Chemical and geometric representation fidelity improves drug--target affinity prediction

Yixiao Li; Yining Qian; Yefan Chen; Zenghui Chen; Jiayue Sun; Yuhai Zhao; Cheng Tan; An-Yang Lu

q-bio.BMSep 2, 2026

Chemical and geometric representation fidelity improves drug--target affinity prediction

Authors: Yixiao Li, Yining Qian, Yefan Chen, Zenghui Chen, Jiayue Sun, Yuhai Zhao, Cheng Tan, An-Yang Lu

Organizations: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China · School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China · School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China · Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China

Abstract

Predicting drug--target binding affinity (DTA) requires models to distinguish subtle chemical and structural determinants underlying molecular recognition. Although recent approaches increasingly incorporate richer drug and protein information, such information may be compressed, homogenized or discretized during representation construction, causing affinity-relevant distinctions to be lost before interaction modelling. We hypothesized that this representation-stage information loss constitutes an upstream bottleneck that cannot be reliably overcome by increasingly complex interaction predictors. To test this hypothesis, we developed ReGeoDTA, a representation-preserving framework that maintains affinity-relevant chemical heterogeneity in molecular representations and continuous geometric relationships in protein structures. Across three benchmark datasets, ReGeoDTA consistently improved affinity prediction, and the proposed representation-preserving strategies retained their benefits across diverse DTA architectures. Controlled representation degradation progressively reduced predictive performance, whereas increasing downstream predictor complexity failed to recover information lost during representation construction. These findings identify representation fidelity as an upstream design principle for accurate and generalizable drug--target affinity prediction, with potential implications for computational compound prioritization.

Figures & tables

Fig. 1: Overview of ReGeoDTA. A Position-Aware Protein Encoder (PAPE) with relative geometric positional encoding. B Protein positional information processing module for generating processed relative geometric information from 3D protein coordinates. C Drug graph encoder with GMFF-based node feature representation. D Gated Multi-Feature Fusion (GMFF) module for heterogeneous atomic feature integration. E Overall architecture of ReGeoDTA.

Dataset	Scenario	Method	MSE $\downarrow$ (Std)	CI $\uparrow$ (Std)	$R^{2}$ $\uparrow$ (Std)
Davis	Cold drug	GraphDTA [ 13 ]	0.920 (0.029)	0.678 (0.036)	0.160 (0.019)
		GEFA [ 29 ]	0.847 (0.012)	0.709 (0.028)	0.182 (0.015)
		FusionDTA [ 30 ]	0.581 (0.094)	0.737 (0.012)	0.187 (0.034)
		MGraphDTA [ 31 ]	0.563 (0.065)	0.729 (0.022)	0.192 (0.021)
		NHGNN [ 32 ]	0.554 (0.091)	0.752 (0.017)	0.207 (0.030)
		ReGeoDTA	0.546 (0.090)	0.744 (0.028)	0.255 (0.030)

Table 2

Fig. 2: Randomization tests. Performance comparison under four corrupted conditions.

Dataset	Experiments	MSE $\downarrow$	CI $\uparrow$	MAE $\downarrow$	$R^{2}\uparrow$	AUPR $\uparrow$
Davis	Full Model	0.186	0.906	0.211	0.756	0.807
	Exp. 1	0.187	0.904	0.211	0.755	0.806
	Exp. 2	0.188	0.901	0.212	0.751	0.807
	Exp. 3	0.200	0.895	0.222	0.722	0.782
	Exp. 4	0.212	0.895	0.232	0.689	0.775
	Exp. 5	0.195	0.899	0.218	0.724	0.786

Table 4

Fig. 3: Controlled representation degradation analysis across Davis, KIBA, and BindingDB. The complete ReGeoDTA model is used as the reference setting. Step 1 removes the interaction between aromaticity-related information and numerical atomic features; Step 2 removes numerical atomic descriptors; Step 3 removes aromaticity information and retains only atom-type information; Step 4 replaces the proposed drug encoding with conventional one-hot atom encoding; Step 5 replaces relative position-aware protein attention with contact-map-based attention; Step 6 removes protein positional information; and Step 7 replaces ProtTrans-derived protein representations with standard learnable embeddings. Lower MSE and higher CI,

R^{2}

, and AUPR indicate better performance. The overall degradation pattern suggests that preserving chemical heterogeneity, continuous protein geometry, and pretrained protein contextual information is important for affinity prediction.

Dataset	Initial Representation	Predictor	MSE $\downarrow$	CI $\uparrow$	$R^{2}\uparrow$	AUPR $\uparrow$
Davis	Enhanced initial representation	Linear	0.186	0.906	0.756	0.807
		Cross-attention	0.211	0.895	0.685	0.771
		LRBF	0.189	0.904	0.749	0.803
		PMF	0.184	0.907	0.761	0.809
	GIN-based initial representation	Linear	0.225	0.898	0.718	0.770
		Cross-attention	0.229	0.896	0.712	0.767

Table 6

Fig. 4: Generalization of our drug encoding strategy across different graph models. In the radar plots, values farther from the centre indicate better performance.

Fig. 5: Generalization of the position-aware encoder across baseline models. In the radar plots, values farther from the centre indicate better performance.

Fig. 6: Decomposition and structural localization of PAPE-derived attention in the ABL1–Nilotinib complex (PDB ID: 3CS9). Images 1–4 show the semantic, distance-aware, orientation-aware, and fused residue-pair attention maps, respectively, illustrating how the attention pattern changes as geometric information is incorporated. Image 5 evaluates the fused map against 22 annotated binding-site residues assigned to four structurally defined regions, with blue indicating high fused attention ( $\geq 0.35$ ) and red indicating lower fused attention ( $<0.35$ ). Image 6 analyses component complementarity: blue denotes other high-weight fused pairs, orange denotes high-weight pairs identified through positional complementarity, and green denotes high-weight pairs identified through semantic complementarity. The black open circle marks the representative M290–H361 long-range cross-region association.

Fig. 7: Attention, convolutional and graph-propagation maps represent different internal quantities and are presented for qualitative structural comparison (PDB ID: 3CS9). Image 1: Overall structure of ABL1. Image 2: Binding site with spatial functional groups. Image 3: Attention map from our proposed method. Image 4: Attention map from TransVAE. Image 5: Attention map from DeepTGIN. Image 6: Interaction map from CNN with a small convolution kernel. Image 7: Interaction map from CNN with a large convolution kernel. Image 8: Graph neural network propagation map under a low-threshold protein contact map. Image 9: Propagation map under a high-threshold contact map.

Fig. 8: Alignment between drug embedding similarity and multi-target affinity-profile similarity on the Davis dataset. Image 1: Alignment between functional and embedding similarity. Image 2: Bootstrap stability of spearman alignment. Image 3: Bootstrap stability of pearson alignment.

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

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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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