Molecular representations play critical roles in researching drug design and properties, and effective methods are beneficial to assisting in the calculation of molecules and solving related problem in drug discovery. In previous years, most of the traditional molecular representations are based on hand-crafted features and rely heavily on biological experimentations, which are often costly and time consuming. However, recent researches achieve promising results using machine learning on various domains. In this article, we present a novel method named Smi2Vec-BiGRU that is designed for learning atoms and solving the single- and multitask binary classification problems in the field of drug discovery, which are the basic and also key problems in this field. Specifically, our approach transforms the molecule data in the SMILES format into a set of sample vectors and then feeds them into the bidirectional gated recurrent unit neural networks for training, which learns low-dimensional vector representations for molecular drug. We conduct extensive experiments on several widely used benchmarks including Tox21, SIDER and ClinTox. The experimental results show that our approach can achieve state-of-the-art performance on these benchmarking datasets, demonstrating the feasibility and competitiveness of our proposed approach.
Learning user’s preference from check-in data is
important for POI recommendation. Yet, a user
usually has visited some POIs while most of POIs
are unvisited (i.e., negative samples). To leverage
these “no-behavior” POIs, a typical approach
is pairwise ranking, which constructs ranking pairs
for the user and POIs. Although this approach is
generally effective, the negative samples in ranking
pairs are obtained randomly, which may fail to
leverage “critical” negative samples in the model
training. On the other hand, previous studies also
utilized geographical feature to improve the recommendation
quality. Nevertheless, most of previous
works did not exploit geographical information
comprehensively, which may also affect the performance.
To alleviate these issues, we propose a geographical
information based adversarial learning
model (Geo-ALM), which can be viewed as a fusion
of geographic features and generative adversarial
networks. Its core idea is to learn the discriminator
and generator interactively, by exploiting two
granularity of geographic features (i.e., region and
POI features). Experimental results show that Geo-
ALM can achieve competitive performance, compared
to several state-of-the-arts.
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