HierDTA: Hierarchical Structure-Aware Representation Learning for Drug--Target Affinity Prediction

Huangchao Xu; Baohua Zhang; Qian Liu; Zhong Jin; Yi Du

cs.LGOct 19, 2026

HierDTA: Hierarchical Structure-Aware Representation Learning for Drug--Target Affinity Prediction

Authors: Huangchao Xu, Baohua Zhang, Qian Liu, Zhong Jin, Yi Du

Organizations: Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China · School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 101408, China

Abstract

Drug--target affinity (DTA) prediction plays an important role in computer-aided drug discovery, where reliable computational models can help prioritize compound--protein pairs before experimental validation. A key challenge in DTA prediction is to effectively mine multi-level molecular information from drug structures while accurately modeling the binding-site environment of target proteins. To address these issues, we introduce HierDTA, a hierarchical structure-aware framework for DTA prediction. \textcolor{black}{HierDTA represents each drug as an atom--motif heterogeneous graph and introduces an augmentation module that propagates functional-motif semantics to the corresponding atoms. For proteins, kinase-domain structures are converted into residue interaction graphs. The resulting atom and residue representations are integrated using cross-attention to capture pair-specific relationships, while molecule fingerprints and pretrained ESM-2 embeddings provide complementary global information. Compared with state-of-the-art methods, HierDTA achieves superior performance on the Davis dataset across all metrics and attains the highest $r_m^2$ of 0.791 on KIBA. It also exhibits strong generalization under cold-start settings, particularly on KIBA, where MSE is reduced by 21.7% in the unseen-protein scenario. Ablation study and interpretability analysis supported the contributions of the hierarchical augmentation and interaction components.} Case studies further showed that HierDTA provided early enrichment of candidate bioactive compounds. These results indicate that HierDTA is an effective framework for affinity prediction and target-specific virtual screening.

Figures & tables

Figure 1: Architecture of the proposed framework.(A) Encoders. Drug features are encoded from motif and atom graphs, with motif-derived context augmenting atom representations, while protein residue features are extracted from the kinase-domain structure. (B) Interaction. Atom and residue tokens are integrated through multi-head attention to capture fine-grained drug–protein interactions. (C) Global Representation. ECFP fingerprints and ESM-2 embeddings are projected and fused to obtain a global representation. (D) Prediction. Local and global interaction features are concatenated and fed into an MLP to predict binding affinity.

Name	Size
Atom node features	37
atom type (B, Br, C, Ca, Cl, F, H, I, N, Na, O, P, S)	13
atomic number	1
explicit valence (0–6)	7
total hydrogen count (0–4)	5
atom hybridization (SP, SP 2 , SP 3 , SP 3 D, SP 3 D 2 )	5

Table 1: Feature definition of the hierarchical molecular graph for drug representation.

Name	Size
Residue node features	41
solvent-accessible surface area (normalized)	1
phi angle ( $\phi$ )	1
psi angle ( $\psi$ )	1
one-hot encoded secondary structure	6
AAPHY7 physicochemical descriptors	7

Table 2: Feature definition of the residue interaction graph for protein representation.

Figure 2: Architecture of the motif-to-atom augmentation module. Motif representations are propagated to their constituent atoms through atom–motif membership edges and aggregated to obtain motif-derived semantic messages. For each atom, TLA operation uses the incoming atom representation as the query and attends to the motif context and atom message. The resulting augmentation message is incorporated into the atom representation through a residual connection before subsequent atom-level message passing.

Model	Davis			KIBA
Model	MSE $\downarrow$	CI $\uparrow$	$r_{m}^{2}\uparrow$	MSE $\downarrow$	CI $\uparrow$	$r_{m}^{2}\uparrow$
DeepDTA	$0.284\pm 0.008$	$0.869\pm 0.002$	$0.624\pm 0.015$	$0.201\pm 0.003$	$0.850\pm 0.002$	$0.671\pm 0.017$
GraphDTA	$0.242\pm 0.010$	$0.880\pm 0.001$	$0.683\pm 0.010$	$0.192\pm 0.001$	$0.848\pm 0.002$	$0.718\pm 0.004$
FusionDTA	$0.226\pm 0.005$	$0.891\pm 0.001$	$0.686\pm 0.020$	$0.155\pm 0.002$	$0.882\pm 0.001$	$0.750\pm 0.015$
MGraphDTA	$0.228\pm 0.004$	$0.883\pm 0.002$	$0.679\pm 0.022$	$0.152\pm 0.003$	$0.884\pm 0.002$	$0.766\pm 0.016$
MSGNN-DTA	$0.234\pm 0.005$	$0.890\pm 0.002$	$0.686\pm 0.014$	$0.149\pm 0.003$	$0.885\pm 0.002$	$0.763\pm 0.009$

Table 3: Performance comparison under the warm setting on the Davis and KIBA datasets. The best result for each metric is shown in bold, and the second-best result is underlined.

Panel of Figure 3 — Figure 3: Scatter plots of predicted vs true binding affinities on the Davis and KIBA test sets under the warm setting. Redder points indicate larger prediction errors, while blackr points indicate smaller errors.

Setting	Model	MSE $\downarrow$	CI $\uparrow$	$\boldsymbol{r_{m}^{2}}\uparrow$
Unseen drug	GraphDTA	$0.920\pm 0.029$	$0.678\pm 0.036$	$0.160\pm 0.019$
	FusionDTA	$0.581\pm 0.094$	$0.737\pm 0.012$	$0.187\pm 0.034$
	MGraphDTA	$0.563\pm 0.065$	$0.729\pm 0.022$	$0.192\pm 0.021$
	MSGNN-DTA	$0.560\pm 0.075$	$0.731\pm 0.036$	$0.186\pm 0.058$
	GRA-DTA	$0.578\pm 0.005$	$0.725\pm 0.015$	$0.121\pm 0.024$
	DMFF-DTA	$\mathbf{0.548\pm 0.063}$	$\mathbf{0.742\pm 0.037}$	$0.228\pm 0.073$

Table 4: Performance comparison under cold-start settings on the Davis dataset. The best results for each metric are shown in bold.

Setting	Model	MSE $\downarrow$	CI $\uparrow$	$\boldsymbol{r_{m}^{2}}\uparrow$
Unseen drug	GraphDTA	$0.471\pm 0.047$	$0.713\pm 0.002$	$0.342\pm 0.007$
	FusionDTA	$0.429\pm 0.031$	$0.748\pm 0.005$	$0.364\pm 0.012$
	MGraphDTA	$0.425\pm 0.047$	$0.746\pm 0.002$	$0.366\pm 0.016$
	MSGNN-DTA	$0.426\pm 0.043$	$0.747\pm 0.003$	$0.358\pm 0.022$
	GRA-DTA	$0.337\pm 0.007$	$0.765\pm 0.012$	$0.466\pm 0.024$
	DMFF-DTA	$0.408\pm 0.036$	$0.753\pm 0.009$	$0.397\pm 0.020$

Table 5: Performance comparison under cold-start settings on the KIBA dataset. The best results for each metric are shown in bold.

(A) Drug graph encoder
Configuration	MSE $\downarrow$	CI $\uparrow$	$\boldsymbol{r_{m}^{2}}\uparrow$
GAT	$\mathbf{0.174\pm 0.016}$	$\mathbf{0.905\pm 0.004}$	$\mathbf{0.757\pm 0.026}$
GCN	$0.183\pm 0.029$	$0.899\pm 0.005$	$0.744\pm 0.040$
GIN	$0.184\pm 0.022$	$0.903\pm 0.005$	$0.756\pm 0.027$
(B) Component
HierDTA	$\mathbf{0.174\pm 0.016}$	$\mathbf{0.905\pm 0.004}$	$\mathbf{0.757\pm 0.026}$

Table 6: Ablation results on the Davis dataset. The best result within each panel is shown in bold.

Figure 4: Comparison of the TLA and No TLA operation on the Davis dataset across the four evaluation settings: (a) MSE, (b) CI, and (c) $r_{m}^{2}$ . Lower MSE and higher CI and $r_{m}^{2}$ values are better.

Figure 5: Interpretability analysis of drug-protein complexes. (a) 1KV2 complex: The left panel shows the experimentally resolved 3D binding conformation, with key residues assigned high attention weights by HierDTA labeled. The right panel shows the atom-level attention heatmap over the ligand (darker colors indicate higher weights). (b) 1UWH complex: All subpanels have the same meanings as those in (a).

Figure 6: Motif attention weights from the first and second TLA operations for 1KV2 ligand. Values closer to one indicate greater reliance on the motif-derived context, whereas values closer to zero indicate greater reliance on the atom structural message.

Rank	Compound	Predicted KIBA	Vina Score (kcal/mol)
1	Dacomitinib	13.912	$-10.2$
2	Canertinib	13.801	$-9.2$
3	JAK3-inhibitor-I	13.792	$-7.9$
4	Afatinib	13.786	$-8.9$
5	Poziotinib	13.755	$-9.5$
6	Gefitinib	13.696	$-8.6$

Table 7: Top-10 unique DrugBank compounds ranked by HierDTA for EGFR (PDB ID: 3POZ). Boldface denotes established EGFR inhibitors.

Figure 7: Top- $k$ active compound hit rates of HierDTA and MSGNN-DTA on the external ChEMBL evaluation sets for (a) EGFR and (b) VEGFR2.

Protein-Ligand Binding Affinity Prediction

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

Yixiao Li, Yining Qian, Yefan Chen, Zenghui Chen+4

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+1

Privacy-Preserving Representation Learning Protein-Ligand Binding Affinity Prediction Representation Learning Molecular Representation Learning Geometric Representation Learning Protein Representation Learning

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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.

Abstract

Protein-Ligand Binding Affinity Prediction

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