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In this context, pretrained Large Language Models (LLMs) have emerged as potential tools for molecular design, as they appear to be capable of creating and modifying molecules based on simple instructions provided through natural language prompts.
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Jan 23, 2023 · A large-scale transformer-based language model with relative position embedding that enables the encoding of spatial information in molecules.
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May 21, 2024 · In this work, we show that the Claude 3 Opus LLM can read, write, and modify molecules according to prompts, with an impressive 97% valid and unique molecules.
Feb 6, 2024 · Here we show that GPT-3, a large language model trained on vast amounts of text extracted from the Internet, can easily be adapted to solve various tasks in ...
Working collection of papers, repos and models of transformer based language models trained or tuned for the Chemical domain, from natural language to ...
Large language models have made significant strides in natural language processing, enabling innovative applications in molecular science by processing ...
Jun 26, 2024 · We find that SMILES embeddings generated using LLaMA outperform those from GPT in both molecular property and DDI prediction tasks.
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Feb 2, 2024 · This paper provides a thorough exploration of the nuanced methodologies employed in integrating LLMs into the field of chemistry.
LlaSMol (large language models for small molecules) is a series of LLMs built for conducting various chemistry tasks. Specifically, we use Galactica, Llama 2, ...
Jul 24, 2024 · This paper presents a study on the integration of domain-specific knowledge in prompt engineering to enhance the performance of large language models (LLMs) in ...
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