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Active search in intensionally specified structured spaces

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

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Authors

Dino Oglic

Roman Garnett

Thomas G�rtner



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. (2017). Active search in intensionally specified structured spaces.

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

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