Language models in molecular discovery
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Acceso en línea: | |
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| _version_ | 1866929550573699072 |
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| author | Janakarajan, Nikita Erdmann, Tim Swaminathan, Sarath Laino, Teodoro Born, Jannis |
| author_facet | Janakarajan, Nikita Erdmann, Tim Swaminathan, Sarath Laino, Teodoro Born, Jannis |
| contents | The success of language models, especially transformer-based architectures, has trickled into other domains giving rise to "scientific language models" that operate on small molecules, proteins or polymers. In chemistry, language models contribute to accelerating the molecule discovery cycle as evidenced by promising recent findings in early-stage drug discovery. Here, we review the role of language models in molecular discovery, underlining their strength in de novo drug design, property prediction and reaction chemistry. We highlight valuable open-source software assets thus lowering the entry barrier to the field of scientific language modeling. Last, we sketch a vision for future molecular design that combines a chatbot interface with access to computational chemistry tools. Our contribution serves as a valuable resource for researchers, chemists, and AI enthusiasts interested in understanding how language models can and will be used to accelerate chemical discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_16235 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Language models in molecular discovery Janakarajan, Nikita Erdmann, Tim Swaminathan, Sarath Laino, Teodoro Born, Jannis Chemical Physics Artificial Intelligence Computation and Language Machine Learning Biomolecules The success of language models, especially transformer-based architectures, has trickled into other domains giving rise to "scientific language models" that operate on small molecules, proteins or polymers. In chemistry, language models contribute to accelerating the molecule discovery cycle as evidenced by promising recent findings in early-stage drug discovery. Here, we review the role of language models in molecular discovery, underlining their strength in de novo drug design, property prediction and reaction chemistry. We highlight valuable open-source software assets thus lowering the entry barrier to the field of scientific language modeling. Last, we sketch a vision for future molecular design that combines a chatbot interface with access to computational chemistry tools. Our contribution serves as a valuable resource for researchers, chemists, and AI enthusiasts interested in understanding how language models can and will be used to accelerate chemical discovery. |
| title | Language models in molecular discovery |
| topic | Chemical Physics Artificial Intelligence Computation and Language Machine Learning Biomolecules |
| url | https://arxiv.org/abs/2309.16235 |