5 papers
Mixing Vector Model for Copolymer Inference via Mixed Integer Linear Programming
Jianshen Zhu, Raveena Rai, Taiyo Sohkawa +4
A novel two-phase molecule inference framework, mol-infer, has recently been developed to infer chemical graphs with prescribed abstract structures and desired property values thro…
Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Model
Jianshen Zhu, Naveed Ahmed Azam, Kazuya Haraguchi +2
Recently, a novel two-phase framework named mol-infer for inference of chemical compounds with prescribed abstract structures and desired property values has been proposed. The fra…
Towards Environment-Sensitive Molecular Inference via Mixed Integer Linear Programming
Jianshen Zhu, Mao Takekida, Naveed Ahmed Azam +3
Traditional QSAR/QSPR and inverse QSAR/QSPR methods often assume that chemical properties are dictated by single molecules, overlooking the influence of molecular interactions and…
A Unified Approach to Inferring Chemical Compounds with the Desired Aqueous Solubility
Muniba Batool, Naveed Ahmed Azam, Jianshen Zhu +3
Aqueous solubility (AS) is a key physiochemical property that plays a crucial role in drug discovery and material design. We report a novel unified approach to predict and infer ch…
Cycle-Configuration: A Novel Graph-theoretic Descriptor Set for Molecular Inference
Bowen Song, Jianshen Zhu, Naveed Ahmed Azam +3
In this paper, we propose a novel family of descriptors of chemical graphs, named cycle-configuration (CC), that can be used in the standard "two-layered (2L) model" of mol-infer,…