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Active search in intensionally specified structured spaces

Oglic, Dino; Garnett, Roman; Gärtner, Thomas

Authors

Dino Oglic dino.oglic@nottingham.ac.uk

Roman Garnett garnett@wustl.edu

Thomas Gärtner



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.

Publication Date Feb 9, 2017
Journal Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI 2017)
Peer Reviewed Peer Reviewed
APA6 Citation Oglic, D., Garnett, R., & Gärtner, T. (2017). Active search in intensionally specified structured spaces
Publisher URL http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14952
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf

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active-search-in-intensionally-specified-structured-spaces.pdf (1.5 Mb)
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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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