Co-Diffusion: An Affinity-Aware Two-Stage Latent Diffusion Framework for Generalizable Drug-Target Affinity Prediction

Yining Qian; Pengjie Wang; Yixiao Li; An-Yang Lu; Cheng Tan; Shuang Li; Lijun Liu

stat.MLMar 11, 2026

Co-Diffusion: An Affinity-Aware Two-Stage Latent Diffusion Framework for Generalizable Drug-Target Affinity Prediction

Authors: Yining Qian, Pengjie Wang, Yixiao Li, An-Yang Lu, Cheng Tan, Shuang Li, Lijun Liu

Organizations: School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China · College of Information Science and Engineering, Northeastern University, Shenyang 110819, China · Westlake University, Hangzhou 310000, China · School of Artificial Intelligence, Beihang University, Beijing 100000, China · Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern University, Shenyang 110169, China

Abstract

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.

Figures & tables

Fig. 1 : Overview of the drug discovery process. The proposed Co-Diffusion framework operates at the predictive modeling stage, providing robust affinity estimates to guide the selection of candidates before in vitro testing.

Fig. 2 : Latent Diffusion Process in Training and Inference. (a) In training, the VAE encoder produces latent $\mathbf{z}_{0}$ , which is perturbed to $\mathbf{z}_{t}$ by adding noise. A UNet is trained to predict the noise, following the Noise-Prediction Training Objective . (b) In inference, the reverse denoising process transforms sampled noise $\mathbf{z}_{T}$ back to $\mathbf{z}_{0}$ , which is decoded to generate the target sequence, as described in Generation .

Fig. 3 : The graphical structure for Co-Diffusion model.

Dataset	Targets	Drugs	Binding pairs
KIBA	229	2111	118,254
Davis	442	68	30,056

TABLE I : Statistics of the benchmark datasets used in this study.

Panel of Figure 6 — Fig. 5 : Performance comparison on Davis and KIBA under random split.

Dataset	Settings	Method	MSE(std) $\downarrow$	MAE(std) $\downarrow$	CI(std) $\uparrow$	$r_{m}^{2}$ $\uparrow$
Davis	Unseen-drug	DeepDTA	0.745 (0.008)	0.704 (0.050)	0.699 (0.045)	0.078
		AttentionDTA	0.656 (0.004)	0.622 (0.003)	0.682 (0.010)	0.104
		GraphDTA	0.590 (0.016)	0.552 (0.014)	0.695 (0.008)	0.077
		Co-VAE	0.577 (0.009)	0.523 (0.014)	0.616 (0.005)	0.128
		TransVAE-DTA	0.643 (0.012)	0.542 (0.009)	0.543 (0.010)	0.007
		Pair-VAE	0.570 (0.004)	0.498 (0.007)	0.730 (0.003)	0.134

TABLE II: Performance Comparison on Davis and KIBA Datasets Under Cold-start Scenarios.

Panel of Figure 8 — Fig. 6 : Scatter plots of predicted versus actual affinity on Davis (top) and KIBA (bottom) under Unseen Drug, Unseen Target, and Unseen Pair. The dashed line denotes the identity line.

Settings	Method	MSE $\downarrow$	MAE $\downarrow$	CI(std) $\uparrow$	$r_{m}^{2}$ $\uparrow$
Unseen Drug	Co-Diffusion	0.568	0.471	0.722	0.166
	Drug-Diffusion	0.580	0.498	0.630	0.122
	Target-Diffusion	0.617	0.537	0.636	0.122
	No-diffusion	0.571	0.519	0.619	0.131
Unseen Target	Co-Diffusion	0.556	0.429	0.836	0.461
	Drug-Diffusion	0.542	0.482	0.821	0.457

TABLE III : Ablation test of diffusion components under cold-start scenarios on the Davis dataset.

Setting	Method (Co-Diffusion)	KIBA				Davis
Setting	Method (Co-Diffusion)	MSE $\downarrow$	MAE $\downarrow$	CI $\uparrow$	RM2 $\uparrow$	MSE $\downarrow$	MAE $\downarrow$	CI $\uparrow$	$r_{m}^{2}$ $\uparrow$
Unseen Drug	end-to-end	0.537	0.490	0.686	0.256	0.622	0.509	0.692	0.127
Unseen Drug	two-stage	0.421	0.406	0.751	0.402	0.568	0.471	0.722	0.166
Unseen Target	end-to-end	0.405	0.439	0.632	0.290	0.522	0.413	0.830	0.496
Unseen Target	two-stage	0.347	0.383	0.685	0.377	0.556	0.429	0.836	0.461
Unseen Pair	end-to-end	0.488	0.505	0.597	0.135	0.858	0.570	0.693	0.121

TABLE IV : Comparison of Co-Diffusion trained with an affinity-aware two-stage regimen versus an end-to-end variant across cold-start scenarios on KIBA and Davis.

$T$	Setting	Davis		KIBA
$T$	Setting	MAE $\downarrow$	$r_{m}^{2}$ $\uparrow$	MAE $\downarrow$	$r_{m}^{2}$ $\uparrow$
800	Unseen Pair	0.5487	0.1401	0.4669	0.2074
	Unseen Drug	0.4709	0.1614	0.4096	0.4011
	Unseen Target	0.4311	0.4477	0.3953	0.3801
1000	Unseen Pair	0.5510	0.1491	0.4591	0.2011
	Unseen Drug	0.4710	0.1660	0.4065	0.4016

TABLE V : Sensitivity of Co-Diffusion to the number of diffusion steps

T

on Davis and KIBA.

Beta	Setting	Davis		KIBA
Beta	Setting	MAE $\downarrow$	$r_{m}^{2}$ $\uparrow$	MAE $\downarrow$	$r_{m}^{2}$ $\uparrow$
$[1\mathrm{e}{-5},\,4\mathrm{e}{-5}]$	Unseen Pair	0.5842	0.1408	0.4713	0.1986
	Unseen Drug	0.5100	0.1612	0.4062	0.3967
	Unseen Target	0.4845	0.4317	0.4048	0.3557
$[1\mathrm{e}{-4},\,4\mathrm{e}{-4}]$	Unseen Pair	0.5510	0.1491	0.4591	0.2011
	Unseen Drug	0.4710	0.1660	0.4065	0.4016

TABLE VI : Sensitivity of Co-Diffusion to the number of noise magnitude

beta

on Davis and KIBA.

Panel of Figure 13 — Fig. 7 : t-SNE projections of new (blue) vs. original (green) drug and target embeddings on KIBA (a–f) and Davis (g–l) under unseen drug, unseen target, and unseen pair scenarios.

ID	Year	True	Pred.	MSE ( $\downarrow$ )
6j0g	2019	5.640	5.609	0.001
6ce6	2018	5.100	5.162	0.004
5zyg	2019	5.210	5.277	0.005
6qlp	2019	5.280	5.349	0.005
6s56	2019	5.100	5.182	0.007
5xpi	2018	5.280	5.189	0.008

TABLE VII : Out-of-sample results on recent PDBbind entries.

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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.

Abstract

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