Customized Amazon Nova models improve molecular-property prediction in drug discovery
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Machine learning
Customized Amazon Nova models improve molecular-property prediction in drug discovery
A single, optimized LLM unifies what previously required multiple models and can serve as a reasoning partner for medical chemists.
By Krishnateja Killamsetty , Andy Lapastora , Karthick Prasad Gunasekaran
April 15, 2026
8 min read
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Overview by Amazon Nova
Using supervised fine-tuning and reinforcement fine-tuning, a single customized Amazon Nova model predicts 11 molecular properties simultaneously, matching or outperforming multiple separately trained multitask GNN models on 7 of 11 properties. Reinforcement fine-tuning with Huber loss rewards yielded the best results, achieving a 4.9% R² improvement over the supervised fine-tuning baseline and average performance within 5% of specialized GNNs as measured by RMSE. Nimbus Therapeutics deployed its custom-built LLM, Novus, on Amazon Bedrock for molecular-property prediction, with plans to extend capabilities toward molecular design and conversational reasoning.
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In recent years, large language models (LLMs) have become indispensable assistants for software engineers and knowledge workers. Nimbus Therapeutics enlisted us at Amazon’s Generative AI Innovation Center and Artificial General Intelligence (AGI) organization to investigate whether it’s possible to make equally capable assistants for medicinal chemists discovering new drugs. Such an agent could significantly speed up drug discovery, potentially saving lives. AI in drug discovery has traditionally involved models called graph neural networks , or GNNs. GNNs are the workhorses of molecular-property prediction across pharmaceutical R&D, and for good reason: they deliver strong accuracy on well-defined tasks.
Without a unified AI solution, chemists had to navigate multiple models to evaluate a single molecule — piecing together disconnected results across different interfaces, data formats, and failure modes.
Typically, multiple GNNs, specialized for different molecular properties, have to be built and maintained in-house — an expensive, operationally complex process. In recent years, the success of LLMs in a variety of research domains has caught the eye of biotech firms, but for drug discovery, general, off-the-shelf LLMs have proven to be less accurate than GNNs or other computational methods. We have adopted a new approach that combines the accuracy of GNNs with the generalizability and reasoning ability of LLMs. Using supervised fine tuning (SFT) and reinforcement fine tuning (RFT) to customize a general-purpose LLM, we were able to achieve results comparable to those of using multiple GNNs, at a fraction of the time and labor. Fine-tuned LLMs offer a significantly simplified workflow. In the traditional setting, each GNN has a separate interface, with its own quirks, data formats, and failure modes.…
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Notability
notability 6.0/10Substantive application post in drug discovery