TEFDTA , a combined encoder and fingerprint transformation model; aimed to study the covalent-bonds interactions rather than only focusing on non-covalent bonds. At first, FASTA sequences of the proteins are label encoded, go through an embedding layer, and then captured data go through several 1D-CNNs. While drugs' SMILES sequences are converted to MACCS fingerprints, to an embedding layer with position encoding employed, and finally features are extracted by the encoder. At the end, concatenated predictions are fed into an FCNN that generates predicted DTA values. TEFDTA was able to sensitively predict DTAs with minor structural variations.