LaPro-DTA: Latent Dual-View Drug Representations and Salient Protein Feature Extraction for Generalizable Drug--Target Affinity Prediction

Zihan Dun; Liuyi Xu; An-Yang Lu; Shuang Li; Yining Qian

cs.LGMar 16, 2026

LaPro-DTA: Latent Dual-View Drug Representations and Salient Protein Feature Extraction for Generalizable Drug--Target Affinity Prediction

Authors: Zihan Dun, Liuyi Xu, An-Yang Lu, Shuang Li, Yining Qian

Organizations: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China · School of Artificial Intelligence, Beihang University, Beijing 100000, China · School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China

Abstract

Drug--target affinity prediction is pivotal for accelerating drug discovery, yet existing methods suffer from significant performance degradation in realistic cold-start scenarios (unseen drugs/targets/pairs), primarily driven by overfitting to training instances and information loss from irrelevant target sequences. In this paper, we propose LaPro-DTA, a framework designed to achieve robust and generalizable DTA prediction. To tackle overfitting, we devise a latent dual-view drug representation mechanism. It synergizes an instance-level view to capture fine-grained substructures with stochastic perturbation and a distribution-level view to distill generalized chemical scaffolds via semantic remapping, thereby enforcing the model to learn transferable structural rules rather than memorizing specific samples. To mitigate information loss, we introduce a salient protein feature extraction strategy using pattern-aware top- $k$ pooling, which effectively filters background noise and isolates high-response bioactive regions. Furthermore, a cross-view multi-head attention mechanism fuses these purified features to model comprehensive interactions. Extensive experiments on benchmark datasets demonstrate that LaPro-DTA significantly outperforms state-of-the-art methods, achieving an 8% MSE reduction on the Davis dataset in the challenging unseen-drug setting, while offering interpretable insights into binding mechanisms.

Figures & tables

Figure 1: Overview of the LaPro-DTA architecture. (a) Drug Representation: Extracts drug features using a dual-view mechanism to capture both instance-level and distribution-level view. (b) Target Representation: Extracts target features by selectively focusing on key bioactive segments rather than the whole sequence. (c) Feature Fusion and Prediction: Aligns drug and target features to capture fine-grained interactions between them. The red line denotes the DeCNN-to-shared-encoder route that generates the distribution-level view.

Dataset	Drugs	Targets	Interactions	Affinity metrics
KIBA	2,111	229	118,254	$K_{d},K_{i}\,and\,IC_{50}$
Davis	68	442	30,056	$K_{d}$

Table 1: Summary of the datasets used in our experiments.

Model	Davis				KIBA
Model	MSE (std) $\downarrow$	MAE (std) $\downarrow$	CI (std) $\uparrow$	$\mathbf{r_{m}^{2}}$ (std) $\uparrow$	MSE (std) $\downarrow$	MAE (std) $\downarrow$	CI (std) $\uparrow$	$\mathbf{r_{m}^{2}}$ (std) $\uparrow$
Setting: Unseen Drug
DeepDTA (2018)	0.745 (0.008)	0.704 (0.050)	0.699 (0.045)	0.078 (0.023)	0.478 (0.007)	0.485 (0.004)	0.688 (0.006)	0.263 (0.002)
AttentionDTA (2019)	0.656 (0.004)	0.622 (0.003)	0.682 (0.010)	0.104 (0.004)	0.433 (0.041)	0.432 (0.031)	0.722 (0.025)	0.322 (0.030)
Co-VAE (2021)	0.577 (0.009)	0.523 (0.001)	0.616 (0.005)	0.128 (0.014)	0.429 (0.019)	0.459 (0.041)	0.745 (0.010)	0.334 (0.019)
GraphDTA (2021)	0.590 (0.016)	0.552 (0.014)	0.695 (0.008)	0.077 (0.010)	0.473 (0.021)	0.526 (0.020)	0.738 (0.004)	0.282 (0.032)

Table 2: Performance comparison on Davis and KIBA datasets under cold-start scenarios. ( Best , Second Best ). Results are reported as mean (standard deviation) over five independent runs.

Figure 2: Ablation study on the Davis dataset.

Figure 3: Parameter sensitivity analysis on the Davis dataset.

Figure 4: Saliency visualization of molecules with and without distribution-level view integration, and t-SNE projection of learned drug representations. The distinct clustering of drugs sharing similar scaffolds demonstrates that the model effectively captures discriminative structural semantics, mapping chemically similar compounds into proximal latent regions.

PDBbind ID	Year	Affinity ( $pK_{d}$ )	Pred. Affinity
5txy	2017	5.460	5.476804
6qlp	2019	5.280	5.301821
5u0e	2017	5.110	5.134590
6i12	2019	5.570	5.541025
6ma2	2018	5.389	5.419472
6ajv	2019	5.039	5.130577

Table 3: Predictive performance comparison on recent PDBbind entries (all methods evaluated under identical conditions ).

Figure 5: A saliency visualization of the drug–target binding pocket, where colored regions indicate key residues identified by the model.

Table 4: Robustness analysis on recent PDBbind entries (SMILES variants). SMILES strings are truncated, denoted by “…”.

Appendix figures & tables6 assets

Supplementary material from the paper’s appendix.

Appendix

Drug Representation
Hyperparameter	Value
Embedding Dimension	128
CNN Filter Sizes	[4, 4, 4]
Number of Filters	128
DeconvNet Layers	3
Latent Dimension ( $z$ )	96

Appendix

Table 5: Hyperparameter settings of LaPro-DTA.

Figure 6: True affinity value against the predicted value on Davis and KIBA datasets.

Model	KIBA Dataset			Davis Dataset			BindingDB Dataset
Model	MSE $\downarrow$	CI $\uparrow$	$\mathbf{r_{m}^{2}}$ $\uparrow$	MSE $\downarrow$	CI $\uparrow$	$\mathbf{r_{m}^{2}}$ $\uparrow$	MSE $\downarrow$	CI $\uparrow$	$\mathbf{r_{m}^{2}}$ $\uparrow$
KronRLS (2015)	0.411	0.782	0.342	0.379	0.871	0.407	0.910	0.710	–
SimBoost (2017)	0.222	0.836	0.629	0.282	0.872	0.644	–	–	–
DeepDTA (2018)	0.194	0.863	0.630	0.261	0.878	0.630	0.633	0.844	0.633
WideDTA (2019)	0.179	0.875	–	0.262	0.886	–	–	–	–
AttentionDTA (2022)	0.155	0.882	0.755	0.216	0.887	0.677	0.745	0.542	–

Appendix

Table 6: Performance comparison of representative DTA models on KIBA, Davis, and BindingDB datasets. ( Best , Second Best ).

Components			Unseen Drug			Unseen Target			Unseen Pair
Dual	TopK	Fusion	CI ( $\uparrow$ )	MSE ( $\downarrow$ )	$\mathbf{r_{m}^{2}}$ ( $\uparrow$ )	CI ( $\uparrow$ )	MSE ( $\downarrow$ )	$\mathbf{r_{m}^{2}}$ ( $\uparrow$ )	CI ( $\uparrow$ )	MSE ( $\downarrow$ )	$\mathbf{r_{m}^{2}}$ ( $\uparrow$ )
–	–	–	0.723	0.567	0.137	0.808	0.654	0.368	0.728	0.836	0.104
✓	–	✓	0.736	0.532	0.181	0.798	0.548	0.453	0.739	0.750	0.150
–	✓	✓	0.726	0.542	0.201	0.755	0.752	0.302	0.739	0.804	0.124
✓	✓	–	0.654	0.646	0.032	0.742	0.856	0.191	0.664	0.909	0.025
✓	✓	✓	0.737	0.519	0.191	0.828	0.519	0.473	0.770	0.727	0.166

Appendix

Table 7: Ablation study of LaPro-DTA on the Davis dataset under three cold-start settings. Dual : Latent Dual-View Mechanism; TopK : top-

k

Pooling; Fusion : Cross-View Attention. The best results are highlighted in bold , and the second-best results are underlined . “

\uparrow

” indicates higher is better, “

\downarrow

” indicates lower is better.

Components			Metrics
Dual	TopK	Fusion	CI ( $\uparrow$ )	MSE ( $\downarrow$ )	$\mathbf{r_{m}^{2}}$ ( $\uparrow$ )
$\times$	$\times$	$\times$	0.854	0.347	0.559
$\times$	✓	$\times$	0.875	0.240	0.663
$\times$	$\times$	✓	0.885	0.262	0.598
$\times$	✓	✓	0.881	0.242	0.651
✓	$\times$	✓	0.880	0.258	0.607

Appendix

Table 8: Ablation study on the Davis dataset under the random split setting. Dual : latent dual-view (only instance-level view); TopK : top-

k

pooling; Fusion : cross-view attention.

Figure 7: Comparison of MSE, CI and

r_{m}^{2}

of LaPro-DTA with different K on the Davis dataset.

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Predicting drug-target affinity is fundamental to virtual screening and lead optimization. However, existing deep models often suffer from representation collapse in stringent cold-start regimes, where the scarcity of labels and domain shifts prevent the learning of transferable pharmacophores and binding motifs. In this paper, we propose Co-Diffusion, a novel affinity-aware framework that redefines DTA prediction as a constrained latent denoising process to enhance generalization. Co-Diffusion employs a two-stage paradigm: Stage I establishes an affinity-steered latent manifold by aligning drug and target embeddings under an explicit supervised objective, ensuring that the latent space reflects the intrinsic binding landscape. Stage II introduces modality-specific latent diffusion as a stochastic perturb-and-denoise regularizer, forcing the model to recover consistent affinity semantics from noisy structural representations. This approach effectively mitigates the reconstruction-regression conflict common in generative DTA models. Theoretically, we show that Co-Diffusion maximizes a variational lower bound on the joint likelihood of drug structures, protein sequences, and binding strength. Extensive experiments across multiple benchmarks demonstrate that Co-Diffusion significantly outperforms state-of-the-art baselines, particularly yielding superior zero-shot generalization on unseen molecular scaffolds and novel protein families-paving a robust path for in silico drug prioritization in unexplored chemical spaces.

Abstract

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