  Accurately predicting drug-target binding affinity (DTA) in silico is a key
task in drug discovery. Most of the conventional DTA prediction methods are
simulation-based, which rely heavily on domain knowledge or the assumption of
having the 3D structure of the targets, which are often difficult to obtain.
Meanwhile, traditional machine learning-based methods apply various features
and descriptors, and simply depend on the similarities between drug-target
pairs. Recently, with the increasing amount of affinity data available and the
success of deep representation learning models on various domains, deep
learning techniques have been applied to DTA prediction. However, these methods
consider either label/one-hot encodings or the topological structure of
molecules, without considering the local chemical context of amino acids and
SMILES sequences. Motivated by this, we propose a novel end-to-end learning
framework, called DeepGS, which uses deep neural networks to extract the local
chemical context from amino acids and SMILES sequences, as well as the
molecular structure from the drugs. To assist the operations on the symbolic
data, we propose to use advanced embedding techniques (i.e., Smi2Vec and
Prot2Vec) to encode the amino acids and SMILES sequences to a distributed
representation. Meanwhile, we suggest a new molecular structure modeling
approach that works well under our framework. We have conducted extensive
experiments to compare our proposed method with state-of-the-art models
including KronRLS, SimBoost, DeepDTA and DeepCPI. Extensive experimental
results demonstrate the superiorities and competitiveness of DeepGS.
