title: Language models in molecular discovery
summary: 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.
paper_authors._id: 6516330699e8060f1e88c0a0
paper_authors.name: Nikita Janakarajan
paper_authors._id: 6516330699e8060f1e88c0a1
paper_authors.name: Tim Erdmann
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-29T12:10:38.230Z
paper_authors._id: 64e792d03f5a5a3c9635ff3e
paper_authors.avatarUrl: /avatars/c1e308b217b291832a9b18381041d6cc.svg
paper_authors.fullname: Tim
paper_authors.type: user
paper_authors.user: timerdmann
paper_authors._id: 6516330699e8060f1e88c0a2
paper_authors.name: Sarath Swaminathan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-29T12:10:48.645Z
paper_authors._id: 63521353beef5fb9913b69c5
paper_authors.avatarUrl: /avatars/349c59f22f43e2b1aa4205a34ab2f824.svg
paper_authors.fullname: Sarath Swaminathan
paper_authors.type: user
paper_authors.user: sarathkrishna
paper_authors._id: 6516330699e8060f1e88c0a3
paper_authors.name: Teodoro Laino
paper_authors._id: 6516330699e8060f1e88c0a4
paper_authors.name: Jannis Born
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-29T12:11:08.211Z
paper_authors._id: 63305e87b68c7453d2e9a7e6
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63305e87b68c7453d2e9a7e6/OjSdIZxuAx4Q-aLDmgVVS.jpeg
paper_authors.fullname: Jannis Born
paper_authors.type: user
paper_authors.user: jannisborn
paper_title: Language models in molecular discovery
paper_summary: 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.
paper_ai_summary: Language models, particularly transformers, are used in molecular discovery, enhancing drug design, property prediction, and reaction chemistry with open-source tools, and envisioning future chatbot interfaces to computational chemistry.
paper_ai_keywords: language models transformer-based architectures scientific language models small molecules proteins polymers molecular discovery de novo drug design property prediction reaction chemistry chatbot interface computational chemistry tools