Search Results for author:
Found 7 papers, 3 papers with code
PepGB: Facilitating peptide drug discovery via graph neural networks
In summary, our proposed frameworks can serve as potent tools to facilitate peptide early drug discovery.
From Static to Dynamic Structures: Improving Binding Affinity Prediction with a Graph-Based Deep Learning Model
Accurate prediction of the protein-ligand binding affinities is an essential challenge in the structure-based drug design.
KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction
In this paper, we argue that there exist two major issues hindering current self-supervised learning methods from obtaining desired performance on molecular property prediction, that is, the ill-defined pre-training tasks and the limited model capacity.
Graph Representation Learning
Molecular Property Prediction
+2
MS$^2$-Transformer: An End-to-End Model for MS/MS-assisted Molecule Identification
Manually interpreting the MS/MS spectrum into the molecules (i. e., the simplified molecular-input line-entry system, SMILES) is often costly and cumbersome, mainly due to the synthesis and labeling of isotopes and the requirement of expert knowledge.
BERE: An accurate distantly supervised biomedical entity relation extraction network
Automated entity relation extraction (RE) from literature provides an important source for constructing biomedical database, which is more efficient and extensible than manual curation.
DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM
Particle picking is a time-consuming step in single-particle analysis and often requires significant interventions from users, which has become a bottleneck for future automated electron cryo-microscopy (cryo-EM).
Computational Protein Design Using AND/OR Branch-and-Bound Search
The computation of the global minimum energy conformation (GMEC) is an important and challenging topic in structure-based computational protein design.
