Adaptive Online Replanning with Diffusion Models
Siyuan Zhou, Yilun Du, et al.
NeurIPS 2023
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.
Siyuan Zhou, Yilun Du, et al.
NeurIPS 2023
Erik Miehling, Rahul Nair, et al.
NeurIPS 2023
C.A. Micchelli, W.L. Miranker
Journal of the ACM
Kenneth L. Clarkson, Elad Hazan, et al.
Journal of the ACM