To tackle this challenge, we introduce molecular hypergraphs and propose Molecular Hypergraph Neural Networks (MHNN) to predict the optoelectronic properties of organic semiconductors, where hyperedges represent conjugated structures. A general algorithm is designed for irregular high-order connections, which...
We propose a Molecular Hypergraph Convolutional Network (MolHGCN) that predicts the molecular properties of a molecule using the atom and functional group information as inputs. Molecules can contain many types of functional groups, which will affect the properties the molecules. For example, the to...
grammar, VAE, hypergraph, goal-directed optimization Github Multi-objective de novo drug design with conditional graph generative model Yibo Li, Liangren Zhang, Zhenming Liu Journal of Cheminformatics, 10 graph neural networks, distribution-learning, auto-regressive, conditional generation, ChEMBL ...
Liu and co-workers used persistent spectral hypergraph based molecular descriptors for their machine learning approaches to predict protein-ligand binding affinities. Vaisman and Masson have developed a computational geometry approach using Delaunay tessellations to capture the influence of the four nearest ...
Solutions to the handling of multi-valent bonds have been introduced via the use ofhypergraphs; in a hypergraph, edges are sets of at least two atoms (hyperedges) instead of tuples of atoms [37]. However, the use of hypergraphs is not further discussed here as they are not currently wide...