13,098 research outputs found
ChemTS: An Efficient Python Library for de novo Molecular Generation
Automatic design of organic materials requires black-box optimization in a
vast chemical space. In conventional molecular design algorithms, a molecule is
built as a combination of predetermined fragments. Recently, deep neural
network models such as variational auto encoders (VAEs) and recurrent neural
networks (RNNs) are shown to be effective in de novo design of molecules
without any predetermined fragments. This paper presents a novel python library
ChemTS that explores the chemical space by combining Monte Carlo tree search
(MCTS) and an RNN. In a benchmarking problem of optimizing the octanol-water
partition coefficient and synthesizability, our algorithm showed superior
efficiency in finding high-scoring molecules. ChemTS is available at
https://github.com/tsudalab/ChemTS
PaperRobot: Incremental Draft Generation of Scientific Ideas
We present a PaperRobot who performs as an automatic research assistant by
(1) conducting deep understanding of a large collection of human-written papers
in a target domain and constructing comprehensive background knowledge graphs
(KGs); (2) creating new ideas by predicting links from the background KGs, by
combining graph attention and contextual text attention; (3) incrementally
writing some key elements of a new paper based on memory-attention networks:
from the input title along with predicted related entities to generate a paper
abstract, from the abstract to generate conclusion and future work, and finally
from future work to generate a title for a follow-on paper. Turing Tests, where
a biomedical domain expert is asked to compare a system output and a
human-authored string, show PaperRobot generated abstracts, conclusion and
future work sections, and new titles are chosen over human-written ones up to
30%, 24% and 12% of the time, respectively.Comment: 12 pages. Accepted by ACL 2019 Code and resource is available at
https://github.com/EagleW/PaperRobo
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