DeepSADR: Deep Transfer Learning with Subsequence Interaction and Adaptive Readout for Cancer Drug Response Prediction
Authors: Yuanpeng Zhang
Zhijian Huang
Ziyu Fan
Siyuan Shen
Yahan Li
Shangqian Wu
Min Wu
Lei Deng
Venue: ICLR 2026 Poster (2026)
Keywords: adaptive readout; subsequence interaction;drug response;cancer patient
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Cancer treatment efficacy exhibits high inter-patient heterogeneity due to genomic variations. While large-scale in vitro drug response data from cancer cell lines exist, predicting patient drug responses remains challenging due to genomic distribution shifts and the scarcity of clinical response data. Existing transfer learning methods primarily align global genomic features between cell lines and patients. However, they often ignore two critical aspects. First, drug response depends on specific drug substructures and genomic pathways. Second, drug response mechanisms differ in vitro and in vivo settings due to factors such as the immune system and tumor microenvironment. To address these limitations, we propose DeepSADR, a novel deep transfer learning framework for enhanced drug response prediction based on subsequence interaction and adaptive readout. In particular, DeepSADR models drug responses as interpretable bipartite interaction graphs between drug substructures and enriched genomic pathways. Subsequently, a supervised graph autoencoder was designed to capture latent interactions between drugs and gene subsequences within these interaction graphs. In addition, DeepSADR treats the drug response process as a transferable domain. A Set Transformer-based adaptive readout (AR) function learns domain-invariant response representations, enabling effective knowledge transfer from abundant cell line data to scarce patient data. Extensive experiments on clinical patient cohorts demonstrate that DeepSADR significantly outperforms state-of-the-art methods, and ablation experiments have validated the effectiveness of each module.

Published as a conference paper at ICLR 2026
DEEPSADR: DEEP TRANSFER LEARNING WITH SUB- SEQUENCE INTERACTION AND ADAPTIVE READOUT FOR CANCER DRUG RESPONSE PREDICTION
Yuanpeng Zhang', Zhijian Huang", Ziyu Siyuan Shen', Yahan Li", Shangqian Wu', Min Wuz.*, Lei Deng!.*
School of Computer Science and Engineering, Central South University, ChangSha, China
Institute for Infocomm Research (FR)), A*STAR, Singapore
(yuanpengzhamg Leideng] edu, en, wuminêa aatinnnaaaaar....m edu. sg *Corresponding author
ABSTRACT
Cancer treatment efficacy exhibits high inter- iiter-pateent heterogeneity due to genomic variations. While large-scale in vitro drug response data from cancer cell lines ex- ist, predicting patient drug responses remains challenging due to genomic distri- bution shifts and the scarcity of clinical response data. Existing transfer learning methods primarily align global genomic features between cell lines and patients. However, they often ignore two critical aspects. First, drug response depends on specific drug substructures and genomic funetion subsequences. Second, drug re- sponse mechanisms differ in vitro and in vivo settings due to factors such as the immune system and tumor microenvironment. To address these limitations, we propose DeepSADR, a novel deep transfer learning framework for enhanced drug response prediction based on subsequence interaction and adaptive readout. In particular, DeepSADR models drug responses as interpretable bipartite interac- tion graphs between drug substructures and genomic function subsequences. Sub- sequently, supervised graph autoencoder was designed to capture latent interac- tions between drugs and gene subsequences within these interaction graphs. addition, DeepSADe ADR treats the drug response process as transferable domain. A Set Transformer- based adaptive readout (AR) function learns domain-invarte re- sponse representations, enabling effective knowledge transfer from abundant cell line data to scarce patient data. Extensive experiments on clinical patient cohorts demonstrate that DeepSADR significantly outperforms state-of-the-art methods, and ablation experiments have validated the effectiveness of each module.
INTRODUCTION
Cancer the leading cause of morbidity and mortality worldwide (Fan et al, 2019). Genomic het- erogeneity drives strong variability in drug response across patients, necessitating precise predictive models for personalized therapy. To aid in treatment development, large-scale studies have been con- ducted globally, such the Cancer Genome Atlas (TCGA) database (Hutter & Zenklusen, 2018) to compile high-dimensional genomic information from cancer patients. However, patient drug re- sponse data current databases are extremely scarce, primarily due limited patient cohorts and the fact that each patient typically receives only few drugs. For example, TCGA contains only 500 patient drug response cases (Sharifi-Noghabi al. 2020p. To overcome this limitation, researchers often rely on pre-clinical datasets, especially cancer cell lines. Cell lines are derived from patient tumors and cloned to maintain stable genomic profiles. These cloned cells can be exposed to many different drugs. This allows researchers to collect drug response data across multiple drugs within the same cell line. Such data is highly valuable as it cannot be collected directly from patients due to the risks of administering multiple drugs concurrently. Although currently constrained to roughly 1,000 cell lines and a limited number of drugs, it nonetheless provides an essential foundation for developing personalized drug response models based on genomic information.

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Currently, many researchers have proposed predictive models based drug- cell line sponse data, such as DeepCovDR (Huang et al. 2023), GraphCDR (Liu et al. 2022) and Deep- ExpDR (Zhang et al., ) which deep learning models. These deep learning models have shown strong performance in predicting drug responses in cancer cell lines. However, studies have shown that such models often fail to accurately predict drug efficacy in patients (Seyhan (009h). One major reason is the distributional gap between cell lines and patient data. Genomic profiles of cell lines (99) are more homogeneous than those of patients (C*). This results in distinct distribu- tions of genomic information (P(g") & P) As shown in the t-SNE visualization in Appendix (Figure 4 the distributions of cell lines and patients differ significantly. As such, they can be regarded as coming from different domains (see Appendix A.2)
To address these challenges, researchers have developed various drug response models based on domain adaptation and transfer learning. These methods attempt to bridge this distributional gap typically learn domain-invariant feature representations shared between cell lines (source domain) and patients (target domain). Despite their progress, existing approaches still face important limi- tations. First, they often ignore certain important functional fragments. Treating drugs and genes monolithic entities overlooks key drivers, such as drug pharmacophores and enriched genomic pathways (e.g., apoptosis). For instance, variations in tumor suppressor genes (TSGs) are major contributors to paclitaxel resistance across cancers (Xu al., (2016). Similarly, the benzodiazepine scaffold in the anticancer drug Devazepide is active against opioid receptors and other protein tar- gets (Marsters Jr et al., 1994). Second, many methods focus solely on the distribution differences in genomic characteristics between cell lines and patients, yet overlook a critical biological fact: there are systemic differences in the mechanisms by which drugs respond in vitro cell lines versus in vivo patient environments. These differences stem from factors such as the tumor microenvironment, the immune system, and systemic physiological factors elements that cannot be fully replicated in cell line models (Seyhan 2019a).
In this paper, we propose DeepSADR, a transfer learning model for drug response prediction from cell lines to patients, built on subsequence interaction and adaptive readout. DeepSADR adopts pre- training and fine-tuning strategy. It constructs subsequence interaction graphs to capture associations between drug and gene subsequences, which improves both performance and interpretability. To enable effective knowledge transfer the drug response domain, we introduce an adaptive readout features, thus enhancing the model's predictive performance
function that learns on patient data. The contributions of this work are summarized as follows.
We model drug responses as bipartite interaction graphs between drug subsequences and gene subsequences. A supervised graph autoencoder is then used to capture their complex associations in an interpretable way.
We propose an adaptive readout based on the ensemble transformer architecture that effec- tively aggregates node features from the subsequence interaction graph into a global drug response representation. During fine-tuning, we also incorporate pre- pre-trained drug response embeddings to enhance features and learn overcomes the limitations of standard pooling functions in graph transfer learning.
representations. This design
We treat the entire drug response biological process with cell lines/patients as a distinct domain for transfer learning, moving beyond simple genomic feature alignment to better address inherent biological differences.
Extensive experiments show that DeepSADR significantly improves drug response predic- tion performance (AUC/AUPR) on scarce clinical patient data and provides interpretability through interaction visualization.
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RELATED WORK
2.1 DRUG RESPONSE PREDICTION
Currently, drug response prediction (DRP) models for patients mainly focus on transfer learning between cell lines (source domain) and patients (target domain). These methods can be classified as inductive, transductive, or unsupervised, depending on whether labeled patient data is used. In-
ductive methods include drug2tme (7hai & Liu 2024), PREDICT-AI (Jayagopal et al. 2024 and
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GANDALF Jayagopal et al. 2025b), which utilize both labeled cell lines and patient samples to capture differences in label distributions between the two domains through Deep learning. However, this approach heavily relies on labeled patient data, which is often difficult, expensive, and scarce in clinical practice. A few methods employ unsupervised approaches, such as CODE-AE (He et al.) 2022), which uses unlabeled cell lines and patient data for pre-training. Transduction- methods include TUGDA (Peres da Silva et al. 2021). WISER (Shubham et al. (024)) and PANCDR (Kim al.) 2024), which utilize labeled cell lines and unlabeled patient samples. Inductive and trans- ductive methods are currently the most widely used approaches. These methods primarily aim to learn shared representation spaces across domains, thereby mitigating the distributional differences between cell lines and patient data. While shared representations can capture similarities across different domains and improve model predictive performance to some extent, these methods do not adequately account for gene data fragments and drug substructures that play crucial role in drug re- sponses, nor do they sufficiently consider the distributional differences in the biological mechanisms underlying drug responses (arioesssrruuttieieeeeeee responses are influenced by numerous biological factors). whiic is critical for predicting drug responses in patients.
2.2 SUBSEQUENCE SEGMENTATION
In recent years, subsequence segmentation methods have been applied in many fields of bioinfor- matics and machine learning. For example, explainable sub-structure fingerprinting (ESFP) is gubstructure-bassee fingerprint representation method (Huang et al., 2019) that constructs molecular fingerprints by identifying and quantifying specific sub-structures in molecules, which is helpful for drug and protein research. Conventional DRP models typically extract features from drug and gene transcription information separately and then combine them as features for drug responses. This ap- proach is overly simplistic and fails to consider the drug/gene subsequences that play a crucial role in drug responses. This paper innovatively combines gene transcription subsequences with drug SMILES subsequences, transforming the entire drug response process into a subsequence interac- tion graph. Drug/Gene subsequences serve as nodes in this graph, and the features of the entire subsequence interaction graph are used as features for drug responses.
2.3 ADAPTIVE READOUT
The readout function is a critical component in Graph Neural (etN)rk(GNN) for processing graph- level tasks, as it transforms node representations into graph representations. Common readout func- tions include simple ones such as summation, averaging, and maximum values. We transform the drug response process into subsequence interactions graph, selecting appropriate readout function is critical to the final performance of the transfer learning model. The Set Transformer is Transformer model designed for set-based data, which handles unordered inputs through per- mutation invariance and is suitable for tasks where the order of elements is irrelevant. Therefore, designed an adaptive readout function based the Set Transformer concept. We incorporated the embedded vectors output by the pre-trained model function into the model fine-tuning stage enhance features, thereby overcoming the limitations of traditional readout functions in transfer learning for patient drug response data.
PROPOSED METHOD
3.1 PROBLEM DEFINITION
Suppose there are No labeled drug-cell line genomic profiles(g*) response data and N, labeled drug- patient genomic profiles(GP) response data. In general, N: >> N₂. Let D = {dz, d2 dz, dx J be the set of N drugs with labeled drug responses, where (e(c,, gJ) denotes the corresponding response of drug d, to a cell lines genomic profile oe E Ge, and (dl ge) be the drug response for patients of E Gp. Note that Ve(d,, (d,, g E {1,0} where indicates a positive response, indicates negative response. The primary goal of our work is leverage large-scale labeled drug response data from cell lines (source domain) via transfer learning, to significantly improve the prediction of drug response in patients (target domain) who have small sample sizes.
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DeepSADR
FFinetune
Pre-training
Brrehhld select
meak
896A
Interaction map
Ensader
Deceder
Calllnes
Gone
Adaptive
Predictor
Decompose
Extractor
Subsequence Interaction
Readout
Fine-tuning
BWCCS
bliinar
Ureshhuld
scorting
Predict rest
69GA
Interrction
regrassion
Sab Decompose
Nertee
Subsequence Interaction
VGAE
Adaptiive Readout
Predictor
GNP
PMA
Noda
am
Figure 1: Overview of the DeepSADR. (A) shows the model architecture, which is divided into two stages: pre-training and fine-tuning. The model consists of five components: Sub Decompose, Sub Extractor, Subsequence Interaction, Adaptive Readout, and Predictor. During fine-tuning, only the adaptive readout and predictor modules undergo training. The latent space embeddings gen- erated during pre-training serve feature inputs the fine-tuning stage, enabling the model to learn domain-invariant drug response features and achieve efficient knowledge transfer.(B) shows the framework of the GNP module. (C) shows the framework of the PMA module. (D) shows the framework of the SAB module.
3.2 METHOD OVERVIEW
DeepSADR is deep learning model used to improve the predictive performance of drug responses in patients (DeepSADR follows an inductive transfer learning framework, where we fine-tune the model using labeled cell line data (source) and small subset of labeled patient data (target), similar to the GANDALF Dayagopal et al. 20255) approaches). Its overall architecture is shown in Figure The main process of the model is divided into two stages: pre-training and fine-tuning, and consists of the following four key modules:
Sub Decompose: Biologically meaningful decomposition of drug molecules and genomic profiles.
Sub Extractor: Feature extraction of drug and gene subsequences.
Subsequence Interaction: Construct interaction graph between drug subsequences and gene subsequences. Use supervised graph autoencoder to learn latent representations in order to capture the complex relationships and interaction strengths between them.
Adaptive Readout: Through readout function based Set Transformer, subsequence interaction graphs are integrated into global drug response representation. This adaptive readout mechanism crucial for learning transferable features across different domains (cell lines and patients).
3.3 SUB DECOMPOSE
Extensive research indicates that within the DRP framework, the drug response process is not only related to drug structure and gene profiles information but may also be highly correlated with more granular 'subcomponents" such as the substructures of drug molecules and the genetic sub-fragment characteristics of cancer cells. However, most current drug response prediction models perform
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feature extraction drugs/genes as whole, which extent overlooks certain important subcomponents and makes more difficult for the model to explain how drugs treat patients. Based on this, this paper performs "subcomponent" decomposition drug and genomic data.
Drug Smiles Decompose. For the decomposition of drug SMILES sequences, we utilize the core method (BRICS) for molecular decomposition in the RDKit library (Degen et al., 2008). The for- mula is as follows:
(1) where S(d:) denotes the Smiles sequence of drug dj E D, Saub denotes the subsequence of the drug Smiles obtained through BRICS decomposition, and the superseript denotes the corresponding index, n denotes the total number of drug subsequences.
BRICS(S(d)) - -Seubb Subs ..... Subbl:
Genomic Profiles Decompose. We used the gseapy library (Fang et al. 2022) to perform functional enrichment analysis on the gene lists of cell lines/patients to identify significantly enriched biological processes (such as "apoptosis" and "DNA repair") and reveal the biological functions that these genes may share. For example, if the input genes cancer differentially expressed genes, the enrichment analysis may find that they are significantly enriched in the "cell cycle regulation" or "immune response" pathways, suggesting that these processes related cancer. Based the gene pathway results, we designed gene functional clustering algorithm to assign these genes to generalized functional pathway clusters. The formula is follows:
ENRICHOO - (9
(2)
where 99 denotes the genomic profile of cell lines/patients, ENRICH denotes gene function clustering algorithm (For more details, see Appendix [A.20)), Gsur denotes the subsequence of the genomic profiles, and the superscript denotes the corresponding index, m denotes the total number of genomic profiles subsequences.
3.4
SUB EXTRACTOR
Drug Sub Extractor. Traditional methods for processing drug SMILES sequences generally use GNN (Scarselli et (2009),,)) which represent molecules as graphs (atoms = nodes, chemical bonds = edges). Although this method has achieved relatively good results, its message passing mechanism limited by local neighborhood aggregation, making it difficult to capture non-bond interactions and model long-range effects across molecules. To address the issue of limited message passing in GNNs and effectively extract features from drug subsequences, based on the research of (Luo et al. 2025) we integrated six techniques into the classic GNN: edge feature integration, normalization, Dropout, residual connections, feedforward networks (FFN), and positional encoding. These techniques were combined to form a GNP feature encoder for feature encoding drug subsequences. GNP shown in Figure I and its specific formula is follows:
Subş = DSE((BRIC(((CA)))
(3)
DSE(S sub. .... STab) =
(4)
where DSF represents 'Drug Sub Extractor', Subj E R(nxea) denotes the all drug subsequence features of the i-th drug. More details of GNP in Appendix
Gene Sub Extractor. For gene subsequences, we use m fully connected layers for preliminary feature extraction, as shown the following formula:
Subf/Suli GS E(eenrichh((
(5)
GSE sahr P = ( ()
(6)
where GSE represents 'Gene Sub Extractor', Subş/ Suby E R (m)x))) denotes the gene subse- quence features of the j-th cell line/patient and ge the dimension of gene subsequence features, denotes fully connected layer.
3.5 SUBSEQURNCE INTERACTION
In order to explore the potential associations between drug subsequences and gene subsequences and enhance the interpretability of the model, constructed subsequence interaction graph using
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the subsequence features of drugs and genes, and then extracted features from the subsequence interaction graph using a supervised graph autoencoder (Kipf & Welling 2016a).
Construction of subsequence interactions graph. We design an interaction function ab with simple bilinear score to measure the interaction between each subsequence in the drug and each subsequence in the gene. The specific formula is follows:
R (Sub Subf).
edd. g) (dwgg")
where R&ax represents trainable parameter matrix. Subţ is variable representing the cell line subsequence features Subf during the pre-training phase and the patient subsequence features Subj during the fine-tuning stage. and I are the subsequence features of the drug and gene, respectively. a denotes sigmoid activation function and the function & outputs R E Raxm is two-dimensional scalar matrix (interactions score), each value the matrix ranges from [0.1] and represents the strength of interaction between each subsequence. Therefore, we can regard each drug response process as a subsequence interaction map. If a pair of subsequence significantly contributes to the prediction result, they will be updated during training and obtain higher score at the corresponding position the graph. The trained graph can provide key insights into which subsequences influence drug response outcomes, thereby enhancing the model's interpretabillity
Supervised graph autoencoder. Since we view the drug response process as a subsequence inter- adequately consider the relations between subsequences in the interaction
action graph, how graph to obtain high-quality drug response features that are conducive to transfer learning? In- spired by the powerful capabilities of graph convolutional networks, we propose formalizing the subsequence interaction graph and subsequence features as a bipartite graph structure G(A, X), where X {Sub,, Subf/ /ubb") denotes the feature set corresponding the two entities (drug subsequence features and gene subsequence features), A E (=*+=) (m+ is an adjacency matrix obtained by threshold selection from R. The specific process is as follows:
Op Xm R
R
A
(9)
0
R
R R
R
(10)
<t,
where represents the threshold, which is a selectable parameter. Since the subsequence interactions complete graph, reality, some subsequences are independent of each
graph we constructed other and may have no association. Therefore, we introduce threshold selection operation to remove some association edges with smaller weights from the subsequence interaction graph (we regard this edges noise), reducing unnecessary interference and improving the efficiency and performance of the model.
We then use the encoder in SGAE to extract features from the interaction graph, thereby capturing and aggregating the correlations between all interactions. The specific process is as follows:
Z SGAE A).
(11)
More details of SGAE can be found in Appendix A.4
3.6 ADAPTIVE READOUT
Unlike methods focusing solely on genomic distribution differences between source (cell lines) and target (patients) domains, we conceptualize the entire drug response process as a distinct domain. We then employ transfer learning (Pan & Yang. 2010) to mitigate distribution shifts specifically within this drug response domain between source and target. Therefore. how to readout drug re- sponse representations from subsequence interactions graph has significant impact transfer learning. Traditional graph readout methods use fixed pooling functions (such as sum/mean/mmx) to aggregate node embeddings into graph embeddings. This readout lacks flexibility and is not ducive to transfer learning. In this study, we design an adaptive readout function based on the 'Set Transformer" (Lee et al. 2019) aggregate node embeddings into graph embeddings. This readout

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function learns invariiat representations by aggregating node embeddings in permutation- invariant manner, capturing complex interactions between sub-sequences. It's trainable parameters enable fine-tuning of the readout layer feasible and efficient transfer strategy. During fine- tuning, we freeze other modules of the pre-trained model while updating only the Adaptive Readout (AR) and predictor, allowing the model to adapt to patient-specific biological mechanisms (e.g., im- mune responses, tumor microenvironment without overfitting, The readout function is as follows:
= AR(Z)
Σ Decoder (amcoder(Z))l...
(12)
where Z denotes the nodes feature output by SGAE, |. refers to a computation specific to head k of multihead attention module. The Encoder and Decoder modules follow the definitions below.
Encoder (Z) = SAB (Z, Z),
(13)
Decoder H) = FF (SAB* (PMA(H), PMA(H)))
(14)
PMA(H) SAB (s. FF(H)),
(15)
SAB(E, Y) = B FF(B)
(16)
B = + M MultiHead Y, Y)
(17)
Here, H is the Encoder output. The Encoder consists of 1 classical multi-head attention blocks (SAB) that do not include positional encoding. The Decoder includes a multi-heal attention pool- ing block (PMA) A) (where is initial output vector generated by learnable seed vector), followed by further processing through à self-attention modules and a linear projection block (FF). E and Y are the inputs the SAB module, typically in matrix form.
3.7 PRE-TRAINING AND FINE-TUNING
Pre-training. All parameters participate in training during the pre-training stage, This stage utilizes only drug response-related data from cell lines. The loss function for the pre-training stage is defined as follows:
Spre = P
[Zpre) (2pre)),
KL (z(Z,X ( A))p())))))))))))))))){{)))))A)
(18)
where Zpre denotes drug response features of cell lines, which is output by Adaptive Readout func- tion (Alt) in pre-training model. V/ denotes the labels of drug response in cell lines. MSE(.) rep- resents the mean square error loss function, Pi (.). ) is the predictor. KL [-I-- is the Kullback- divergence. Here, p(Z) is a standard Gaussian prior N (0, 1), as commonly used variational au- toencoders.
Fine-tuning. During the fine-tuning stage, we transfer parameters from the pre-trained model to the fine-tuned model. Subsequently, all parameters in the fine-tuned model are frozen except for the adaptive readout function and prediction module. This stage utilizes only drug response-related data from patients. The training loss function is follows:
L ((
(19)
where Ziine denotes drug response features of patients, which is output by the adaptive readout (AR) function in the fine-tuning model. Zpre is the response feature obtained by inputting the drug data and patient data used to compute yiie into pre-trained model (with all parameters frozen). || denotes concatenation. y/P denotes the labels of drug response in patients. P2(.) is the predictor.
During the fine-tuning stage, we effectively balance domain-invariant knowledge and domain- specific knowledge through trainable AR modules and feature concatenation strategies. This en- ables drug response representations to adapt to patient-specific biological variations while retaining pre-trained knowledge from cell lines. This approach also mitigates the risk of overfitting in small- sample learning (patient drug responses). For further explanation this strategy, please refer to Appendix A.17
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4 EXPERIMENTS
4.1 EXPERIMENTAL. SETUPS
Dataset. We utilized the same cancer cell lines and patient genomic characteristics as WISER (Shubham et al. 2024) (including expression data for 1,426 genes), and the drug Smiles sequences were obtained from PubChem (Kim et al., 2019). We collected 966 cancer cell line samples with drug response label (used in pre-training stage) from the DepMap portal (Ghandi et al. 2019) and 555 patient samples with drug response label (used in fine-tuning stage) from the TCGA database. Drug responses in cell lines were determined based on z-score values calculated from the area under the dose-response curve (AUDRC). A z-score value less than O was considered a positive response and greater than 0 was considered a negative response. For patients, responses on the time to cancer recurrence after chemotherapy, with responses exceeding the median classified as positive and those below the median as negative. For specific data preprocessing methods and related details, please refer to (He et al. 2022). In the pre-training stage, we selected 20 drugs that were present in both the DepMap and TCGA. In the fine-tuning stage, due to the limited amount of labeled patient genomic profiles, we only selected five drugs suitable for fine-tuning training (these five drugs all contained at least 20 cases of patient response data). More detial of data Appendix A.5
assessed based
Evaluation protocol. This study primarily observes how the DRP model utilizes large-scale drug- cell line response data as 'proxy' to predict drug responses in patient data (with smaller data scale) through transfer learning. First, the DRP model is trained using drug-cell line response data (source domain). Then, the model is fine-tuned using a small amount of patient drug response data (target domain), and finally used to predict patient-drug response labels. During the fine-tuning phase, patient drug response data is divided into training and testing sets at a 7:3 ratio. We use two commonly used metrics to evaluate the model's classification performance: area under the curve (AUC) and area under the precision-recall curve (AUPR).
Baselines. We have compared this method with WISER (Shubham et al. 2024), GANDALF (Jayaoopll et al., 2025b), CODE-AE (He et al., (022), VAÊN (Jia al. 2021), DAE (Vincent et al. 2008), DruID (Jayagopal et al. 2025a), drug2tme (Zhai & Liu 2024). TransDRP (Liu & Li
2025 PANCDR Kim et al., 2024) and PREDICT-AI (Jayagopal et al.j 2004). Additionally, this
study compares domain adaptation techniques such as Celligner (Warren et al.) (021). Velodrome
(Sharifi-Noghaii et al., 2021). Deep CORAL (Sun & Saenko, 2016), and DSN (MMD and DANN variants) (Bousmalis et al.) (016). To compare with cell line-based drug response prediction mod- els, we also included patient data in the experimental results of DeepTTA (Jiang et al., 2022) and
GraphCDR (Liu et al. 2022). Detailed pipelines for each method are provided in Appendix A.6
4.2 EXPERIMENTAL RESULTS
Performance comparison. As shown in Table [I] DeepSADR significantly outperforms the base- line model for three drugs (Temozolomide, Sorafenib and Cisplatin), with superior AUC and AUPR scores; it remains competitive for the remaining two drugs (Fluorouracil and Gemeitabine). Com- pared to more recent models, our approach of constructing the model from a subsequence perspec- tive has proven effective, improving the model's predictive performance and enhancing its inter- pretability. In addition to comparing with newer baseline models, we also directly applied the cell line-directed model to patient data for testing (i.e., without transfer learning adjustments), with re- sults shown in Appendix A.7 All cell line models performed poorly in predicting the efficacy of five drugs, with AUC and AUPR scores decreasing by approximately 0.3 compared to the fine-tuned model, demonstrating the effectiveness of our transfer learning strategy. models that only con- sider the differences in data distribution between cell lines and patients, even the best- west-performing GANDALF lags behind our DeeRSADR, indicating that our strategy of treating the overall biologi- cal processes of drugs in cell lines and patients as the source domain and target domain for transfer learning is more effective. The results of all our methods were obtained using multiple random seeds to obtain the mean/standard deviation, as shown in Appendix A.9
A Ablation study. To investigate the necessity of each module in the model architecture, we conducted several comparative experiments on DeepSADR and its variants:
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Table I: Performance (AUC and AUPR scores) comparison of all methods for 5 clinical drugs
Methods
Flnoruracil
Temosnolonide
Sorafenih
Gemeitahinc
Cisplatin
AUCT
AUPRI
AUCC
AUPRI
AECT
AUPRI
AUCT
AUPR
ATIC
AUPR
DopPADR
0.81500056
0.82100023
0.87698026
0.886/8.029
0,957/0637
0.97K9.024
0.719RLSST
0.782/0.022
0.927A4.027
0.922/8.211
GANDALF
0.793/0021
0,74000.005
0.79LA0.17
0.752/0.011
0.1100020
0,295/0.62
0.709/1526
0.691/0016
0.852AL071
0.81100111
WISER
0.71540.016
0.74100023
0.76000000
0,78600019
0.727NL007
0.728/0.044
0.619/0.007
0,65200,002
0.78100007
0,7960.020
CODE-AE
0.78200001
8.72208816
0.74266017
0.752200221
0.70581062
0.99001166
0,611/0006
0.65300071
0.74500111
VAFN
0633/0 157
0.5850.100
0.64560.335
0.63220 162
0.60000.021
0.662/1112
0.526/0.00 087
0.618/.223
0.60400049
0.69820.06 065
DAE
0.591/0.066
0,5730.006
0.68510.033
0.668/0.105 105
0 0.88500053
0.13300016
0.530/0006
0,511/0,045
0,522A0.087
0,581/0,066
DuaDD
0.655000922
B454400544
0.64500027
0.634K00077
0.6
0.624/).044
0.66481.002
0.63840025
0.637A00776
0.625X0D48
drgg2mme
0.19000000
D. 0.6460.073
0.67500.009
0.66220:012
0.64100003
0.621/0.054
0.62//00557
0.60210.055
0.61400.488
0.65220.037
CORAL
0.578/00015
0.65 135
1.675000220
0.654400200
0,49180025
0.616/0.0488
0.291A0000
0.54480,057
0.61773.072
0.61770.124
VELODROME
0.598/0.054
04000000
0.701/0:028
0.66800003
0 029
0.749/8.005
0,547/00030
0.4540.0222
1.33320029
0.442X0.12
CELLIGNER
0.536/0.000
0.53
0:5400.070
0.45440.070
0.65400.70
0.575/0.029
0.520/.053
0.49740,042
0,550/0.033
0.57540.029
DSN DSN-DANN
0.65500.K5
0.5960 101
0.685000155
0.66
0.53300000
0.628/0009
0.555/0010
0.58200004
0.60SAL133
DSN-MMD
0.678/0074
66470.10
0.712/00331
0,75000001
0.51580066
0,582/0000
44500.04
0.40100069
0.63000023
0.505A0
TamsDRP
0.791/0:013
0.7900 113
0.721/6.021
0.71580:035
0,731N0033
0.766/0082
0.65/000114
0.598/00022
0.66540027
0.54500.06
PREDICT-AI
0.70200
B.
115
1:71900135
0.3481
0.7520.
0.12211141
0.592402294
0.609022011
0.61
PANCDR
0668/0.014
0.643/0011)
0.701/0.022
0,71120015
0.66590.066
0.74/0.071
0.63300043
0.618/0.090
0.63510.033
0.613/0026
DeepTTA
0.5690.0050
0.509/0.042
0.64600,022
0.62400:038
0,66680.35
0,501/0.005
0.467/0.036
0.498/0.049
0.65400070
0,696000700
GriphCDR
0.5360.012
0.540/.007
0.57600.006
0,568/0014
0.592:0.05
0.549/0.001
0.538/0.008
0.55480.010
0,550.0.000
0,54200.00
Note: Data related to clinical relapse is used for evaluations. The results are reported as the mcan/standard deviation of multiple random seeds. Best performer among all baselines is in bold.
DeepSADR (w/o AR) that removes the adaptive readout module, replacing it with conven- tional readout functions (sum/max/meaan)
DeepSADR (w/o SN) that removes the subsequence interaction module and directly read- out subsequence features through an adaptive readout function.
DeepSADR (w/o TS) removes the threshold selection operation, it does not remove edges with lower weights (noise) in the subsequence interaction graph.
DeepSADR (w/o ET) does not incorporate the pre-trained drug response features as addi- tional input to the fine-tuning stage.
The ablation experiment results are shown in Table When the readout function was placed with a standard readout function (w/ AR), the AUC and AUPR scores decreased from 0.856 to 0.662 and from 0.862 to 0.675, respectively, indicating that the adaptive readout func- tion we used has significant value for the DRP transfer learning task. The results of the variant (w/o ET) indicate that using the drug response features from the pre-training stage as additional input for the fine-tuning model does indeed facilitate transfer learning for DRP. The results of the variant (w/o SN) further con- firm that converting the drug response process into a subsequence interaction graph can effee- tively improve the predictive performance of the DRP model and increase the model's 's inter- pretability. The results (w/o TS) indicate that removing some of the less significant connec- tions edges (noise) from the subsequence in- teraction graph can improve the performance of the model. Overall, the DeepSADR model, which integrates the above modules, performs well, and the absence of any module will compromise its power (the ablation results for each drug, see Appendix A.8).
Table 2: Ablation results (average of 5 drugs).
Methods
AUC1
AUPR
DeepSADR
0,856
0.862
DeepSADR(wlo AR)
0.662
0.675
DeepSADR(wlo SN)
0.698
0.710
DeepSADR TS)
0.775
0.749
(Weo ET)
0.781
0.787
Visualization analysis. During the construction of the subsequence interaction graph, DeepSADR generates an interaction strength correlation graph to analyze the interaction strength between sub- sequences in the input drug and genomic profiles. Subsequences significantly contributing to drug response outcomes receive higher scores. To intuitively present the interaction relationships between these subsequences, we use a heat map (as shown in Figure 2) to display the interaction graph.
The Y-axis indicates which generalized functional pathway clusters (biological processes such as apoptosis, cycle regulation, etc.) are affected by temozolomide. The X-axis represents all substruc- ture sequences (chemical fragments) of the temozolomide molecule. The intensity of colors in the heatmap indicates the weight of influence exerted by corresponding drug subsequences on biolog- ical functional pathway clusters. The sequence with the highest weight is highlighted in red. It

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breaks through the limitations of other models that merely conclude "this drug is effective", delv- ing into the deeper molecular mechanisms of "which structural component of this drug is effective and why", This not only thoroughly elucidates the mechanism of action of temozolomide but also provides a direct, visual chemical blueprint for designing next-generation, superior anticancer drugs.
To validate the reliability of the subsequence associations learned by our model, we con- ducted correlation validation on drug/gene sub- sequences with higher weights. Studies such as (Stupp et al., 2005; (Zhang et al., 2012 Hegi al., 2005) indicate that the "tetraene ring" fragment exhibits high association score with the "cell stress and apoptosis pathway"-not merely computational result, but conclusion validated through extensive biological experi- ments. This demonstrates that our DeepSADR model accurately captures the strength of gen- uine sub-sequence interactions.
Geree W.W.ARUETECTTTTTOOOO CEA.TTTTO---AAAAA
Mint
Vilio
Cower
Drg Scbrequeooo
Drug Nurre/mmooloonc
We also performed similar visualization analy- other drugs, well as other experiments such as parameter sensitivity analyses. For more details on other experiments and model parameters, please refer to the appendix A.10
(a (e):Heatmap
(b):Drug Structure Diagram
Figure 2: Visual analysis of patient-drug subse- quence interactions. (a) shows heat map. (b)
shows the drug molecular structure.
A.20
5 CONCLUSION
In this study, we leverage biological knowledge within the framework of transfer learning to propose DeepSADR, a robust deep learning model for transferring drug responses from cell lines to clinical patients. On one hand, it converts drug responses into subsequence interaction graphs for feature extraction, enhancing predictive performance and model interpretability. On the other hand, treats the entire drug response process domain, introducing an adaptive readout function improve domain transfer accuracy. transfer learning experiments from cell lines to the patients, Deep- SADR outperformed other state-of-the-an -frt methods in terms of predictive performance. However, DeepSADR has the following limitations: (1) Threshold tuning is required for each drug (poten- tially related to pharmacological diversity); (2) Performance depends to extent on the quality of constructed sub-sequence interaction graphs. Future research should consider addressing these
two aspects.
REPRODUCIBILITY. Our source code and the data used in our experiments are publicly available http://giinubboomm/////ss/Dpppp
ACKNOWLEDGMENTTS This research was supported by the National Natural Science Foun- dation of China (Grant Nos. U23A20321 and 62272490); the Natural Science Foundation of Hunan Province of China (Grant No. 2025J120062); and A*STAR's Decentralised Gap Funding (123DIAG081) and AIDD Catalyst Grant (H25AIN0003)).
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ETHICS STATEMENT
This work adheres to the ICLR Code of Ethics. this study, no human subjects or animal ex- perimentation was involved. All datasets used were sourced in compliance with relevant usage guidelines, ensuring no violation of privacy. We have taken care to avoid any biases or discrimi- natory outcomes in our research process. No personally identifiable information was used, and no experiments were conducted that could raise privacy security concern. We are committed to maintaining transparency and integrity throughout the research process.
REPRODUCIBILITY STATEMENT
We have made every effort ensure that the results presented in this paper are reproducible. The ex- perimental setup, including training steps, model configurations, and hardware details, is described detail in the paper.
Additionally, all datasets used in this paper are publicly available resources, ensuring the consistency and reproducibility of the evaluation results.
We believe these measures will enable other researchers to reproduce our work and further advance the field.
LLM USAGE
Large Language Models (LLMs)) were used to aid the writing and polishing of the manuscript. Specifically, we used an LLM to assist in refining the language, improving readability, and ensuring clarity in various sections of the paper. The model helped with tasks such as sentence rephrasing, grammar checking, and enhancing the overall flow of the text.
important to note that the LLM was not involved in the ideation, research methodology, or experimental design. All research concepts, ideas, and analyses were developed and conducted by the authors. The contributions of the L.M were solely focused on improving the linguistic quality of the paper, with involvement in the scientific content data analysis.
The authors take full responsibility for the content of the manuscript, including any text generated polished by the LLM. We have ensured that the LLM-generate text adheres to ethical guidelines and does not contribute to plagiarism or scientific misconduct.
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A
APPENDIX
A.1 PSEUDOCODE AND LIST OF NOTATIONS
This section shows the pseudocode of the DeepSADR in Algorithm and list of notations in Table
Algorithm DeepSADR: Drug Response Prediction
Require: D: Drug set with SMILES sequences S(d;)
9: Genomic profiles of cell lines
GO: Genomic profiles of patients
V:: Drug response labels for cell lines
D/p: Drug response labels for patients
t: Threshold for adjacency matrix (default=0.5)
1: Stage 1: Pre-training
Cell Line Data
2: for each drug dj E D) and cell line of E g do
3: Step 1: Subsequence Decomposition
4: [S]
giub..... ................................ S
sub. + BRICS ( (d4)) (Drug SMILES decomposition
5:
subs...
suh. gseapy (gf) (Gene enrichment analysis)
6:
Step 2: Feature Extraction
7:
Subş - {GNP(S) (ub)).............. GNP (
{Drug subsequence features]
8:
Sub, + (gbb)).
(9 [Gene subsequence features)
9:
Step 3: Subsequence Interaction Graph
R[]][ < (GNN(SS) sub) W
(9 (Interaction scores}
R[LL + [R[i R[]] 2
11:
(Threshold filtering]
100
otherwise
12:
Construct adjacency matrix A from R
13:
Z +- SGAE(X (Suby Subf], ( A) (Graph encoding)
14:
Step 4: Adaptive Readout & Prediction
15:
Z - K E Decoder (nnnoder(nncder(())) {Set Transformer readout]
Dr + PZZ) (Prediction)
Lpre 4- MSE()., V.) ( (Loss with VAE regularization)
18: end for
19: Stage Fine-tuning on Patient Data
20: Freeze all layers except Adaptive Readout and Pz
21: for each drug d2 E and patient of E G do
22: Step 1: Forward through Pre-trained Model
23: Compute 2pre using frozen pre-trained model (Pre-trained features)
24: Step 2: Fine-tuned Forward Pass
Ziene +- Adaptive Readout(Z) (Fine-tuned features)
26:
Step 3: Feature Fusion & Prediction
27:
) + P2([2 2prel) {Concatenate features}
28:
ffine (SE(),, yp) (Fine-tuning loss}
29: end for
Ensure: Fine-tuned model for patient drug response prediction
A.2 DISTINCTION BETWERN THE TWO DOMAINS(CELL LINES AND PATIENTS)
Table provides detailed information on the two study subjects (cell lines and patients) consid- ered in this study. The cell line domain has rich labeled responses for multiple drugs, while patient data has only small number of labeled samples. In our experiments, we selected 20 drugs that are used both patients and cell lines. To assess the applicability of our method in patients, we considered five drugs with recorded responses in at least 20 patients.(The cell lines genomic pro- files data can get in http:///dpmmappoppppoooo/ and the patients genomic profiles data can get in http://portal..dcccaceee
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Table 3: LIST OF NOTATIONS
Symbol
Description
N
The number of drug
N
The number of drug response data in cell lines The number of drug response data in patients cell lines genomic profiles patients genomic profiles The i-th drug
N
ge
Go
di
The j-th genomic profiles of cell lines/patients The subsequence of drug Smiles
Soub
Soub
The subsequence of genomic profiles
Subç
The set of all subsequence features of drug i The set of all subsequence features of cell lines / patient
Subę/ Subb
Indicates the number of subsequences of drug decomposition
n
Indicates the number of sub-sequences of gene decomposition
m
R E R nxm
Subsequence interaction map
Z
The nodes feature output by SGAE
The drug response feature output by adaptive readout functional
2
Table Details about the two domains in cancer drug response prediction.
Numbher drug responses
Number drugs selected in our experiments
Domalas
Used stage
Labeled data
Drug response lahel
Evalusted using Z-score
on AUDRC scures.
Cell lines
treitrannig
225,781
considered
respondents.
7-soone greater considered negotive respondents.
Cancer relapse time paul-hhmocchragg
Patients
Fine-tuming
576
(1) Valaes greater dian the median considered pusitive repondenis. considered nepative responlents.
338
A.3 THE DETAILS GNP
To obtain better drug sub-sequence features, we used Wu et al.'s method to improve its performance graph-level tasks. The scheme integrates six popular techniques: edge feature fusion, normaliza- tion, dropout, residual connections, feedforward networks (FFN), and position encoding.
A.3.1 EDGE FEATURE INTEGRATION
Edge features were initially incorporated into GNN frameworks GGimerr et al., 2017) by integrating them into the message-passing process. Following this practice, we encode edge features to enrich node representations. For GCN (Kipf & Welling 2016b):
h = Σ
Vd.d. n W' Euc W!);
(20)
nEN(oJU(e)
where W! is the trainable weight matrix for edges.
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A.3.2 NORMALIZATION
Normalization techniques stabilize GNN training by mitigating covariate shift. We use Batch Nor- malization (loffe & Szegedy 2015):
h = a(BN( E
mEN(o)J(u) ddudd h 1 W eew W.)).
(21)
A.3.3 DROPOUT
Dropout (Srivastava et al. 2014) addresses overfitting by reducing co-adaptation among neurons. Applied alter activation:
h Dropout(a (BN(
Σ
'w' + Euo W:)))-
(22)
uEN(o)u(e) V d.ndo
A.3.4 RESIDUAL CONNECTION
Residual connections (He al. 2016) alleviate vanishing gradients. Integrated
h (BoN
uEV(e)U(E) Vdudo hi 'w'
Σ
(23)
te.. w:)) + h
(24)
A.3.5
FEED-FORWARD NETWORK
Inspired by Transformers Vaswani et al. 2017) we append an FFN:
FFN(h) = (NW FnN) WWigg + h),
(25)
The node embeddings become:
h (FN((ropuu
uEN(eJU(s) Vdldd. h ' W
Σ
(26)
+ew W!))) h (-1),
(27)
A.3.6 POSITIONAL. ENCODING
We use Random Walk Structural Encoding (RWSE)
Li et al. 2020):
Be [x
x
RW SE
WFE,
(28)
where [: || denotes concatenation and Wpg is trainable.
A.4 SUPERVISED GRAPH AUTOENCODER
We use supervised graph autocncoder to further extract features from the sub-soquence interaction graph. graph autoencoder consists of probabilistic encoder and decoder, with several important differences compared standard architectures operating vector-valued inputs. The encoder component is obtained by stacking graph convolutional layers learn the parameter matrices ya and that specify the Gaussian distribution of latent space encoding.
(Z A) II A,) T=1
(29)
q X A) = N (zilpz, diag(o?)).
(30)
Here, μ GX,,,(X, A) is the matrix of mean vectors pss similarly logor = GCN, A). The two- layer GCN is defined as GCN(X, A) RReLU(AX.)))
with weight matrices W, GCN. (X, A)
and GCN, (X, A) share first-layer parameters Wo. Re.. maz(0.) .) and À D 112 AD 1/2 is the symmetrically normalized adjacency matrix. N denotes the Gaussian distribution.
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Table 5: Annotated samples of the 5 drugs
Drug name
samples
Pos
Neg
train
test_num
Fluorouracil
88
47
41
52
36
Cisplatin
40
20
20
24
16
Sorafenib
26
13
13
15
11
Gemcitabine
138
60
78
82
56
Temozolomide
46
23
23
27
19
Table 6: Performance (AUC and AUPR scores) comparison of all methods for 5 clinical drugs
Methods
Finorouracii
Temoozodamide
Sorafenih
Gemeitabine
Cieplatin
AUCT
AUPRI
AUCC
AUPRI
AECT
AUPK"
AUCT
AUPR
AUC
AUPRT
DeeDADD
0.815/0.066
0.82100,223
0.57800.026
0.88699022
0,957/0837
0.97800824
0.7190.857
0.702/0.023
0.927/9.227
0.922/0.211
GANDALF
0.793/0.011
0.74000:886
E791/0011
6.78210011
08118022
0.705/0/022
(L7091L/226
0.607000116
0.8220.071
WISER
0.7100.0366
0.741/0.025
8.7600.006
6,78600.019
0.727:0.00 067
0.726/00034
0/49/0.17
0.652/0.001
0,781X0.077
0,79690.22
CODE
0.782/0021
0,722/0.11
0,742/00117
0,732/0.021
0.63180020
0,705/0002
0.29301016
0.651A0,006
0.652A0071
0,743M0011
VAEN
0.63300.15
0.5650.
0.64800.335
0.6520
021
0.66800112
0.526/1.067
0.618/0.225
0.60400049
0.69520.00 D65
DAE
0.573/0086
0.685/0.013
0,66800
0.530/0.036
0,511/0,048
0,522/0067
0.581/0.096
DriLD
0.655000922
80540:034
064500027
0.63400377
0.61490055
0.62490044
0.06411.022
0.621A00776
0.62500D4N
drgg2mme
061900080
0.67/000899
0:66220:12
0.6-180005
0.21//8/644
0/22000057
0.61400048
0.63220087
CORAL
0.578/0015
1.5
0.67500.220
0.654X0.200
0 0,0002
0.666/00488
0.592/0000
0.546/0,037
0.617A0.072
0,517A0L12 14
VELODROME
0.598/0884
0.70L00028
0.66800005
0,505/0029
0,74890005
0.547/00500
0.434820022
0.58340.029
0,242X0012
CELLIGNER
0.536/0.099
D. 0.531000034
0.54400070
0.454000000
0,45/00070
0 0.575/0.00 029
0.520/0.053
0.497
O. 0,575X0.020 0.5500h033
DSN DANN
0.655000055
10596A.110
0.69000.000
0,53390050
0.628/0.099
0.555/0000
0.582/0.044
0.5
0.605A0.135
DSN-MMDD
0.68/0.0774
0.670 103
0.71200031
6,75900001
0.51590036
0.58280000
0.465/1041
0.65000025
0.605AODD67
TamsDRP
0.791/0.013
0,7940 113
0.721/0.021
0,71500.035
0 0.73180.03
0,766/0.03
0635/0.014
5969000042
0.66540022 027
0.54530.036
PREDICTFAL
0.702/0.112
1.7
0,71910.135
0.75482236
0,752001993
0.61201114
0.59300.244
201
227
PANCDR
0668/00014
0.64/00111
0.201.0002
0,711/0015
0674/0071
06230004
0.68800059
0.6350.025
DeepTTA
0.56900000
0.59%/00022
0.466000222
0.62400038
0.501/0.05
0.49810:029 0.467/00066
0.459000700
0,99600070
GuipCCDR
0.53600312
0.5400:807
0.57600066
0.56800014
0,5928005
0.549/0021
0.508/00008
0.55400010
0.55000000
0.54200009
Note: Data related to clinical relapse is used for all evaluations. The results are reported as the mean/standard deviation of multiple random sceds. Best performer among all haselines bold.
A.5 DETAILS OF DATA USED IN THE EXPERIMENT
Table shows the data details of the five drugs used in the fine-tuning phase. It is worth noting that both the DepMap and TCGA datasets we utilized underwent standardized preprocessing pipelines, including log transformation and quantile normalization, mitigate batch effects. Furthermore, adhered the same gene expression normalization as WISER (Shubham et al. 2024) ensure consistency.
A.6 THE INTRODUCTION OF BASELINES
The following is an introduction to the baselines we selected for our comparative experiments:
WISER (Shubham et al. 2024). A weakly supervised and supervised representation learn- ing fusion framework aimed at solving the problem of scarce labeled data in cancer drug response prediction. By integrating unlabeled omics data (such as gene expression and mutations) to generate pseudo labels, and combining them with labeled data to jointly train the model, the robustness of prediction in small sample scenarios is improved.
GANDALF (Jayagopal al., 2025b). This is generative attention framework for pre- dicting cancer drug responses. It employs diffusion model and cross-domain attention mechanism to directly transform cell line data into augmented samples that retain key in- formation while exhibiting distributions closer to real patients. By generating pscudo labels through multi-tar learning, effectively enhances personalized prediction performance in data-scarce scenarios.
CODE-AE (He et al.) 2022). Context-decoupled autoencoder. Separate biological back- ground information (such as cell type) from drug response-specifte features in omics data through adversarial training to eliminate confounding factors. The decoder reconstructs samples based on decoupled features, while the predictor focuses on drug response signals, significantly improving clinical translation capabilities.
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VAEN (Ja al., 2021p. Variational autoencoder network. The VAE architecture is used to learn the low-dimensional manifold structure gene expression data. The generator reconstructs the input features, and the predictor infers drug sensitivity based latent variables.
DAE (Vincent et al. 2008). Classic denoising autoencoder. By adding noise to the input data and training the network to reconstruct the original signal, it learns robust feature representations. As an early deep learning method, provides a basic feature extraction module for subsequent drug prediction models, and is widely used in pre-training with unlabeled data.
Drull (Jayagopal al. 2025a). Multi-task domain adaptation model. For cancer recur- rence prediction, shared encoder is designed to learn general gene features across tumor types, while branch decoders are adapted to different chemotherapy drugs. MMD loss is used to align the source domain (cell lines) and target domain (patients) distributions to mitigate domain shift issues.
drug2tme (Zhai Liu 2024). Tumor microenvironment decoupling framework. Separate tumor microenvironmen (TME)-related features from cancer cell intrinsic features using graph neural networks, and quantify the impact of TME on drug efficacy. Provide inter- pretable analysis of drug response mechanisms to guide combination therapy design.
Celligner (Warren et al. 202T). Cell Line-Patient Transcriptome Alignment Tool. Cal- culates gene expression similarity between cell lines and patient tumors based on optimal transport theory, constructing a cross-domaie mapping matrix to correct biological discrep- ancies between preclinical models and real patients.
Velodrome (Sharifi-Noghab al. 2021). Distribution- generalization framework. Combining labeled and unlabeled data, distribution-robute features are learned through domain-invariant regularization (such as MMD and CORAL) and adversarial training. Spe- cializing in drug response prediction for unknown cancer subtypes, it has been verified to outperform traditional methods in TCGA pan-cancer data.
Deep CORAL (Sun & Saenko, 2016 Classic domain adaptation method. Align the second-order statistics (covariance matrix) of the source domain and target domain to min- imize the difference between domains.
DSN (MMD and DANN variants) (Bousmalis et al. 2016). Domain separation network. Contains private encoders and shared encoders: extracts domain-invariant features and difference losses, and separates public and private features through MMD or adversarial training. The effectiveness of feature decoupling has been verified in cross-domain drug sensitivity prediction.
Proposes a knowledge-guider domain-adaptive model that simultaneously predicts multiple drug responses and captures their correlations via multi- label graph neural networks. It employs global-local adversarial strategy to achieve fea- ture alignment and separation across cancer types, thereby cnabling the transfer of drug response predictions from cell lines to patients.
TransDRP (Liu & Li, 2025
PANCDR (Kim al. 2024). Constructed a two-stage adversarial network that alternately trains the discriminator and predictor models (rather than gradient reversal) to reduce the distribution discrepancy between preclinical and clinical data. It employs a Gaussian en- coder to extract gene expression features, enabling patient-specifie prediction of anticancer drug responses.
PREDICT-AI (Jyygoppl et al. 2024). Designed a Transformer- sequence modeling approach that directly processes variable-length mutation sequences from clinical diagnos- tic panels through a novel annotation strategy. It integrates patient survival information as an auxiliary supervisory signal to enhance the accuracy drug response prediction.
DeepTTA (Jiang et 2022). Substructure encoding of drug molecule SMILES sequences and cell line gene expression as feature vectors are used to model drug-target-genot inter- actions through a multi-head attention mechanism. Transformer is introduced for the first time into IC50 prediction, achieving SOTA on the GDSC/CCLE dataset.
GraphCDR (Liu et al.) 2022). Graph contrastive learning model. Graph neural networks are used aggregate neighborhood information, and a contrastive loss function is designed
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maximize the similarity of positive samples (similar drug sensitivity) and minimize the similarity of negative samples. This model performs exceptionally well in cold-start drug prediction.
Table 7: Results of five drugs on the unfine-tuned DeepSADR model.
fine-tuning
no fine-tuning
Drug
AUC1
AUPR
AUC1
AUPR
Fluorouracil
0.806
0.821
0.534
0.557
Cisplatin
0.927
0.922
0.585
0,675
Sorafenib
0.957
0.978
0.609
0.621
Gemcitabine
0.719
0.702
0,496
0.501
Temozolomide
0.870
0.886
0.617
0.667
A.7 EXPERIMENTAL RESULTS OF DEEPSADR WITHOUT FINE-TUNING
To verify that our model achieved effective transfer learning through fine-tuning strategies, we pre- dicted the responses of five drugs in patients before fine-tuning the model. The results are shown in Table 7
Ortanesso and
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Subrequences
Name: Soratenb
Drug Subsequences
Gendialine
Heatmap
(b):Drug Structure Diagram
Ha):meatmap
(b]:Drug Structure Diagram
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Drug Subvequenccs
Drug Name: Florcuna
Subsequenccee (a):Heatmap
Drug Nome: Caplidi
Heatmap
(b]:Drug Structure Diagram
[b]:Drug Structure Diagram
Figure 3: Visualization of subsequence interactions.
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Table 8: Results of ablation experiments conducted on five different drugs
Methods
Fluorouraci
Temozulomide
Sorafenth
Gemeltubine
Cisplatin
ATICT
AUIPR
AEIC
AUPRI
AUCC
AUPR
AUCC
AUPR
AUCC
AUPPR
DeepSADI
0.8R5ML856
0.821/9.23
0.57088122
0.89690229
0.557A8837
0.978901144
0.702H0022
8J2770.827
0.92220.121
DepSADRR(wd AR)
0.71550 006
0.74100003
D.600 DOs
0166640019
0.621000007
0.6260.012 014
0.50180037
0,552/0
0.22 007
0.7360.020
DiajSADRion SN)
0.741/00011
0:76220.006
0.722/0111
0.660000000
069500.622
0.65700015
0.7838
0.7130.011
DepSADDR(wo TS)
0.77441-021
0.734000016
0:50480013
0.752/0021
0.831800000
0.80500.022
066901116
0.651/0005
08815810771
0.77580.011
DeepSADRtwi ETT)
0,753/0.151 157
0.77580,101 101
0.81400035
0,802/0 162
0.849/0.021
0,85770.112
0666800657
0.66480:255
0.83440.004
0.85770.065
DeepSADRawio CNP)
0.76400009
0.80900000
0.8580.017
0.861/0087
0.907/0.016
0.91500.227
0. 0,30300711
0.69100.033
0.88510.057
0.88700.054
Note: Data related to clinical relapse is used for evaluations. The results are reported as the mcan/standard
deviation of multiple random seeds. Best performer among all baselines is in bold.
A.8 MORE DETAILS ABLATION
To maintain consistency with the comparative experiment, we presented the ablation experiment results for the five drugs, shown in Table [8] To validate the positive guidance of our model by introducing the GNP module for extracting drug sub-sequence features, we conducted an ablation experiment on the GNP module. Specifically, we replaced the GNP module with the simplest GNN to extract drug sub-sequence features. The experimental results are shown in the DeepSADR (w/o GNP) section of [
We found that using either the AR module or the SN module alone did not yield significant perfor- mance improvements. This observation was precisely the impetus for designing our model frame- work. Our analysis of this phenomenon is as follows:
DeepSADR (w/o SN) performs poorly, indicating that the structured information provided by the sub-sequence interaction graph is crucial. Without this information, the model degrades into basic feature concatenation model. Many previous drug response models have made predictions solely by concatenating drug and gene transcription features. We found that transfer learning based on such features yields suboptimal results.
DeepSADR (w/o AR) underperforms, indicating that intelligent feature aggregation in interaction graphs is key to transfer learning. Simple aggregation operations (e.g., summation) lose substantial critical information. We reached this conclusion through molecular property prediction experiments.
The two modules synergize: the SN module generates high-quality, interpretable input representa- tions, while the AR module extracts domain-relevan information for cross-domain tasks. Together, they address the dual core challenges of domain transfer and interpretability. We supplement this analysis in our paper.
A.9 THE MORE DETAIL OF COMPARATIVE EXPERIMENT
In the comparative experiments in the main text, we only wrote the average values and did not write the standard deviation. This section supplements the standard deviation in the comparative experiments. See Table for details.
A. MORE DETAIL OF VISUALIZATION ANALYSIS.
Since we trained five drugs fine-tuning, we visualized the sub-sequence interaction graphs of the five drugs. The results are shown in Figure 2
In addition, we also visualized the response characteristics of trained drugs in cell lines and patients. and the results are shown in Figure For part (a) in Figure we visualize using molecular fingerprint features and patient genetic data. For part (b), we visualize the concatenated features derived from the pre- pre-trained feature 2pre and fine-tuned feature 2iie of patient drug responses.
The results show that the feature distribution of the trained model exhibits good consistency. indi- cating that our model has achieved a certain degree of generalization of drug response distribution in cell lines and patients.
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Global-tevel
Global-level
40
40
30-
20
20
0-
-20
10-
-30
Patient
Patient
-60
Cell line
-40
Cell line
-00
40
60
(a) before training
(b) after training
Figure 4: 1-SNE visualization of the drug response feature in cell line data and patient data.
A.11 PARAMETER SENSITIVITY ANALYSIS.
Since ablation studies have shown the importance of modules such as threshold selection and readout functions, conducted sensitivity analyses on some of the more important parameters in these modules. Figure 5 shows the sensitivity experiments for four parameters: threshold parameter, convolution layer parameter, number of attention heads, and Dropout. From the results in the Figure 5 we find that the model's performance is quite sensitive to the threshold selection parameter. We analyzed that threshold selection is critical part constructing the sub-sequence interaction graph. Initially, trained the model using the fluctuations in model performance across different drugs. We analyzed that this might be because different drugs have varying degrees of sensitivity to the threshold. threshold that is too small may introduce excessive noise interference, while a threshold that too large may remove important sub- sequence interaction information. This is
threshold for each drug, but found significant
will further investigate in the future.
(a)
(b)
Recattuily Shencaal ALC
Keeuliiity thredteld AUPS
Kreutindy Sager
Reanciaiily Saprr AIPA
(c)
(d)
0.5
Figure 5: Parameter sensitivity analysis. (a) threshold parameter sensitivity analysis, (b) sensitivity analysis of the number of convolutional layers in SGAE, (c) sensitivity analysis of the number of attention heads in the adaptive readout function, and (d) sensitivity analysis of the dropout rate in the adaptive readout function.
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Table 9: The detail params of five drugs DeepSADR model.
Drug name
threshold
convolution layer
attention heads
drupout
epoch
train
fine.epoch
fine
Fluorouracil 0.19
4
0.5
200
1e405
100
le-04
Cisplatin
0.44
4
0.5
200
1e-05
100
le-04
Sorafenih
1e-04
Gemeitabine
0.55
4
0.5
200
1e-05
100
1e-04
Temozolomide
0.4
0.5
200
1e-05
100
1e-04
Table 10: Range of hyper-paramettrs used for optimizing model performance.
Hyper-parameter
Range
Distribution
threshold
[0.1, 0.9]
Float (step size=0.01)
convolution layer
[1.3, 6, 9]
Categorical
dropout
[.43, 0.6]
Categorical
attention heads
[1,2, 4,
Categorical
fine_Ir
[1e 1e
Log uniform
train_Ir
[]e 1e-
Log uniform
Table the parameter details for each drug to achieve the best predicted results. Tabel shows the range of hyper- parameters used for optimizing DeepSADR model performance.
A.12 PERFORMANCE ON OTHER DRUGS
In addition to testing the five drugs mentioned in the main text (for which relatively known patient response data exists and positive and negative samples are balanced), we also conducted corresponding experiments on several other drugs with fewer response data. The results are shown in Table II
Table 11: Performance (AUC and AUPR scores) comparison of all methods for other clinical drugs
Sunitinib
Doxorubicin
Erlotinib
Methods
AUC
AUPR
AUCT
AUPR
AUC1
AUPR
DeepSADR
0.705/0.026
0.691/0.013
0.703/0.016
0.686/0.009
0.727/0.027
0.718/0.014
GANDALF
0.623/0.013
0.625/0.016
0.621/0.017
0.622/0.011
0.611/0.011
1.603/0.022
WISER
0.615/0.013
0.633/0.021
0.611/0.006
0.637/0.019
0.621/0.009
0.628/0.014
CODE-AE
0.582/0.011
0.552/0.012
0.562/0.027
0.532/0.031
0.591/0.021
0.605/0.032
VAEN
0.563/0.017
0.585/0. 100
0.598/0.025
0.562/0.012
0.592/0.021
0.568/0.022
DAE
0.571/0.026
0.573/0.016
0.558/0.013
0.568/0.015
0.585/0.003
0.593/0.026
DruID
0.565/0.0m7 .09
0.554/0.024
0.564/0.007
0.534/0.037
0.574/0.015
0.554/0.014
drug2tme
0.529/0.008
0.546/0.003
0.575/0.009
0.562/0.012
0,541/0.005
0.5
CORAL
0.518/0.015
0,551/0.035
0.517/0.002
0.565/0.007
0.491/0.023
0.616/0.00 048
VELODROME
0.518/0.004
0,493/0.002
0.571/0.018
0.568/0.003
0.515/0.029
0.549/0.00 0US
CELLIGNER
0.536/0.060
0.531/0.024
0.454/0.070
0.454/0.070
0.454/0.070
0.575/0.029
DSN-DANN
0.535/0.005
0.526/0.021
0.501/0.005
0.511/0.004
0,503/0.050
0.518/0.009
DSN-MMD
0.508/0.004
0,521/0.003
0.492/0.003
0.518/0.005
0.485/0.006
0.509/0.00 009
TransDRP
0.581/0.031
0.589/0.013
0.612/0.012
0.613/0.014
0.573/0.023
0.576/0.023
PREDICT-AI
0.557/0.218
0.573/0.132
0.523/0.233
0.519/0.251
0.549/0.236
0.558/0.
PANCDR
0.611/0.004
0.622/0.018
0.681/0.012
0.658/0.011
0,569/0.026
0.598/0.016
DeepTTA GraphCDR
0.519/0.005
0.529/0.012
0.546/0.012
0.524/0.000 000
0,484/0.015
0.501/0.005
0.516/0.012
0.512/0.007
0.526/0.006
0.508/0.014
0.512/0.002
0.509/0.002
Note: Data related to clinical relapse is used for all evaluations. The results are reported as the mean/standard deviation of multiple random seeds. Best performer among all baselines in bold.
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Based on the experimental results, our model exhibited certain decline in performance, primarily due to insufficient sample sizes for some drugs, which prevented fine-tuning (hence we directly input the data into the pre-trained model generate corresponding outputs). Although our model's performance decreased, it still demonstrated certain advantages compared to all baseline models.
A.13 PERFORMANCE ACROSS DIFFERENT CANCER TYPES
To further validate model's performance and demonstrate its ability achieve effective transfer learning across the entire drug response spectrum, we conducted cross-cancer experiments. Patients with the same cancer type were grouped together, then separately fine-tuned and tested. The corre- sponding results are shown in Table
Table 12: Comparison of performance across various cancer types.
Cancer Type
AUC1
AUPR
TCGA-CN
0.874/0.036
0,862/0.032
TCGA-2J
0.857/0.042
0.864/0.036
TCGA-IB
0.762/0.037
0.798/0.037
TCGA-VQ
0.759/0.038
0.783/0.023
TCGA-DU
0.849/0.053
0.826/0.051
A.14 TREATING DRUG REACTIONS AS DISTINCT FIELD
We treat drug responses as a distinct field because there are well-documented biological differences in how drugs act in preclinical models (such cell lines) versus clinical patients. The notorious "translation gap" stems precisely from these disparities, including factors like the tumor microen- vironment, immune regulation, and drug pharmacokineties. Existing approaches primarily focus on revealing static, invariant features within the genome, yet fail to adequately model the dynamic, mechanistic variations occurring during the response process. Therefore, domain differences manifest not only as variations in gene expression data distributions but funda- mentally reflect inherent disparities in drug response mechanisms. By modeling the drug response process as a specific domain and leveraging adaptive readout technology to learn representations,
core philosophy is this:
more directly address this fundamental challenge.
To aid understanding, we can draw an analogy: Consider the process of diagnosing a car malfunc- tion. Cell lines are like vehicles placed simple, controlled garage, while patients are like the same model driving on the road, subject to weather, traffic, and different drivers (akin to the immune system, tumor microenvironment, etc.). Traditional approaches focus on matching parts lists be- tween garage and road (e.g.. "both equipped with V6 engines and four tires"). While helpful, this is incomplete because identical components may fail under different conditions. DeepSADR focuses on matching functional interactions- the "circuit diagram" where ignition systems, fuel injectors, and ECUs collaborate to start the car. This functional blucprint exhibits stronger transfcrability between garage and real-world scenarios, even when spark plug brands differ.
Biological Terminology:
Cell Line/Patient Domain: Defined by raw genomic features gP). These distributions exhibit differences (P(g") f P(GP)), resulting in distribution shifts.
Drug Response Domain: Defined by interaction patterns between drug sub-structures and gene sub- sequence. Key inhibitors often target the same apoptotic pathway-whethr tested vitro or in vivo despiee potential variations in the specific expression levels of genes within that pathway.
By modeling and integrating these functional interaction patterns (via sub-sequence interaction graphs and adaptive readout mechanisms) rather than directly processing raw genomic component lists, we bypass vast amounts of irrelevant genomic heterogeneity to precisely capture the transfer- able elements of drug mechanisms of action.
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A.15 TIME REQUIRED TO CONSTRUCT THE SUBSEQUENCE INTERACTION GRAPH
Considering the need to dynamically construct separate interaction graphs for each (patient, drug) pair, and that the time required for construction depends on the size of drug and gene sub- sequences, we calculated the time required to construct a sub-sequence interaction graph for dif- ferent drugs visually demonstrate computational overhead. The results shown in Table 13
Table 13: Time consumption.
A.16 COMPARISON OF STATISTICAL SIGNIFICANCE TESTS BETWEEN DEEPSADR AND BASELINE METHODS
Drug name
samples
time(ms)
Fluorouracil
88
112
Cisplatin
40
61
Sorafenib
65
To demonstrate that our DeepSADR model achieves significantly better performance com- pared to other baseline methods, added p- values to indicate statistical significance, with results shown in Table 14 Specifically, computed p-values for DeepSADR versus other baseline methods across five drug outcomes un- der ten different random seeds. Subsequently, we combined the p-values for these five drugs using Fisher's 'ss method obtain more robust statistical value. The experimental results reveal that DeepSADR achieves highly significant performance improvements over all baseline methods (all p-values 0.001), finding of substantial statistical significance. Such low p-values indicate that the observed performance gains are highly unlikely to stem from random factors sampling er- rors, confirming that DeepSADR's 's model framework effectively enhances drug response prediction capabilities.
Gemeitabine
138
198
Temozolomide
82
Table 14: Statistical Significance Test Results: Comparison of DeepSADR with Baseline Methods
AUC
AUPR
Method
Avg
Diff
p-value
Avg
Diff
p-value
Deep
0.856
0.862
GANDALF
0.791
+0.064
5.81e-05
0.765
+0.096
5.34e-11
WISER
0.726
+0.129
2.67e-12
0.741
+0.121
4.28e-16
CODE-AE
0.680
+0.175
6.84e-12
0.711
+0.151
4.54c-17
VAEN
0.620
+0.235
2.34e-13
0.640
+0.222
5.34e-11
DAE
0.563
+0.293
2.46e-16
0.589
+0.273
2.46e-16
DruID
0.639
+0.217
3.38e-12
0.635
+0.227
9.75e-16
drug2tme
0.634
+0.222
3.97e-14
0.633
+0.229
6.40e-15
CORAL
0.592
+0.264
1.41e-16
0.616
+0.245
1.30e-12
VELODROME
0.587
+0.269
8.01e-17
0.539
+0.323
1.44e-17
CELLIGNER
0.503
+0.353
6.03e-17
0.526
+0.335
1.44e-17
DSN-DANN
0.598
+0.257
1.83e-14
0.621
+0.241
4.91e-13
DSN-MMD
0.604
+0.252
6.40e-15
0.622
+0.240
8.55e-14
TransDRP
0.709
+0.147
1.29e-10
0.704
+0.158
2.10e-12
PREDICT-AI
0.679
+0.176
1.73e-05
0.691
+0.171
1.73e-05
PANCOR
0.652
+0.203
6.40e-15
0.652
+0.210
9.75e-16
DeepTTA
0.517
+0.339
3.41e-17
0.544
+0.318
1.44e-17
GraphCDR
0.558
+0.297
1.44e-17
0.551
+0.311
1.44e-17
25

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Published as a conference paper at ICLR 2026
A.17 THEORETICAL BASIS
A. A.17.1
FEATURE SPLICING STRATEGY
In transfer learning, concatenated pre-trained features with fine-tuned features. The core idea behind this operation is to decompose drug response representations into a "general" component and (domain--ppecine component, then fuse them through concatenation achieve better knowledge transfer.
Zpre: Represents universal drug response mechanism learned from extensive data on drug-cell line interactions. It encodes the most fundamental, cross-domain invariant interaction patterns between drug substructures and genomic functional pathway clusters. This feature is "frozen" and constitutes the model's prior knowledge.
Represents patient-specifie adaptations learned by the model after fine-tuning on small dataset of patients. It captures the influence of factors unique to the patient's body but absent in cell sncs-such as the immune microenvironment and tumor heterogeneity drug response.
finc:
From feature decomposition to risk minimization. As with other methods, we assume that the source domain (cell lines) and target domain (patients) share some common features, but also pos- sess private features. However, we do not merely consider common and private features in gene expression data from cell lines and patients; in DeepSADR, common features capture domain- transcending invariant patterns (such as core mechanisms of drug sune-substruuture interactions), while private features capture domain-specific factors (e.g., patient-specifie immune microenviron- ments or tumor heterogeneity). DeepSADR explicitly separates and fuses these two types of fea- tures by concatenating pre-trained and fine-tuned features, thereby: (1) Enhancing generalization: Regularizing the fine-tuning process with common knowledge from pre- pre-trained features to prevent small-sample overfitting. (2) Improving predictive performance: Enabling the model to dynamically balance common knowledge and domain-specific knowledge.
Assume the true drug response is determined by both common features c and private features p:
y f(c,p) +e
(31)
where is an unknown function and represents noise. In DeepSADR: The pre-trained features Epre approximate the common features c, learned from cell line data. The fine-tuned features 2 fine approximate the private features p, learned from patient data.
The concatenation operation [Zee 2pre constructs a joint feature representation, enabling the predictor P2 to learn the function f. From the perspective of risk minimization, the expected risk is:
R(P2) (x, (1
(32)
where E is the loss function (MSE), and D, is the patient data distribution. By minimizing this risk, the model simultaneously utilizes:
Common Feature Stability: 2pmr is learned from large-scale cell line data, providing
high-confidence prior knowledge.
Private Feature Adaptability: Ejine captures patient-specific mechanisms through fine- tuning.
This decomposition effectively reduces overfitting risk in small-sample learning because:
When patient data is scarce, the model tends to rely the common features in 2 avoiding overfitting to noise.
The tne-ttnnng process only updates a small number of parameters (AR and P2), further constraining model complexity.
26

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Published as a conference paper at ICLR 2026
Table 15: Results of drug cold experiments conducted on four different drugs
Doxorubicin
Erlotinib
Oxaliplatin
Sunitinib
Methods
AUC1
AUPRI
AUC1
AUPR
AUC1
AUPRT
AUC;
AUPR
DeepSADR
0.855/0.075
0.848/0.052
0.883/0.044
0.881/0.049
0.892/0.048
0,884/0.045
0.863/0.01
0.871/0.056
WISER
0.795/0.056
0.811/0.093
0.803/0.106
0.826/0.091
0.751/0.091
0.752/0.884
0.7970.087
0.783/0.092
GANDALF
0.799/00077
0.808/0.098
0.801/0.074
0.798/0.081
0.751/0.071
0.749/0.094
0.74910.089
0.737/0.096
CODE-AE
0.794/0.091
0.789/0.076
0.801/0.071
0,702/0.089
0.771/0.082
0.785/0.092
0.742/0.096
0.258/0.106
TRP
0.734/0.057
0.755/0.101
0.788/0.058
0.782/0.062
0.749/0.089
0.751/0.101
0.763/0.089
0,784/0.103
Note: Data related to clinical relapse is used for all evaluations. The results are reported as the mean/standard
deviation of multiple random seeds. Best performer among all baselines is in bold.
A. DRUG COLD SETTING
To further validate our model's predictive capability for drugs not yet tested in human trials, we conducted cold-start experiment. We selected four drugs from the patient drug response dataset that were not used during fine-tuning as untested drugs, and applied the fine-tuned model to predict outcomes for these four drugs. The corresponding results are shown in Table 15
19 OTHER EXPERIMENT
Table 16: Performance (AUC and AUPR scores) for 5 clinical drugs
Methods
Finorouracil
Temozolomide
Sorafenth
Gemeitablne
Cisplatin
AUCT
AUPR
AUC1
AUPRI
AUCT
AUC
AUPR
AUC1
AUPRI
DeepSADR
1,805/9,156
6.22/1.923
0,87000026
0.8669.029
0.967/1037
0,978/0824
0.719A8057
0.702/0.022
0.9E7NU027
0.9229.0211
DeepSADR
0.30
086180122 1.
0.579A0131
0.951/0.142
0.970N
0.716A2078
0.91900112 0/101/00222
DespAADR
0.70640110 103
80300067
082100.02
0.917/00744
8:907800044
0.719/0057
0.70200022
0.89780077
0.80200008
Note: Data related to clinical relapse is used for all evaluations. The results are reported as the mean/standard
deviation of multiple random seeds. Best performer among all baselines is in bold." " Indicates that only the top 30% of association weights are used in the sub-sequence interaction subgraph to replace threshold selection. denotes results obtained by training the model using standard cross-entropy loss.
A.20 GENE PARTITIONING STRATEGY
To achieve pathway clustering of genes, we designed method called marichment-based Gene Func- tional Clustering (ENRICH) within the DeepSADR framework (Kim et al.) 2008). This method combines prior biological knowledge with data-driven pattern discovery to specify gene clusters, and each pathway cluster coherent module may include multiple biological pathways that exhibit synergistic roles in cellular processes. The specific implementation process is as follows:
Multi-source pathway knowledge integration: First, we systematically extracted func- tional pathway association information for 1,426 genes from authoritative biological path- way databases such as KEGG 2021 (anehikaa et al. 2021) and Gene Ontology Bi- ological Process 2021 (Consortium et al. 2021) within the patient drug response database. This enables the construction of comprehensive pathway-gent mapping network, provid- ing biological foundation for subsequent functional module clustering.
Manual Definition of Core Functional Modules: Based on key mechanisms in cancer bi- ology, we predefined a set of core functional categories (c.g., signal transduction, cell cycle regulation, DNA damage repair, immune response, and metabolic pathways). Each cate- gory uses pathway keyword matching algorithms to preliminarily classify relevant genes into corresponding modules, with upper and lower limits set for module size ensure practicality and Flexibility.
Adaptive Clustering for Unassigned Genes:
For genes not assigned via predefined rules, hierarchical clustering was applied to intelligently regroup them based on co-occurrence patterns across multiple pathways. The number of clusters dynamically adjusted according
27

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Published as a conference paper at ICLR 2026
to the size of remaining genes and total target modules, forming "automatically discovered modules" that effectively capture novel functional combinations hidden in the data.
Full Coverage Assurance Mechanism: We introduced a mandatory assignment verifica- tion step. By applying pathway similarity matching and prioritizing the smallest modules, we guarantee 100
Aulti-dimensiona Quality Validation System: Each generated module undergoes qual- ity assessment via functional enrichment analysis (evaluating significantly enriched biolog- ical pathways) and the Jaccard similarity index (measuring functional consistency between the module and the global background). This ensures each module possesses both func- tional specificity and biological coherence.
In this paper, the number of gene functional clusters was determined through multiple experimental validations, it serves as a flexible and adjustable parameter. We conducted experimental verifications with various cluster sizes, with the specific results presented in Table 17
The superscript in the first column of the ta- ble denotes the number of gene functional clus- ters set for DeepSADR. DeepS ADR represents the optimal model from our paper, with a func- tional clusters count set to 13.
Table 17:
Experimental results for different number of gene functional clusters(average of drugs).
Methods
AUC1
AUPR
DeepSADR indicates that we do not cluster genes according the algorithm, treating all genes as a unified whole. DeepSADR 1426 dicates that each gene functional cluster
DeepSADR
0.856
0.862
DeepSADR
0.762
0.775
DeepSADR
0.781
0.779
The results of the DeepSADR experiment demonstrate that our functional clustering and grouping of genes is effective. The re- sults of DeepSADR indicate that insuf- ficient number of clusters leads to "underfit- ting" in the model, preventing it from captur- ing more complex underlying patterns in the data related drug and gene functional clus- tering. Conversely, an excessive number of clesters(DeeSAADRR and D22 causes the model to "overfit", causing it rigidly memorize noise and idiosynerasies from small number of samples rather than learning general- izable patterns. Additionally, we observed that experimental results remained relatively stable with minimal variation when the number of clusters was set between 11 and 15. After comprehensive consideration, we selected 13 as the optimal value for this study.
DeepSADR
0.847
0.854
DeepSADR
0.849
0.855
DeepSADR
0.848
0.859
DeepSADR
0.846
0.858
DeepSADR
0.763
0.751
DeepSADR 1426
0.653
0.639
Table 18 details the 13 gene functional clusters.
28

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Published as a conference paper at ICLR 2026
Table 18: The introduction of 13 Gene Functional Clusters.
ID
Gene functional Cluster
introduction
metabolic
Fundamental cellular life processes including energy metabolism, trient synthesis and degradation, and small molecule metabolism.
2
signal pathway
The recognition, transduction, and regulation of signals such as hor- mones, growth factors, and cytokines involve cell proliferation, differ- entiation, and stress responses.
3
Cell growth and differentiation
Stem cell differentiation, tissue development, neurogenesis, and other developmental and regenerative processes.
4
Cell cycle and proliferation
DNA replication, mitosis, initiation and termination of cell prolifera- tion.
5
Immune-related
Innate and Adaptive Immunity, Inflammatory Response, Pathogen Recognition and Clearance.
6
Cell transport and localization
Protein transport, endocytosis, ion transport across membranes, etc.
7
Cell stress and apoptosis
Oxidative stress, hypoxia, toxic substance responses, and programmed cell death (apoptosis) regulation.
8
Cytoskeleton and cell motor
Cytoskeletal reorganization, cell migration, and shape maintenance.
9
Aisease-associated
Mechanisms directly associated with specific diseases (such cancer, neurodegenerative diseases, infectious diseases).
10
Other
Sensory perception, tissue repair, gene expression regulation, ion bal- ance, and other diverse life activities.
11
Cancer
Mechanisms associated with cancer initiation and progression, such as unlimited proliferation, evasion of apoptosis, and angiogenesis.
12
Gene expression regulation
Transcription regulation, RNA processing, epigenetic regulation, etc.
13
Unknow
Pathways lacking clear classification or annotation information may represent novel biological processes functions not yet fully annotated in databases.
29


Reviews:
[{"review_id": "b1cruHFRb4", "reviewer": "Reviewer_rmkD", "summary": "The authors have tackled the problem of drug response prediction in patients, in the face of limited labelled data. They build on previous approaches in this space, and identify a pertinent gap in prior literature – substructures of drugs and genomic pathways can affect the response to treatment. They propose the use of graph autoencoders and a set transformer to obtain adaptive readouts. The method proposed – DeepSADR – is shown to outperform existing methods in this space.", "questions": "1.\tWhat is the computation overhead of this method, given that for every (patient, drug) pair, a separate interaction graph must be constructed on the fly? Please provide some intuition about this.\n2.\tIt is unclear how the model explicitly handles the biological differences in drug responses across patients and cell lines.  Is this offloaded to the trainable adaptive readout and predictor parts of the network in fine tuning? \n3.\tPlease comment on the data itself – are there batch effects? If so, how is it handled? I’m guessing this is already handled in the data from the earlier method, but please include this in the description.\n4.\tThe graph construction is also dependent on the choice of threshold t. Are there any sensitivity experiments done to understand the effect of this value?\n5.\tThe authors propose the use of drug response process as a different domain – the reason and intuition is unclear. \n6.\tThe model is finally trained in an inductive manner, like some of the prior methods. Please mention this.\n7.\tIn section 4.2, the results are found to be significantly better, please add p values to indicate significance.\n8.\tPlease combine the table 1 in results with the results from the appendix, so that the standard deviation can be seen.\n9.\tPlease include the process of hyperparameter tuning.\n10.\tPlease highlight the limitations of this model.\n11.\tPlease also include baselines like PANCDR[1] and TransDRP[2].\n12.\tThe description of the baselines seems erroneous, please update them \n13.\tIn table 7, the model without finetuning appears to do better – is there a reason why?\n14.\tIn Table 10, Sorafenib is repeated – was this not present in the earlier set of 5 drugs used for training?\n\n[1] Kim, J., Park, S. H., & Lee, H. (2024). PANCDR: precise medicine prediction using an adversarial network for cancer drug response. Briefings in Bioinformatics, 25(2), bbae088.\n\n[2] Liu, X., & Li, M. (2025, April). Knowledge-Guided Domain Adaptation Model for Transferring Drug Response Prediction from Cell Lines to Patients. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 1, pp. 523-531).", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThe problem is well motivated and the research gap identified is relevant.\n2.\tExtensive ablation studies have been conducted to show the importance of each piece in the proposed architecture.\n3.\tVisual analysis of the most important substructures was also done, which is good for interpretability.", "weaknesses": "My key concern is that the reason for treating \"drug response\" as a separate domain is unclear. The results do appear to improve but it would be good to add some intuition on why the improvement is seen.\n\nA few baselines seem to be missing, it would be good to include them -  more details in the Questions section below.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Bfm0VOCVg1", "reviewer": "Reviewer_KQZE", "summary": "This paper presents DeepSADR, a transfer learning framework for predicting cancer drug responses in patients using cell line data. It models drug-gene relationships as bipartite subsequence interaction graphs and uses a supervised graph autoencoder to learn interpretable latent features. An adaptive readout module based on the Set Transformer enables domain-invariant feature learning between cell lines and patients. Experiments show that DeepSADR outperforms existing methods and provides improved interpretability.", "questions": "Please see the weaknesses section for further discussion.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper is clearly written and well-structured, with a strong motivation based on the challenge of predicting patient drug responses from scarce clinical data and heterogeneous genomic profiles.\nThe proposed subsequence interaction graph and adaptive readout mechanism are conceptually sound, capturing fine-grained drug–gene associations while enabling domain-invariant feature learning, making the technical approach meaningful and biologically interpretable.\nThe experimental setup is comprehensive, including multiple clinical cohorts, extensive ablation studies, and comparisons with state-of-the-art baselines, demonstrating robust performance, good generalization, and practical applicability for personalized cancer treatment prediction.", "weaknesses": "Major: A key weakness is the limited dataset size for several drugs, which may undermine the reliability and generalizability of the model’s predictions. In particular, drugs such as Sorafenib (26 samples) and Cisplatin (40 samples) have very few data points, increasing the risk of overfitting. The paper also does not discuss strategies to mitigate issues arising from small sample sizes or class imbalance, which could affect the robustness of the experimental results.\nMinor: In Table 1, for Gemcitabine, DeepSADR achieves an AUPR of 0.702, which is lower than WISER (0.752), yet it is marked in bold. Additionally, Appendix A.1 (Pseudocode and List of Notations) is disorganized and difficult to read. Some recent relevant models, such as PREDICT-AI (https://doi.org/10.1145/3637528.3671652), are missing from the discussion.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "4sznODAUU2", "reviewer": "Reviewer_B6CY", "summary": "The authors present a novel training paradigm that leverages SMILES representations of drugs and enhanced genomic features to effectively recommend drugs for patients. While the approach is interesting and potentially impactful, several key methodological details are insufficiently explained. This lack of clarity makes it difficult to fully understand how the proposed method addresses the limitations and challenges of prior baselines and achieves its reported performance gains.", "questions": "- (L 233-235) The necessity of modeling long-term feature dependencies for molecular graphs is unclear. Compared to graphs in other domains, molecular graphs are typically smaller in size. Why, then, is long-term dependency modeling essential in this context? Has an ablation study been conducted to justify this design choice?\n\n- Suggestion : Consider revising Equation (7). Presenting both the cell line and patient genomic profiles together in the same formulation may confuse readers.\n\n- (L 261-269)The interpretability of the subsequence graph remains unclear. Do the associations learned by the model have any biological or scientific validation? Could the approach potentially introduce spurious associations? Given the limited size of the patient genomic dataset, such issues might substantially affect the model’s generalization ability.\n\n- Why not utilize a drug–gene interaction database, such as DGIdb(https://dgidb.org/browse/sources), to construct an interaction graph? \n\n- Why is mean squared error (MSE) used for a binary prediction task? Wouldn’t the standard cross-entropy loss be more appropriate for categorical outputs?\n- Please clarify what is used as the prior probability term  p(z) in Equation (18).\n\n- What is the rationale behind concatenating pre-trained and fine-tuned features in Equation (19)? Is this decision empirically motivated? Also, based on Figure 1, does the concatenation involve cell line and patient genomic profiles? Since these come from different domains with no one-to-one correspondence, I have doubts about its implementation. Please clarify how this concatenation is implemented. let me know if I have misunderstood any part. \n\n- Is fine-tuning performed on both the patient and cell line datasets? Please clearly specify which datasets belong to the pre-training and fine-tuning stages.\n- How is the evaluation conducted? Is it based on k-fold cross-validation, or on the mean performance across multiple independent runs (e.g., five runs)?\n- Which feature representations are used for the t-SNE plot in Figure 4—pre-trained, post-trained, or concatenated features?\n- Please elaborate on Figure 2. How should the results be interpreted, and how statistically or biologically significant are these findings?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The present work aims to bridge the gap between laboratory-based drug studies and computational learning approaches for cancer drug recommendation. \n- The integration of SMILES representations and genomic enrichment for predictive modeling is well justified and conceptually sound. \n- The paper is well written, especially the introduction section.\n- The results are encouraging and demonstrate clear improvements over the baseline models.", "weaknesses": "- While the proposed approach is interesting, I have concerns regarding how the method learns a domain-invariant representation between the cell line and patient genomic profiles, given that there is no explicit training objective designed for domain alignment.\n\n- The authors have not clearly specified which datasets are used during each phase of training (e.g., pre-training vs. fine-tuning). This lack of clarity makes it difficult for readers to fully understand how the reported performance improvements are achieved. \n\n-  The biological interpretability experiment is insufficiently explained. Further clarification is needed to understand the biological insights or validations derived from the proposed model.\n\n-I have several questions regarding the implementation of the method. Please refer to the “Questions” section for detailed queries. If these issues are adequately addressed, I would be open to update my score.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xdSc0ZMDi0", "reviewer": "Reviewer_GBzW", "summary": "This paper introduces DeepSADR, a deep transfer learning framework designed to improve drug response prediction for clinical patients. The core idea is to decompose drugs and genomes into subsequences (substructures and functional pathways, respectively), model their interactions in a graph, and use an adaptive readout mechanism for knowledge transfer. The experimental results demonstrate that DeepSADR achieves performance improvements. However, the approach is essentially a combination of existing components, and the training framework itself is not new. In addition, the method relies heavily on predefined prior knowledge (such as fixed functional pathways and drug-specific thresholds), which limits its generalizability.", "questions": "See weaknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The methodological pipeline is clearly described, and the overall framework is easy to follow.\n\n2. The proposed approach demonstrates substantial performance improvements over existing baselines", "weaknesses": "1. The paper states that \"we assigned the genes to 13 functional pathways\", but it is unclear how this assignment is performed and why these 13 fixed pathways are predefined. For patients with different diseases, should the pathways be redefined? Since each patient’s pathway profile can differ, these fixed and limited pathways may hinder the model’s generalizability. Moreover, such a strong prior assumption may fail to capture disease-specific biological processes or drug-specific mechanisms, especially across different cancer types.\n\n2. The paper does not clearly describe the data split strategy. Were the baseline models retrained by the authors? Some reported results differ from those in prior literature. For example, WISER reports an AUC of 0.868 for CODE-AE on Fluorouracil, which is inconsistent with the results shown in Table 1.\n\n3. The model appears highly sensitive to threshold parameters $t$, with different thresholds set for each drug. This indicates that the model requires fine-grained hyperparameter tuning for each individual drug. In practice, this is a major limitation as it requires sufficient labeled data for each new drug to determine the optimal threshold, which contradicts the established goal of addressing the scarcity of clinical data.\n\n4. In the ablation study, removing either the AR or SN module leads to a significant performance drop. This suggests that using only one of these modules appears to be ineffective, whereas combining them yields considerable performance gains. This is unusual, and further experiments are needed to elucidate the interactions between these modules.\n\n5. The interpretability validation is insufficient. The interaction heatmap for Temozolomide is shown without deeper analysis of the underlying biological mechanisms, leaving the reader uncertain about the biological relevance of the findings.\n\n6. In Equations (15), (16), and (17), several symbols (e.g., s, E, Y) are not defined, making these formulas difficult to interpret.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)