Accelerating Drug Discovery with BioLLMs
Modernize your drug discovery process using biology-specific generative AI models in 10-12 weeks. At least 50% funded by AWS.
From questions to clarity in weeks.
Engage in our BioLLM Acceleration program and transform the costly and protracted drug discovery process. Within 10-12 weeks, our experts build model architectures using BioLLMs such as protein and DNA language models customized to your proprietary and expanded public datasets. By leveraging generative AI, we exceed the limits of physical screening for bigger sampling and better candidate selection.
Our work concludes with a comprehensive Proof of Concept (POC), model performance comparison, resulting model, intermediate datasets and more, paving the way for broader training initiatives and productization.
I would recommend Loka. They're an enterprise-level partner with a focus on getting the to the core of the POC.
Why Accelerate with BioLLMs?
Increase Predictive Power
Embeddings from large open models (like ESM) can be calculated “off the shelf” and paired with simple predictive architectures to significantly enhance predictive power on internal and proprietary data.
Increase Scale of Screening
Open generative modeling can create novel candidate molecules and powerful predictive models to increase the success rate of the generated candidates.
Tailor Models to In-House Data
Large open models can be easily fine-tuned with public and proprietary data to achieve significant performance improvements.
How It Works
Phase I:
Discovery &
Data Preparation
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 II:
Model Development & Preliminary Testing
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 III:
Optimization, Validation
& POC
The final phase involves refining the BioLLMs for optimal performance and validating their predictions against known benchmarks. We develop a comprehensive POC 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
Practical deliverables that simplify your decision-making
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.
Since 2023, more than 60 life science companies have accelerated generative AI adoption with the Loka.
