Drug-target relation prediction supports candidate screening, drug repositioning, and mechanism analysis.
Existing models often use incomplete drug or protein representations, model cross-modal interactions shallowly, or train affinity regression and interaction classification separately, although these tasks describe closely related views of the same drug-target pair.
DPTM-DT: A Dual-Pretrained Transformer Framework for Multitask Drug-Target Prediction
This paper presents DPTM-DT, a dual-pretrained Transformer framework for multitask drug-target prediction.
DPTM-DT combines GROVER molecular graph embeddings, ESM protein language-model embeddings, and CTD physicochemical descriptors, then exchanges drug-target information through bidirectional cross-modal attention.
A shared pair representation is used for continuous affinity regression, high-affinity binary classification, and six-level affinity classification.
Experiments and Results
Experiments on Davis and KIBA cover random 80/20 and DeepDTA-style standard splits.
On the random 80/20 split, DPTM-DT achieves MSE/CI values of 0.193/0.917 on Davis and 0.120/0.918 on KIBA.
It also reports binary AUPR/MCC values of 0.727/0.654 and 0.798/0.689, and six-class Macro-F1/Top-2 values of 0.800/0.932 and 0.815/0.962 on Davis and KIBA, respectively.
Across the reported regression, binary classification, and multiclass classification settings, DPTM-DT achieves the best overall performance among the compared methods.
Results under the standard split show the same relative trend.
Contribution Analysis
Ablations indicate that dual target representation, gated fusion, and cross-modal attention each contribute to the final performance.
Availability
Code and supplementary materials are available at: anonymous.4open.science/r/DPCM-DT-74E0.