Active search in intensionally specified structured spaces

Oglic, Dino; Garnett, Roman; G�rtner, Thomas

Abstract

We consider an active search problem in intensionally specified structured spaces. The ultimate goal in this setting is to discover structures from structurally different partitions of a fixed but unknown target class. An example of such a process is that of computer-aided de novo drug design. In the past 20 years several Monte Carlo search heuristics have been developed for this process. Motivated by these hand-crafted search heuristics, we devise a Metropolis--Hastings sampling scheme where the acceptance probability is given by a probabilistic surrogate of the target property, modeled with a max entropy conditional model. The surrogate model is updated in each iteration upon the evaluation of a selected structure. The proposed approach is consistent and the empirical evidence indicates that it achieves a large structural variety of discovered targets.

Citation

Oglic, D., Garnett, R., & Gärtner, T. Active search in intensionally specified structured spaces. Presented at Thirty-First AAAI Conference (AAAI 17)

• Conference Name: Thirty-First AAAI Conference (AAAI 17)
• End Date: Feb 9, 2017
• Acceptance Date: Nov 11, 2016
• Publication Date: Feb 9, 2017
• Deposit Date: Dec 5, 2016
• Publicly Available Date: Feb 9, 2017
• Journal: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI 2017)
• Peer Reviewed: Peer Reviewed
• Public URL: https://nottingham-repository.worktribe.com/output/846697
• Publisher URL: http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14952
• Contract Date: Dec 5, 2016