  Simultaneous administration of multiple drugs can have synergistic or
antagonistic effects as one drug can affect activities of other drugs.
Synergistic effects lead to improved therapeutic outcomes, whereas,
antagonistic effects can be life-threatening, may lead to increased healthcare
cost, or may even cause death. Thus identification of unknown drug-drug
interaction (DDI) is an important concern for efficient and effective
healthcare. Although multiple resources for DDI exist, they are often unable to
keep pace with rich amount of information available in fast growing biomedical
texts. Most existing methods model DDI extraction from text as a classification
problem and mainly rely on handcrafted features. Some of these features further
depend on domain specific tools. Recently neural network models using latent
features have been shown to give similar or better performance than the other
existing models dependent on handcrafted features. In this paper, we present
three models namely, {\it B-LSTM}, {\it AB-LSTM} and {\it Joint AB-LSTM} based
on long short-term memory (LSTM) network. All three models utilize word and
position embedding as latent features and thus do not rely on explicit feature
engineering. Further use of bidirectional long short-term memory (Bi-LSTM)
networks allow implicit feature extraction from the whole sentence. The two
models, {\it AB-LSTM} and {\it Joint AB-LSTM} also use attentive pooling in the
output of Bi-LSTM layer to assign weights to features. Our experimental results
on the SemEval-2013 DDI extraction dataset show that the {\it Joint AB-LSTM}
model outperforms all the existing methods, including those relying on
handcrafted features. The other two proposed LSTM models also perform
competitively with state-of-the-art methods.
