Mechanism-Aware Prediction of Tissue-Specific Drug Activity via Multi-Modal Biological Graphs
Authors: Sally Turutov
Kira Radinsky
Venue: Accepted by TMLR ()

Abstract: Predicting how small molecules behave across human tissues is essential for targeted therapy development. While some existing models incorporate tissue identity, they treat it as a label—ignoring the underlying biological mechanisms that differentiate tissues. We present Expresso, a multi-modal architecture that predicts tissue-contextual molecular activity as measured by the assay by modeling how compounds interact with transcriptomic and pathway-level tissue context. Expresso constructs heterogeneous graphs from GTEx data, linking samples, genes, and pathways to reflect expression profiles and curated biological relationships. These graphs are encoded using a hierarchical GNN and fused with frozen molecular embeddings to produce context-aware predictions. A multi-task pretraining strategy—spanning gene recovery, tissue classification, and pathway-level contrastive learning—guides the model to learn mechanistically grounded representations. On nine tissues, Expresso improves mean AUC by up to 27.9 points over molecule-only baselines. Our results demonstrate that incorporating biological structure—as defined by the assay—yields more accurate and interpretable models for tissue-specific drug behavior in human cell-based in vitro assay systems.

Published in Transactions on Machine Learning Research (02/2026)
Mechanism-Aware Prediction of Tissue-Specific Drug Activity via Multi-Modal Biological Graphs
Sally Turutov
hurttosally@anmpp..eeeiiio.cco
Technion Israel Institute of Technology
Kira Radinsky
kirar www.setc.esiscc....
Technion Israel Institute of Technology
Reviewed on OpenReview: https: // opeereveee.e.... formn? id- UDW8m91
Abstract
Predicting how small molecules behave across human tissues essential for targeted ther- apy development. While some existing models incorporate tissue identity, they treat as label ignoring the underlying biological mechanisms that differentiate tissues. We present Expresso, a multi-modal architecture that predicts tissue-contextual molecular ac- tivity measured by the assay by modeling how compounds interact with transcriptomic and pathway-level tissue context. Expresso constructs heterogeneous graphs from GTEx data, linking samples, genes, and pathways to reflect expression profiles and curated bio- logical relationships. These graphs are encoded using a hierarchical GNN and fused with frozen molecular embeddings to produce context-aware predictions. A auulti-task pretrain- ing Seaateyy-spanning gene recovery, tissue classification, and pathway- contrastive learning guides the model to learn mechanistically grounded representations. On nine Lis- sues, Expresso improves mean AUC by up to 27.9 points over molecule-only baselines. Our results demonstrate that incorporating biological structure as defined by the assay yields more accurate and interpretable models for tissue-specific drug behavior in human cell-based in vitro assay systems.
1 Introduction
While machine learning has advanced rapidly in molecular modeling, many stato-of-tho-art models still evaluate drug compounds in isolation -ithhu considering the biological context which those molecules operate. In reality, compound's effect depends not just on its chemical structure but also on the specific tissue it enters, which genes are expressed there, and which biological pathways are active. These factors fundamentally shape how drug behaves in the body. As result, models that ignore biological context often struggle to generalize to new tissues or disease states, limiting their reliability in real-world applications.
Some recent approaches incorporate biological or cellular context alongside molecular information for ex- ample, by embedding the identity of the cell line or by integrating transcriptomic profiles into the predictive model (Oskooei et al. 2018 Wu al.] 2025 Shahzad et al. 2024p. While this provides useful context, these mothods typically represent collular state as a flat label or static feature vector, without explicitly modeling the underlying regulatory programs, pathway structure, and gene gene interactions that shape casuee-oontetual responses measured by the assay. In the absence of such mechanistic structure, predictions may become brittle and miss critical determinants of tissuo-specific drug behavior.
At the same lime, large-scale transcriptomic datasets across human tissues have become increasingly ac- cessible. These datasets don't merely label tissues they characterize them, offering detailed, sample-level insight into gene activity and biological pathway dynamics. When properly integrated, this wealth of biolog- ical structure can serve as a powerful inductive bias for molecular prediction. Yet, most current architectures lack the capacity to exploit this information to generate biologically meaningful predictions.

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We address this gap by introducing Expresso: Expression and aathway-driven Representation for Enhancing Sampie-contaal and Specific Organ activity prediction. Expresso is a biologically informed framework that integrates molecular embeddings with graph-based tissue representations built directly from gene expression and curated pathway networks. Instead of abstracting tissuc context as a flat label, Expresso constructs a heterogeneous graph per where samples, genes, and pathways form a structured system. This graph encodes both expression magnitude and biological relationships, allowing the model learn tissuc-contextual activity as defined by the assay.
The Expresso architecture utilizes hierarchical tissue encoder that performs message passing over sam- ple-gene-pane pachhapathway graphs. This enables the model to learn rich tissue embeddings conditioned on both observed expression and known biological priors. During training, Expresso is optimized not only for molec- ular activity prediction but also for self-supervised tissue modeling reconstructing masked gene expression, classifying tissue identity, and aligning pathway-level representations. This multi-objective sctup strengthens gencralization and interpretability of the model.
The contributions of this work are threefold: (1) We introduce Expresso, an architecture that integrates molecular structure with rich, expression-driven biological context by leveraging heterogeneous graphs com- posed of samples, genes, and pathways specific to each passue-enabling accurate molecular activity predic- tion in complex biological environments. (2) We develop a novel graph-based tissue encoder, trained via a multi-objective learning framework, that effectively captures both the functional dynamics and structural organization of tissues by jointly modeling real gene expression data and curated pathway priors. (3) We provide new biological insights by identifying key pathways that mediate compound effects across diverse tissues, facilitating more interpretable and biologically grounded predictions.
By emneddding molecular prediction in rich, tissue-specific biological context derived from human transcrip- tomic resources, Expresso moves toward more biologically faithful in-silico pipelines capable of modeling compound behavior with organ-level resolution as reflected in our in vitro assay panel. This enables more informed prioritization of candidate molecules based on their predicted tissue-specific activity in these assay- defined tissue contexts, potentially improving the efficiency and focus of subsequent experimental valida- tion. We make our code and data openly available to the community for further study of the problem:
2 Related Work
Recent advances in molecular representation learning have produced powerful models for chemical prop- erty prediction. Approaches such as Mol-BERT (Li & Jiang (021). MolFormer (Wu et al. 2023). MMELON (Suryanarayanan et al.) 2024), ChemBERTa (Chithrananda et al. 2020), and GROVER (Rong et al. 2020 learn rich, structure-aware representations from SMILES or molecular graphs, enabling strong performance on tasks such as solubility, toxicity, and general physicochemical property prediction. KA- GNNs (Bresson et al., 2025)) further expand this capacity using heterogeneous graph structures with kernel- based attention to capture higher-order chemical dependencies. While effective for chemical-centric tasks, these methods operate on molecules in isolation and do not incorporate biological context, limiting them.
Several approaches attempt to include biological information alongside chemical representations. CLAMP (Seidl et al. 2023) aligns compounds with assay descriptions using contrastive learning, but the bio- logical signal is limited to unstructured text. Domain-specific language models such as BioBERT (Lee et al., 2020) capture biomedical scmantics for NLP tasks but are not designed to reason over molccular structures or bissue-specific gene expression. Pathformer (Liu al., 2024) models gene pathway and pathway pathway relationships to improve interpretability in gene-level prediction, yet it does not support molecular input tissuc-specific expression data. These methods highlight the benefits of integrating biological structure, but they are not directly applicable to tissue-resolved molecular activity prediction.
Multimodal frameworks such as DeepCDR (Wu et al. 2022) and MultiDCP (Liu et al. 2020 combine molec- ular fcatures with cell line-specific omics profiles to predict transcriptional perturbations or drug response. These approaches demonstrate that integrating chemical and cellular information can improve predictive performance.
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Expresso unifies molecular encoders with structured biological knowledge. For each human tissue, Expresso leverages human cell-based assay data (predominantly established cell lines, with some primary cell systems where available), gene expression profiles, and pathway-level organization to create biologically informed embeddings that capture tissuc-specific context as defined by these assays. This enables predictions of molecular activity that are sensitive lo assay-specific tissue context, bridging part of the gap between purely chemical models and biologically informed in vitro tissue response.
3 Problem Definition
We address the problem of lissue-specific molecular aclivity prediction, where the goal is to determine whether given molecule is active in a particular human tissue. This is framed as binary classification task, where each input consists of pair: molecule and a target tissue. The model must whether the molecule exhibits tissue-contextual activity as measured in the assay.
Formally, each prediction instance is defined by: (1) A molecule m E M, represented by its SMILES string. (2) A taryel lissue E T, from a set of human tissues. The model f : M x T + {0, 1} is trained to predict whether m is active in tissue t. Here, "activity" indicates whether compound significantly affected a tissue-specific, biologically defined endpoint in human cell-based assay-eithe primary cells established human cell lines (c.g., cytokine release in lung cells or transcription factor activation in vascular cells); see Appendix A.2 for details.
To accurately model tissue-specific activity, the representation of each tissue must capture its full biological context. Rather than treating tissue as a categorical variable, we aim to build a learned embedding for each tissue that integrates multiple biological components. In particular, we leverage: (1) Samples: Each tissue is represented by multiple samples, where each sample corresponds to gene expression profile derived from a different human donor. Each sample provides vector of gene expression values (c.g., TPMs), giving insight into the transcriptional landscape of the tissue. (2) Genes: These are the measured units in each sample. Each gene has quantitative expression level. The expression patterns across samples form the basis of the tissue's molecular signature. (3) Pathways: Biological pathways define sets of genes that participate in shared cellular processes or functions. By incorporating pathway membership information (e.g.. a binary gene-pathwar matrix), we can group genes into functionally meaningful modules that reflect regulatory and signaling mechanisms.
4 Model Architecture
We present Expresso, biologically informed model that predicts molecular activity across human tissues by integrating chemical and transcriptomic data. It combines molecular encoder with hicrarchical graph- based tissue encoder. Below, detail the molecular encoder, graph construction and message passing, training losses, and the overall model architecture.
4.1 Molecular Encoder
To capture the structural properties of molecules, our model uses a pretrained molecular encoder that maps chemical inputs into contimous vector representations. Specifically, we adopt the compound encoder from CLAMP (Seidl et al. (023). which has been trained on large-scale chemical-assay data using contrastive learning. This encoder processes molecular SMILES strings and generates fixed-dimensional embeddings that capture chemical features, which can be predictive of bioactivity when paired with biological context.
Formally, the encoder defines mapping f M - IRd, where M denotes the space of molecules and d is the embedding dimension. Given a molecule m E M, the output embedding is m = f(m). These embeddings serve as the molecular input to our downstream fusion and prediction modules.
Although we use CLAMP our implementation, the architecture modular and compatible with alternative molecular encoders. This design choice allows flexibility in adapting different data settings or incorporating domain-specific encoders in future applications.
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4.2 Tissue Graph Construction
To represent the complex biological landscape underlying tissuo-specific molecular activity, we construct a heterogeneous graph that captures interactions across multiple biological levels: samples, genes, and path- ways. This graph serves as the foundational structure for downstream tissue representation learning. Fach tissue is modeled as an independent graph that integrates biological prior knowledge with tissue-specific measurements derived from gene expression data.
4.2.1 Node Types
The heterogeneous graph includes three distinct node types: (1) Sample nodes (Va), corresponding to indi- vidual biological samples. These nodes represent observed instances of gene expression within tissue and preserve biological variability across samples. (2) Gene modes (Vg). corresponding to protein-coding genes measured in the transcriptomic profie. Each gene node encodes the role of a specific gene in mediating molccular function. (3) Pathway nodes (Vp), representing curated biological pathways, which serve as bio- logically curated modules that organize genes inlo coherent functional units, enabling structured reasoning about cellular processos.
Let N, G, and P denote the mumber of samples, genes, and pathways respectively in given tissue graph. The full node set is thus V V. UV, U.p.
4.2.2 Edge Types
Edges in the graph encode functional and structural relationships across the biological hicrarchy: (1) Sam- ple- Gene edges (Esesg), an undirected bipartite edge is formed between sample node v, E V, and gene node 1g E V, if the gene g has nonzero expression in that sample s. This relation captures the biological ob- servation that samples express specific genes. The corresponding binary edge index is encoded as sparse adjacency matrix Agg (0, NxG derived directly from the gene expression vensor X RR by thresh- olding nonzero entries. (2)
edges (Egez) exist between gene nodes vg E V and pathway nodes Up E Vp, defined according to binary gene pathway membership matrix M E {0, 10 where Mp = indicates that gene is known to participate in biological pathway p. This relation encodes structured biological prior knowledge from curated databases, capturing modular and interpretable groupings of genes that function together. These edges define a bipartite graph that links individual molecular measurements to higher-level biological functions. (3) Pathway Pallway edges (Epesp) model functional dependencies be- tween biological processes by connecting pathway nodes Up. Ups Vp via
Sathwwa--pthwaar adjacency matrix App E {0, PxP,, where (App)pira 1 reflects known crosstalk regulatory interactions between pathways Pı and p2. These edges capture relationships such as shared downstream targets, feedback loops, or co-activation, enabling informat ion exchange between functionally related modules and supporting more holistic representations of tissue-specific context.
4.2.3 Node Features
Each node type is initialized with a biologically meaningful feature vector: (1) Sample features are computed from the raw gene expression profile. We first apply a simple transformation and normalization to obtain X Nxx Then, each gene expression value is projected to a higher-dimensional space using a feed- forward network, producing sample-specific gene embeddings. These are aggregated across genes to produce sample feature vector he E dd per node. (2) Gene features are initialized using the mean expression of each gene across samples. This results in G-dimensional vector, further transformed using the same feed- forward encoder as above, yielding hg E IRd, (3) Pathway features are derived from structural properties of the pathway pathway-pathwwy network. We compute graph-theored coofficient, along with spectral embodding of the adjacency matrix App (via Laplacian eigenmaps (Belkin) & Niyogi (003)). These features are concatenated and standardized, producing a vector for each pathway, which is then projected into the GNN's hidden dimension: hp E Rdd.
statistics such as node degree and clustering
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4.2.4 Biological Motivation
This graph formulation is biologically grounded and designed to capture the multi-scale organization of cellular function. By modeling samples explicitly as nodes, the graph preserves inter-sample variation, allowing the tissue representation reflect within-tissue heterogeneity in gene expression. The inclusion of gene and pathway nodes enables mechanistic information flow through curated biological structures, supporting both interpretability and generalization. Furthermore, pathway haahway-pathway edges encode regulatory coordination between biological processes, allowing the model to reason at a systems level. Altogether, the constructed heterogeneous graph G = (V,E) provides biologically meaningful representation of tissue context, which then processed by a multi-layer heterogencons GNN to produce compact, informative tissue embeddings for molecular activity prediction.
4.3 Graph Message Passing
To transform the constructed heterogeneous graph G EV,E') into a compact, informative tissue embedding, we employ hicrarchical graph neural network (GNN) architocture that itcratively refines node representa- tions through multi-relational message passing.
4.3.1 Initial Node Embeddings
For each node type, initial feature vectors are defined as follows: (1) Somple nodes V2 E V, are assigned features dcrived by projecting normalized gene expression profiles X R NxG through food-forward encoder, aggregating per-gene embeddings to form a sample representation (h E R². (2) Gene nodes Vg E V, are initialized using the mean expression across samples, transformed via the same encoder, yielding h Rd. (3) Pathway nodes Vp Vp encode structural graph properties of the pathway-pathway network using concatenated graph-theoretic features and spectral embeddings, linearly projected to obtain h, E Rd.
4.3.2 Layer-wise Message Passing
At GNN layer f, each node updates its embodding by aggregating information from neighbors of different types connected via specific edge relations r E R. Formally, for node o:
(++1) = Norm M + Σ Σ
1 [N((r]
Cony 13 (1(())
(E,,
Here, (o denotes a relation-specific graph convolution layer (e.g., SAGEConv (Hamilton et al. 2017 or GATv2Conv (Boody et al. 2022)) tailored to edge type r, (v) the set of neighbors under relation r, and Norm a layer normalization operation. Residual connections ensure stability and mitigate over-smoothing across layers.
This multi-relation propagation mechanism integrates signals bidirectionally between samples and genes, between genes and pathways, and among pathways, thereby capturing local molecular activity, functional grouping, and higher-order regulatory crosstalk.
4.3.3 Tissue-level Embedding Aggregation
After such layers, sample node embeddings {n((4) represent contexl-aware molecular profiles within the tissue. To produce a fixed-length tissue embedding z Rd, these sample embeddings are pooled via a multi-query attention mechanism:
z = MuttiQuuryPPooing( (e("))
This pooling adaptively weights samples' contributions through learned attention queries, allowing the model to capture both dominant and subtle expression patterns relevant for tissue identity and function. Concretely,
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parameterize Q learnable queries, each of which applies standard multi-head attention Vaswann et al, 2017) over the set of sample embeddings. The resulting Q context. vectors are averaged to obtain z.
Overall, this hierarchical GNN encoding framework enables biologically informed integration of heterogeneous molecular and functional information at multiple scales. By explicitly modeling samples, genes, and pathways with distinct features and relations, and leveraging multi-relational convolutions plus attentive pooling, the model learns rich, interpretable tissue representations that reflect both intra-tissue heterogeneity and systemic biological organization.
4.4 Multi-Task Loss Functions
To ensure that the learned tissue embeddings capture meaningful biological signals and generalize well for downstream molecular activity prediction, train the model using a combination of complementary loss functions. These losses jointly encourage accurate reconstruction of molecular measurements, tissue classification, and robust representation of pathway-level functional modules.
4.4.1 Gene Expression Reconstruction Loss
The heterogeneous graph explicitly models gene expression through sample-gene edges, enabling self- supervised learning by masking fraction of gene expression values and tasking the model with their recon- struction. Let X E NRG denote the normalized gene expression matrix, and M S f,,..., denote the set of sample gene index pairs whose expression values are masked during training, randomly sam- pled across all samples and genes according to a fixed masking ratio. The model predicts masked expression values Xij via the inner product of learned sample and gene embeddinggs:
(I...., G}
x,,= - h (t,))
The reconstruction loss is defined as mean squared error over masked entries:
Σ X1. - Xi,) )²
fgeue
[MI
(i.))M
This loss encourages the model learn embeddings that preserve detailed gene expression patterns, facili- tating recovery of missing molecular signals.
4.4.2 Tissue Classification Loss
To encourage Lissue-specific features in the embedding space, we supervise the model classify Lissue identity from the aggregated tissue embedding z. Given C tissue classes and a ground-truth label y E (l,..., C*}, lincar classifier fels predicts logits l RC:
= fzla
The tissue classification loss uses cross-entropy:
Cets log exp(Ly)
Σ exp(le)
This supervision guides the tissue embedding to capture discriminative features relevant to tissue identity.
4.4.3 Pathway Contrastive Loss
To promote pathway embeddings that reflect known functional relationships and are robust noise, we ap- ply contrastive loss two stochastic embeddings of each pathway node obtained via independent passes

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through the GNN. For each pathway node p. let 2 (1) 22 Rd be its embeddings from Chese indepen-
dent stochastic passes. Positive pairs (zp 22p =p)) are contrasted against negative pairs with other pathway embeddings within the batch.
Using cosine similarity sim(..), the loss for pathway p is:
exp (sim Zp /zzp)/7
f== log
(2) Eq=1 exp (sim( 2pp 2 1/T)
where > 0 is temperature hyperparameter. The total pathway contrastive loss is averaged over all pathways:
P Σ
paahwaay
4
This design leverages inherent stochasticity of the model generate informative positive pairs, enabling robust and functionally meaningful pathway embeddings without requiring explicit graph angmentations.
4.4.4 Activity Prediction Loss
The primary supervised objective molccular activity prediction. Given molccular representation and tissue embedding, the model predicts scalar activity score. Let m; denote the molecular embedding obtained from the molecular encoder, and t; the tissue embedding produced by the tissue encoder for the i-th input. These linearly projected and concatenated to form joint representation:
2; W m Weta]:
where Wm m and W, are learned projection matrices, and [- || denotes vector concatenation. A classifier then maps 2, to logit E R, representing the predicted activity score.
Given the ground-truth binary label ya E (0, we minimize class-weighted binary cross-entropy loss:
Lactivity
B Σ Ja yi log (f6) + (1 y)) log(1 - ((:)))
where B is the batch size, (.) is the sigmoid function, and Nuss is the positive-class weight used mitigate class imbalance. This formulation balances contributions from active and inactive compounds, improving learning stability across tissues with skewed activity distributions. Overall, this loss directly supervises the model to distinguish active from inactive molecular responses in tissue-specific context.
Nposte
4.4.5 Overall Objective
The full training objective is a weighted sum of these losses:
= Lactivity + Agene + dets + Apathway Spathway,
where each À is a tunable hyperparameter controlling the influence of the corresponding auxiliary task. This multi-task formulation enables the model leverage both labeled activity data and structured biological information, encouraging tissuc embeddings reflect geno-level variation, tissue identity, and pathway-level organization all while remaining optimized for the end predictive task.
4.5
Model Overview and Training
The full model consists of two main components: (1) molecular encoder, and (2) a tissue encoder that constructs graph-based tissue embeddings from gene expression data integrated with curated biological

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Molecule
Molecular
Embedeinng
Molecula Encoder
Activity Prediction 0/
Activity Cleasifier
Tissue
Tissue Embbedding
Tiseue Encoder
Thesue Groph Construction
Lungs
Heart
Liver
expresses
Tissue Encoder
122
Tesue-level
Embeddi
P2
Embedcing Aggregation
2s
P5
9 in pathway
Figure 1: Overview the Expresso model. A heterogeneous Sample- Potne-Patwwa graph captures tissue- specific structure by integrating gene expression, gene-pathway links, and pathway-pathwar crosstalk. Node types color-coded: yellow for samples (v.), pink for genes (vg), and green for pathways (up).
knowledge. These two embeddings are concatenated and passed through a classifier to predict molecular activity.
We first pre-train the tissue encoder using self-supervised tasks gene expression reconstruction, tissue clas- sification, and pathway-level contrastive learning across all tissues without molecular labels. During su- pervised training, the molecular encoder is frozen while the tissue encoder is fine-tuned. The combined loss, blending binary cross-entropy with self-supervised objectives, promotes biologically grounded and transfer- able tissue representations.
5 Experimental Results
In this section, we empirically evaluate the effectiveness of Expresso for tissue-specific molecular activity prediction. We first present the main results across nine human tissues, followed by an in-depth ablation analysis to dissect the contribution of each model component.
5.1 Main Result: Activity Prediction
We benchmark Expresso against six representative baselines spanning language-based, graph-based, and cross-modal paradigms. Full implementation and fine-tuning details for each baseline are provided in Ap- pendix A.3 Additional evaluation metrics and extended results are provided in Appendix B Table [ summarizes the mean AUC scores per tissue. Expresso consistently outperforms all baselines across ev- ery tissuc, achicving an average AUC of 0.812. This corresponds to 3.7% relative improvement over the best-performing baseline, BioBERT.
Transformer-basee molecular and biochemical language models (BioBERT, Mol-BERT and MolFormer) consistently achieve strong AUCs in tissues where larger numbers of compound-tissue examples are available (e.g., Liver, Breast), suggesting that these pretrained text- and molecule-based representations capture broadly useful sremical-semaniee features when sufficient fine-tuning data are present. Their performance, however, docreases tissues with limited samples (c.g., Brain) or pronounced class imbalance (c.g., Cervix, Kidney), reflecting the reduced ability purely sequence- or text-derived encoders to capture tissuoo-pecific biological variation under data-scarce imbalanced conditions.
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Table 1: Mean performance of Expresso for predicting molecular activity in human tissues, measured by ROC-AUC. Results show Expresso and baseline models, averaged over 5 runs with different random seeds. Higher values indicate better accuracy,
Model
Brain
Breast
Cervix
Kidney
Liver
Lung
Ovary
Prostate
Skin
Expresso
0.6471
0.8233
0.9325
0.8764
0.8207
0.8763
0.7466
0.6953
0.8950
BioBERT
0.6297
0.7867
0.8948
0.8340
0.7796
0.8526
0.7234
0.6848
0,8622
Baselines
CLAMP
0.5546
0.7849
0.8815
0.8038
0.7712
0.8411
0.6899
0.6547
0,8648
GROVER
0.5844
0.6018
0.6060
0.6056
0.6062
0.6017
0.6020
0.6208
0.6017
KA-GNN
0.6008
0.7538
0.8461
0.7878
0.7491
0.8242
0.6935
0.6681
0.8122
Mol-BERT
0.5630
0.7760
0.8840
0.8324
0.7777
0.8582
0.7137
0.6753
0.8672
MolFormer
0.5882
0.7886
0.8989
0.8356
0.7834
0.8458
0.7049
0.6588
0.8683
Mol-BERT Encoder
0.6387
0.7654
0.8885
0.8323
0.7764
0.8534
0.7032
0.6635
0.8741
ChemGPT Encoder
0.6465
0.5699
0.6056
0.5563
0.5762
0.5247
0.5812
0.5854
0.5866
No Tissue Encoder
0.5756
0.7871
0.8895
0.8061
0.7739
0.8173
0.6954
0.6781
0.8413
Non-Craph Encoder
0,5672
0.7855
0,8884
0,8291
0.7801
0,8321
0.7149
0.6509
0,8480
One-Hot Encoder
0.6076
0,8084
0,8962
0,8534
0.7885
0,8568
0.7425
0.6862
0.8649
Ablation
Text-Bases Encoder
0.6092
0.8007
0.8993
0,8312
0. 0.7849
0.8499
0.7222
0.6994
0.8718
Mean Tissue Exprcssion
0.6176
0.7891
0.8886
0.8325
0.7812
0.8416
0.7100
0.6718
0.8666
No Pretraining
0.6180
0.8001
0.9110
0,8552
0. 0.7924
0.8579
0.7242
0.6759
0.8663
Only factivity
0.6176
0.8097
0.8941
0.8493
0.7938
0.8537
0.7147
0.6707
0.8743
factivity Leene
0.6361
0.8209
0.9106
0.8672
0.8129
0.8695
0.7322
0.6738
0.8812
Cactivity + Lele
0.6134
0.8126
0.9074
0.8481
0. 0.7941
0.8592
0.7132
0.6733
0.8743
Cactivity + Cpathway
0.6218
0.8141
0.9112
0.8553
0.8052
0.8695
0.7200
0.6786
0.8731
Only Sample Nodes
0.5966
0.7876
0.8979
0.8208
0.7801
0.8370
0.7016
0.6803
0.8578
No Gene Nodes
0,6050
0.8220
0.9054
0.8578
0.7998
0,8626
0.7372
0.6717
0.8776
No Pathway Nodes
0.6387
0.8091
0.9044
0.8616
0.7949
0.8596
0.7328
0.6559
0.8733
Graph-based methods (GROVER, KA-GNN) and the cross-modal baseline (CLAMP) display complementary limitations that explain their comparatively lower performance. Graph encoders effectively capture molecu- lar topology and local substructures but lack mechanisms to integrate tissue- or sample-level transcriptional information; consequently, they struggle to model differential compound activity driven by tissue-specific molecular mechanisms. CLAMP's multimodal design partially mitigates this by coupling molecular and ual embeddings, yet its contrastive fusion does not fully leverage expression- or pathway-level struc- ture leading to weaker biological grounding.
In contrast, Exprcsso explicitly integrates molecular structure, sample-level gene expression, and pathway topology, enabling context-aware molecular representations that better capture (issue-contextual patterns of compound activity. This biologically grounded integration yields more accurate and generalizable predictions across diverse human tissues.
5.2
Ablation Experiments
To understand the contributions of Expresso's design components, we conduct an ablation study focusod on three key aspects: the encoders architecture, self-supervised objectives, and the biological graph structure.
5.2.1 The Role of the Molecular Encoder
This experiment examined how the choice of molecular encoder influences downstream performance. While Expresso employs the CLAMP (Scidl et al.) 2023) molccular encoder, we replaced it with two alternatives to test different representation strategies: Mol-BERT (Li & Jiang] 2021). a Transformer trained on SMILES strings, and ChemGPT (Frey et al. 2023). a generative model trained on textual molecular data. Mol- BERT performs reasonably well, showing that SMILES--bsse pretraining captures relevant chemical seman- tics, whereas ChemGPT underperforms, suggesting that textual generative models lack sufficient structural precision for accurate molecular activity prediction.
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5.2.2 The Role of the Tissue Encoder
In this experiment, tested various tissue representation approaches to understand their impact model performance. The One-Hot Encoder uses simple, learnable one-hot embeddings to represent tissues, while the Text-Based Encoder employs frozen CLAMP text embeddings to capture tissue-specific textual information, but without lask-specific adaptation. The Non-Gruph Encoder utilizes precomputed pathway activity profiles from ssGSEA, removing pathway pattway interactions to capture broad functional trends. Additionally, we evaluated Mean Tissue Expression, where each tissue is represented by the average gene expression across all samples in that tissue, capturing only the tissue's general activity profile.
The intuition behind these variants was evaluate how different levels of tissue context affect performance. The results show that Expresso consistently achieves the highest performance across all tissue, while removing the tissue encoder leads to a marked decline. The One-Hot and Tezt- Base-Ba variants partially recover this loss but remain below Expresso, showing that simple or frozen representations are insufficient to capture complex biological variability. The Mean Tissue Expression baseline performs comparably to these simplified variants, indicating that averaged transcriptomic signals provide only coarse lissue-level cues.
5.2.3 The Role of Pretraining
Without pretraining, the tissue encoder lacks the opportunity to learn how to represent tissues in a gen- eralizable way. Tt is only optimized to support the downstream molecular activity prediction task, which limits its ability to model gene and pathway interactions more broadly. Pretraining provides this missing context by exposing the encoder to diverse tissues and self-supervised objectives, enabling it to build richer, biologically grounded representations independent of activity labels.
5.2.4 The Role of Auxiliary Losses
We also examine how different self-supervised objectives affect learning. Removing all auxiliary losses reduces performance across the board, confirming that the model benefits from more than just label supervision. Gene-level masking helps the model capture fine-grained expression dynamics, while sample classification reinforces broader tissuc-level structure. Pathway-based objectives are especially useful in tissues where higher-order biological processes dominate, enabling the model to reason more effectively about drug activity.
5.2.5 The Role of Graph Structure
The architecture of the tissue graph plays a central role in Expresso's success. When only sample nodes are used, the model lacks meaningful biological grounding. Removing gene nodes, by dropping all sample -ene gene and gene-pathway odges, climinates crucial expression-lvvel detail, while excluding pathway nodes strips away functional hierarchy. The full graph composed of samples, genes, and pathways enables both bottom-up signal integration and top-down semantic guidance. Each node type contributes complementary perspective, and removing any of them weakens the model's 's ability to reason about tissue-specific drug effects.
5.2.6 Synthesis of Ablation Findings
The ablation study highlights that not all components of Expresso contribute equally to performance: the tissue encoder and the biological graph structure are the most critical. Removing the tissue encoder leads to the largest and most consistent drop in ROC-AUC, demonstrating that molecular features alone are insufficient to capture tissue-specific activity. Similarly, disrupting the graph- by omitting gene or pathway modcs-substantially impairs performance, emphasizing the importance of modeling hicrarchical interactions between samples, genes, and pathways rather than relying on flat or averaged represent aepresentations.. Auxiliary self- supervised losses also improve predictions, particularly in tissues with complex pathway activity, but their effect is sccondary to the structural and contextual components. Overall, these results suggest that Expresso's predictive power arises primarily from the combination of chemically informed molecular embeddings with biologically grounded, graph-structured tissue context.
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Published in Transactions on Machine Learning Research (02/2020)
Top Rathway Catagoriss iis Across Tissues
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Figure 2: Hcatmap of normalized pathway importance across tissues. Each row corresponds to a biological pathway grouped into functional categories, and each column represents tissue. Colors indicate the relative contribution of each pathway to tissue-specific predictive performance, with darker colors representing path- ways whose removal strongly reduces model accuracy, and lighter colors indicating pathways with minimal impact. Values are normalized per tissue such that 0 corresponds to effect and corresponds to maximal impact.
6 Uncovering Tissue-Specific Insights Through Pathways and Predictions
In this section, we present deeper insights into how the model caplures Lissue-specific drug behavior. We analyze the biological pathways relies on across tissues, and evaluate its ability to prioritize therapeutically relevant compounds, offering both interpretability and predictive validation.
6.1 Pathway-Level Insights Across Tissues
To interpret Expresso's tissue-specific predictions, we conducted an interventional pathway ablation anal- ysis. Pathways were grouped into functional categories by an expert. For each tissue, we systematically removed each pathway group from the model's internal graph and measured the resulting decline in pre- dictive performance. The magnitude of this decline indicates the pathway group's 's relative contribution prediction.
Figure displays the normalized importance of pathway categories across tissues. Higher values indicate that removing pathway category substantially reduces predictive accuracy, while lower values indicate minimal contribution. This visualization allows direct assessment of the relative functional dependencies captured by the model across Lissues.
The pathway importance patterns reflect both general and tissue-preferential functional trends. Across most tissues, immune and metabolism pathways consistently contribute, highlighting their broad roles in defense and energetic homeostasis. Signaling pathways particularly important in organs like the kidney and liver, consistent with the need for receptor-mediated regulation and intercellular communication. Neuronal pathways show limited contributions in the brain but moderate contributions in ovary and prostate, suggest- ing that tissue-specific neuronal dependency is not strongly captured by this metric. Apoptosis, cell cycle, glycosylate lation, and transport. pathways show minor but consistent. influence, representing baseline cellular maintenance.
Some dissue-preferent patterns apparent but vary in magnitude: the brain emphasizes immune and maintenance- pathways, reflecting immune- neuronal interactions; the liver relies signaling and metabolism for detoxification and nutrient processing; the kidney shows strong signaling and metabolic imporlance, supporting filtration and homeostasis; and lung lissue balances metabolic and immune contri- butions, consistent with environmental exposure. Other tissues, including breast, cervix, ovary, prostate, and skin, display moderate contributions from multiple categorics, reflecting a combination of biosynthetic, signaling, and defense processes. Overall, Expresso captures coherent, biologically meaningful pathway de- pendencies that vary across tissues while maintaining general functional consistency.
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Published in Transactions on Machine Learning Research (02/2026)
One might worry that ablated graphs are out-of-distribution and could lead to unreliable importance es- timates (Hase et al.) 2021); however, shown in Appendix [ 91% of ablated graphs remain within the model's learned distribution, indicating minimal OOD impact.
Specific tissue-level observations mostly align with known biology. In the brain, immune signaling and apoptosis pathways, including FOXO-mediated transcription and complement cascades, contribute to neu- ronal maintenance and immune neuronal interactions, while neuronal pathways have low apparent impor- tance. Breast tissue emphasizes FGF and mTOR signaling, estrogen-regulated transcription, and glycosy- lation/mitoochondiial pathways, reflecting hormone-driven growth and protein modification demand. The liver shows importance for signaling pathways such as FGFR1/2 and nitric oxide, as well as xenobiotic metabolism, lipid, and bile acid pathways, consistent with detoxification and metabolic regulation. Kidney pathways include heme scavenging, ion transport, and xenobiotic metabolism, matching renal clearance and electrolyte balance. Cervical tissue highlights metabolism, immune, and glycosylation pathways, supporting epithelial diffcrentiation and local defense.
Overall, these results demonstrate that Expresso captures biologically meaningful tissue-specific functional dependencies, with some pathway contributions varying in magnitude across tissues, as reflected in the normalized heatmap.
6.2 Prioritizing Clinically Relevant Compounds
We evaluated tissue-specific prioritization by retrospectively testing FDA-approved drugs with known clinical indications. Each compound was imput to the model across all tissues, and predicted binary activity scores were used to rank compounds within each tissue. This allows assessment of whether the model naturally assigns higher activity to drugs in the tissues where they are clinically effective.
The model successfully prioritized clinically relevant compounds. For instance, it highly ranked So- rafenib (Keating 2017; Kane et al. 2006) in liver (0.7371), therapy for liver cancer, Fulvestrant (Ciruelos ct al. 2014; Nathan & Schmid] (201)) in breast (0.8126), an estrogen receptor antagonist used in breast cancer trealment; and Donepezil (Kertesz, 2004) in brain (0.9120), commonly prescribed for Alzheimer's disease. At the same time, deprioritized compounds with limited outcomes, such as Talampanel, which had minimal efficacy in neurological conditions (0.3284).
These results, together with a more in-depth analysis of predicted tissue-specific activity provided in Ap- pendix [ indicate that Expresso captures molecular signals aligned with known pharmacological effects across tissues. By integrating molecular structure with tissue-specifie biological context, the model enables more targeted prioritization of compounds, bridging the gap between computational screening and exper- imental validation. This approach supports strategic experimental planning and enhances confidence in selecting compounds with higher potential for human relevance.
7 Conclusions
In this work, we presented Expresso, a multi-modal framework that enhances molecular activity prediction by incorporating tissue-specific biological context. While most existing models treat tissues as flat input, Expresso is designed to reason over the molecular and cellular environment in which compounds act, as represented by human cell-based in vitro assays. By embedding both molccular structure and rich tissuc- level transcriptional information into unified predictive model, Expresso narrows the gap between in vitro data and tissue-level biological settings, within the limits of the available assay panel.
We conduct an empirical evaluation across ninc benchmarks involving thousands of molecules, demonstrating the value of leveraging tissue-specific biological information for activily prediction. Expresso consistently outperforms baseline architectures, with performance gains largely attributed to our novel tissue encoder. This encoder models gene-pathway interactions and captures sample-level variation within tissue types. By grounding the model in biological context, Expresso enables more accurate and generalizable predictions of compound activity across diverse human tissue contexts.
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Published in Transactions on Machine Learning Research (02/2026)
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15


Reviews:
[{"review_id": "uFlwldq79r", "reviewer": "Reviewer_1TZT", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The authors present a model, Expresso, that predicts whether a drug has activity in a certain tissue, improving upon prior work that does not explicitly model tissues. The authors highlight their framework for learning tissue embeddings using graphs representing biological structure.\n\nStrengths\n1. The model formulation and architecture is clear and well-structured.\n2. The setup of the tissue encoder using graphs built from GTEx data is fairly novel and interesting.\n3. The authors conducted a comprehensive ablation study and benchmarked against several baselines, demonstrating superior performance.\n\nWeaknesses\n1. The proposed task may not be standard and/or well-defined. Indeed, neither the biological meaning of the binary targets (“tissue-specific drug activity”) nor how they are put together is well specified in the manuscript. Additionally, the toxicogenomics datasets (OPEN TG-GATEs and DrugMatrix) used in this paper are generally used for toxicity rather than therapeutic activity. \n2. It is difficult to assess biological relevance and what the model is learning (Section 6) without clarity on the dataset, assay endpoints, thresholds, and sources of binary labels.\n3. While Expresso shows moderate AUC improvements, results in the appendix on PR-AUC and leave-one-out validation are weaker but not discussed compared to baselines.\n4. Key details on splits, hyperparameters, and uncertainty quantification are missing.", "claims_and_evidence_yn": "No", "claims_explanation": "The authors conducted clear benchmarks and ablation studies. However, more evidence or qualifications are required for these two claims:\nTransferable/generalizable: Results in Table 6 need more discussion and specifications of splits across drugs and tissues.\nDataset and model interpretability: The biological endpoint is not clearly defined, and the model structure itself is not easily interpretable.", "audience_yn": "Yes", "audience_explanation": "Although a combination of off-the-shelf GNN methods are used, the contribution is primarily in applying this to the tissue-specific activity prediction problem. This paper could be of interest to computational biologists, but the biological utility of prediction outputs is unclear.", "requested_changes": "**Critical**\n\nIntroduction: The citations Mamoshina 2016 and Ingraham 2019 should be fixed as they are not related to prior work on similar tasks using simpler tissue encodings.\n\nRelated Work: The difference between what the cited works predict and the biological meaning of the task here needs to be clearly defined.\n\nAppendix A2 (dataset description): “Activity labels…one of the most widely used and well-established benchmarks.” Please clarify or address all the points below:\n- The type of experimental measurement and assay is not mentioned and not found in the papers cited or dataset link.\n- The prior benchmarks using this dataset mentioned are not cited or compared to benchmarks in this work.\n- The number of compounds is not mentioned anywhere in the paper aside from “thousands” (Section 7). The papers cited mention 600 and 170 compounds.\n- The authors should further justify the choice of this toxicogenomics dataset for their task. Covariates such as dosage are not considered. The TG-GATEs dataset also seems to contain only human cancer cell lines, which are not necessarily aligned with GTEx data from human tissues.\n- Many models for drug response prediction predict continuous outputs such as IC50 or dose-response curves. The authors should comment on the choice of supervised learning target, and how their work relates to previous efforts.\n\nText embedding baseline: Other methods (CLAMP) define bioactivity with a clear biological endpoint such as a bioassay or drug target. Clear descriptions of the specific bioassay generating a 0/1 output are passed to the text encoder. However, the BioBERT baseline in A.3 receives a vague prompt that does not define “molecular activity.” More details on BioBERT finetuning are also needed to evaluate the strength of the highest-performing baseline.\n\nMetrics: The conclusions of the paper are too reliant on AUC (Table 1) compared to other metrics. More discussion on weaker PR-AUC and leave-one-out generalization (no longer outperforming baselines that do not use tissue embeddings) is needed. It would be good to see AUPRC for some of the baselines to determine if they are similarly affected by class imbalance.\n\nOther details: The authors should mention more details about methods used for hyperparameter selection. It is also unclear how the 95% confidence intervals for ROC-AUC in Table 1 were constructed.\n\n**Additional**\n\n- Fig. 2 and Section 6.1 are confusing. The meaning of high/low score and color scheme should be clarified to make the text and figure consistent. For example, the text states “neuronal pathways are uniquely enriched in the brain” but Fig 2 shows normalized pathway activity of zero. Citations are missing.\n- In Section 6.2, further justification is needed on how model predictions relate to specific diseases, since the GTEx dataset does not contain samples from those conditions.\n- Important details about datasets, splits, other metrics should be in the main text.\n- Sections 4-5 are thorough but can be shortened or partially moved to the appendix (e.g. repetition between 4.2.3 and 4.3.1).\n- While statistically significant, Expresso’s performance gains are moderate. To further motivate the architecture, it could be interesting to look at how informative the context-aware sample embeddings or tissue embeddings are, perhaps through dimensionality reduction.", "broader_impact_concerns": "NA, since publicly available data is used.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "urLTyOt4v1", "reviewer": "Reviewer_TbHc", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The authors introduce Expresso, an architecture that incorporates biological context for predicting molecular activity in different tissues through heterogeneous graphs and multi-task pretraining. The downstream task is a binary classification task with molecule and target tissue pair inputs to predict molecular activity in that tissue. They compare their work across multiple baseline architectures (BioBERT, CLAMP, etc) and perform ablation studies (Section 5) and a knockout analysis across different biological pathways (Section 6). The motivation is relevant and the results consistently outperform baseline models. The authors provide documented code and describe their implementation details and hardware used for experimentation in the appendix. This work could benefit from some rewriting (mostly around citations). All in all, the work is of interest to the TMLR audience.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The main text contains sufficient explanation on the biological motivation and what the design decisions were (such as the compound encoder from CLAMP and respective sublosses). The Appendix contains sufficient information on data sources and splits (e.g. creating splits on the donor-level rather than sample level), includes additional metrics outside of AUROC, including Brier scores for calibration, and experiments to evaluate robustness to noise. The main text can benefit from more details on why certain design decisions were made (such as using MSE as a subloss, for example) while the code repository will benefit from scripts to run the experiments (more below for both points).", "audience_yn": "Yes", "audience_explanation": "Expresso's architecture and pretraining will be of interest to different downstream biological tasks, as the authors demonstrate the ability of their method to work across different tissue types.", "requested_changes": "Paper comments:\n- The default $\\lambda$ hyperparameters for weighting sublosses in pretraining outlined in Section 4.4.5 seem to be 0.5, 0.5, and 0.25 for $\\lambda_{\\text{gene}}$, $\\lambda_{\\text{cls}}$, and $\\lambda_{\\text{pathway}}$, respectively. Were these the values used for experimentation? Did the authors perform any experiments to evaluate the impact of these values (outside of setting them to 0 for ablation)? In their ablation results, the $\\lambda_{\\text{activity}}+\\lambda_{\\text{gene}}$ results consistently outperforms the other ablated subloss models, and I’m curious if the weighting of the different $\\lambda$ values may be part of the reason why.\n- The ablations results demonstrate that sublosses are important for pretraining; however the decision on the specific functions used for the sublosses could use some justification. For example, the Gene Expression Reconstruction Loss in Section 4.4.1 is mean squared error over masked entries; it isn’t clear to me why this was chosen. Was it simply a simple and pragmatic choice, or is there a specific justification? A brief explanation or reference to the literature would help.\n- In Appendix A.1 Implementation details, the authors discuss using “up to 15 fine-tuning epochs” for fine tuning–did they use early stopping, and if they did, under what metric?\n- Table 4 in the Appendix shows a relatively high $p$-value for the DeLong test between the method (as well as for ovary and prostate) compared to very low $p$-values for the other organs. The authors comment on how low sample size may be the culprit: “...Expresso significantly outperforms the strongest baseline in most tissues, while a few tissues with smaller effect sizes did not reach statistical significance.” This is an interesting result and the authors should consider fleshing this out more.\n- The code repository is well-documented. However, it would be nice if the authors included the experiment files in case anyone wants to recreate their ablation studies or knockout studies from Sections 5 and 6. The authors should also include the different settings they experimented on (e.g. the Appendix mentions that the models were trained on random seeds but the repository seems to only have the default seed).\n\nWriting comments:\n- The author names should be enclosed in in-text citations. For example, “typically by encoding it as a categorical label or an unstructured text input Mamoshina et al. (2016); Ingraham et al. (2019).” should be written as “(Mamoshina et al., 2016; Ingraham et al., 2019).” This might be an issue with \\cite and \\citet rather than \\citep in LaTeX.\n- Ensure that prior work is referenced correctly–for example Mol-BERT [1] is referenced as MolBERT.\n- Some citations should be updated; for example, the Brody et al., 2021 paper “How attentive are graph attention networks?” [2] cites the arxiv preprint instead of the ICLR 2022 version.\n- The captions for the figures and the tables could be fleshed out more such that they can be interpreted by themselves without referencing the main text (especially given that the PDF seems to not be searchable). For example, in Tables 1 and 2, the metric is AUC (I’m assuming it’s  AUROC), but there’s no mention in the caption, only in the first paragraph of Section 5.1. Or in the heatmap for Figure 2, how is a reader supposed to interpret the plot?\n- Page 10, section 5.2.5 “The full graph— composed of samples, genes, and pathways—enables” has inconsistent spacing between words and em-dashes.\n\n[1] Li, Juncai, and Xiaofei Jiang. \"Mol‐BERT: An Effective Molecular Representation with BERT for Molecular Property Prediction.\" *Wireless Communications and Mobile Computing* 2021.1 (2021): 7181815.\n\n[2] Brody, Shaked, Uri Alon, and Eran Yahav. 2022. “How Attentive Are Graph Attention Networks?” International Conference on Learning Representations.", "broader_impact_concerns": "N/A", "additional_comments": "The submitted PDF files for the manuscript and appendix seem to prevent text selection. This made reviewing more difficult and time-consuming since I could not highlight the PDF for notes, search for terms in the document, and had to type out sections of text I wanted to highlight in my review. More importantly, it may impact accessibility and readability for readers (e.g. screen-reader users). Please ensure that the final submission is a text-selectable PDF.", "extra_scores": {}, "extra_text": {}}, {"review_id": "pMSvjMEfJ8", "reviewer": "Reviewer_LyGu", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper presents Expresso, a graph neural network for predicting drug activity in various tissues. Its unique value proposition is the incorporation of multimodal data within a multi-task framework to support the prediction: chemical structure of the compound/molecule, gene expression profile and biological pathways. Claims of outperformance relative to baselines are well-substantiated with detailed experiments and ablation studies. However, there is room for improvement in terms of the biological analysis is performed and reported. The manuscript will benefit from addressing the clarifying questions listed below (under ‘Requested changes’).", "claims_and_evidence_yn": "Yes", "claims_explanation": "There are 3 main claims (1) mechanistically grounded representations, (2) outperformance relative to baselines, (3) new biological insights. Majority of them are supported with empirical evidence (though Claim 1 might be tougher to substantiate and could perhaps be rephrased or supported with additional evidence). However, the biological analysis requires some additional clarifications.", "audience_yn": "Yes", "audience_explanation": "In the applied ML space, there are plenty of work on drug activity/response prediction and the use of graph neural networks for this purpose (customised for biological datasets) is an active area of research as it is well-known that baseline SOTA GNNs are insufficient.", "requested_changes": "Major\n1. Related work - this section is difficult to follow as everything is clustered into 1 paragraph, sentences jump around different topics and some papers are mentioned without linking back to how it is relevant to Expresso. A suggestion is to identify the key aspects of Expresso (e.g. multi-modal, multi-task), mention relevant papers related to each aspect (1 paragraph for each aspect) and always link back to how it matters (e.g. how is Expresso better). \n2. Report the number of trainable parameters in each baseline model and Expresso.\n3. Expresso’s sensitivity to model parameters, in particular the lambdas in the multi-task loss function, should be reported. (e.g. for each lambda, plot a chart of how model performance varies when lambda is varied from the existing optimised values)\n4. Model explainability is done via a perturbation-based approach. This is well known to be confounded by an out-of-distribution (OOD) problem [1]. It would be good to investigate whether the knockouts are indeed OOD and if they are, how they impact the explanations/heatmap produced.\n5. Section 6.2 is unclear as it does not seem to explain how exactly the testing was performed and how the ranks are computed. \n\nMinor\n- Keep the formatting consistent - subsubsections 4.2.1-4.2.4 have colons but not 4.3.2/3, 4.4.1… etc. Put it in all of them or remove from all. \n- Comment on possible reasons why KA-GNN, a recent model published this year, has such consistently poor results across all datasets despite being the latest model out of all baselines. \n- Add a subsection 5.2.6 to synthesise the findings from the ablation study. In particular, mention which aspects of the Expresso architecture were found to have the greatest impact on model performance and suggest why this might be the case. \n\n\n[1] https://dl.acm.org/doi/10.5555/3540261.3540540", "broader_impact_concerns": "Nothing of note for now.", "additional_comments": "-", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)