5 papers
Projecting Molecules into Synthesizable Chemical Spaces
Shitong Luo, Wenhao Gao, Zuofan Wu +3
Discovering new drug molecules is a pivotal yet challenging process due to the near-infinitely large chemical space and notorious demands on time and resources. Numerous generative…
Efficient and Programmable Exploration of Synthesizable Chemical Space
Shitong Luo, Connor W. Coley
The constrained nature of synthesizable chemical space poses a significant challenge for sampling molecules that are both synthetically accessible and possess desired properties. I…
Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
Xingang Peng, Shitong Luo, Jiaqi Guan +3
Deep generative models have achieved tremendous success in designing novel drug molecules in recent years. A new thread of works have shown the great potential in advancing the spe…
Orientation-Aware Graph Neural Networks for Protein Structure Representation Learning
Jiahan Li, Shitong Luo, Congyue Deng +5
By folding into particular 3D structures, proteins play a key role in living beings. To learn meaningful representation from a protein structure for downstream tasks, not only the…
Generative Artificial Intelligence for Navigating Synthesizable Chemical Space
Wenhao Gao, Shitong Luo, Connor W. Coley
We introduce SynFormer, a generative modeling framework designed to efficiently explore and navigate synthesizable chemical space. Unlike traditional molecular generation approache…