted or includes an extended description of the task. target: Stores the target values associated with the molecular property being predicted. task_type: Defines whether the task is a classification or regression problem.
https://github.com/insilicomedicine/insilico-aws/blob/main/insilico_aws/examples/nach01/bbbp/request.csv
https://github.com/insilicomedicine/insilico-aws/blob/main/insilico_aws/examples/nach01/bbbp/request.csv
Support
Vendor support
If you have any questions or need assistance, feel free to reach out at: chemistry42@insillicomedicine.comÂ
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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