Machine learning-aided generative molecular design
Yuanqi Du(Cornell University), Tom L. Blundell(University of Cambridge), Tianfan Fu(Georgia Institute of Technology), Arian R. Jamasb(University of Cambridge), Philippe Schwaller(École Polytechnique Fédérale de Lausanne), Chenru Duan(DeepBiome Therapeutics (United States)), Charles B. Harris(University of Cambridge), Yingheng Wang(Cornell University), Píetro Lió(University of Cambridge), Jeff Guo(NCCR Chemical Biology - Visualisation and Control of Biological Processes Using Chemistry)
Cited by 142
Related Papers
Comparative Protein Modelling by Satisfaction of Spatial Restraints
|Journal of Molecular Biology|1993|13.2k
pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures
|Journal of Medicinal Chemistry|2015|5.3k
Scientific discovery in the age of artificial intelligence
|Nature|2023|1.6k
FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties11Edited by B. Honig
|Journal of Molecular Biology|2001|1.2k
mCSM: predicting the effects of mutations in proteins using graph-based signatures
|Bioinformatics|2013|1.1k