Probing optimisation in physics-informed neural networks
Nayara Fonseca, Veronica Guidetti, et al.
ICLR 2023
Large-scale molecular representation methods have revolutionized applications in material science, such as drug discovery, chemical modeling, and material design. With the rise of transformers, models now learn representations directly from molecular structures. In this study, we develop an encoder-decoder model based on BART that not only learns molecular representations but also auto-regressively generates molecules. Trained on SELFIES, a robust molecular string representation, our model outperforms existing baselines in downstream tasks, demonstrating its potential in efficient and effective molecular data analysis and manipulation.
Nayara Fonseca, Veronica Guidetti, et al.
ICLR 2023
Akihiro Kishimoto, Hiroshi Kajino, et al.
MRS Fall Meeting 2023
Lukas Heuberger, Daniel Messmer, et al.
Advanced Science
Simona Rabinovici-Cohen, Naomi Fridman, et al.
Cancers