@techreport{oai:ipsj.ixsq.nii.ac.jp:00220031,
 author = {Sheng, Hu and Ichigaku, Takigawa and Chuan, Xiao and Sheng, Hu and Ichigaku, Takigawa and Chuan, Xiao},
 issue = {33},
 month = {Sep},
 note = {We present a novel molecular graph generation method by auto-completing a privileged scaffold which represents a core graph substructure step-by-step. We propose a generative GNN model thus providing the ability to generate unseen molecular graphs outside the given training set. An edit-aware graph autocompletion paradigm that follows the “substructure-by-substructure” process is designed to complete the scaffold queries in multiple substructure adopt operations and allow meaningful edit operations to show the user's intention. Such operations enable the involvement of user decisions when interacting with a generative user-centered AI system, which differentiates our work from existing single-run generation paradigms. Particularly, a Monte Carlo tree search (MCTS) method is employed to satisfy the property requirements and navigate the search space when complying with users' edit operations. Moreover, we design a top-k ranking function which considers the preferences on popularity and diversity for different applications, such as query compositions for graph database and drug discovery respectively. Such techniques enable human experts to synergistically interact with the generative models grounded on large data., We present a novel molecular graph generation method by auto-completing a privileged scaffold which represents a core graph substructure step-by-step. We propose a generative GNN model thus providing the ability to generate unseen molecular graphs outside the given training set. An edit-aware graph autocompletion paradigm that follows the “substructure-by-substructure” process is designed to complete the scaffold queries in multiple substructure adopt operations and allow meaningful edit operations to show the user's intention. Such operations enable the involvement of user decisions when interacting with a generative user-centered AI system, which differentiates our work from existing single-run generation paradigms. Particularly, a Monte Carlo tree search (MCTS) method is employed to satisfy the property requirements and navigate the search space when complying with users' edit operations. Moreover, we design a top-k ranking function which considers the preferences on popularity and diversity for different applications, such as query compositions for graph database and drug discovery respectively. Such techniques enable human experts to synergistically interact with the generative models grounded on large data.},
 title = {User-Interactive Molecular Graph Suggestion for Drug Discovery},
 year = {2022}
}