Overview
How it Works:
Phase 1 - Discovery & Data Preparation (1-2 weeks): We collaborate with stakeholders to define specific drug discovery objectives and prepare the integrated proprietary and public datasets for model training. This phase sets the foundation for building architectures tailored to key tasks using BioLLMs.
Phase 2 - Model Development & Preliminary Testing (6-7 weeks): During this technically intense phase, we train BioLLMs using advanced ML techniques on the prepared datasets, followed by initial testing to evaluate performance and scalability for predictive and/or generative workflows for drug discovery.
Phase 3 - Optimization, Validation & POC (1-2 weeks): The final phase involves refining the BioLLMs for optimal performance and validating their predictions against known benchmarks. A comprehensive POC is developed to demonstrate the models' effectiveness in streamlining the drug discovery process.
Who is this for?
- Biotech companies
- AgTech ventures
- Big Pharma arms focusing on R&D rather than commercial or clinical stage
What you get: Since 2023, over 60 life science and healthcare companies have accelerated generative AI adoption with the Loka.
- Proof of Concept (POC): Validate the effectiveness of BioLLMs in achieving goals such as advancing drug discovery.
- BioLLM Architecture: Develop a custom modeling architecture that leverages leading models such as ESM and MoLFormer that is customized your specific use case.
- Performance Evaluation: Setup a robust validation strategy and adequate evaluation metrics that best approximate performance in production.
- Model Training and Validation: Integrate optimized training jobs and experiment tracking within AWS, paving the way for broader training efforts and model productization.
Highlights
- Enhance Predictive Power: Embeddings from large open models like ESM can be readily calculated and paired with simple predictive architectures to boost predictive power on internal data. Expand Screening Capacity: Open generative models seamlessly generate novel molecules, enhancing success rates when combined with a robust predictive model. Tailor Models to In-House Data: Large open models can be fine-tuned with public and proprietary data to significantly improve performance.
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To schedule your free Consultation contact Loka at AWS-team@loka.com , or your AWS representative.