  Detecting probable Drug Target Interaction (DTI) is a critical task in drug
discovery. Conventional DTI studies are expensive, labor-intensive, and take a
lot of time, hence there are significant reasons to construct useful
computational techniques that may successfully anticipate possible DTIs.
Although certain methods have been developed for this cause, numerous
interactions are yet to be discovered, and prediction accuracy is still low. To
meet these challenges, we propose a DTI prediction model built on molecular
structure of drugs and sequence of target proteins. In the proposed model, we
use Simplified Molecular Input Line Entry System (SMILES) to create CDK
descriptors, Molecular ACCess System (MACCS) fingerprints, Electrotopological
state (Estate) fingerprints and amino acid sequences of targets to get Pseudo
Amino Acid Composition (PseAAC). We target to evaluate performance of DTI
prediction models using CDK descriptors. For comparison, we use benchmark data
and evaluate models performance on two widely used fingerprints, MACCS
fingerprints and Estate fingerprints. The evaluation of performances shows that
CDK descriptors are superior at predicting DTIs. The proposed method also
outperforms other previously published techniques significantly.
