VCWorld: A Biological World Model for Virtual Cell Simulation
Authors: Zhijian Wei
Runze Ma
Zichen Wang
Zhongmin Li
Shuotong Song
Shuangjia Zheng
Venue: ICLR 2026 Poster (2026)
Keywords: Virtual Cell
Large Language Models
Perturb-seq
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: We present VCWorld, a cell-level white-box simulator that integrates biological knowledge with large language model reasoning to generate interpretable drug perturbation responses and mechanistic hypotheses.

Abstract: Virtual cell modeling aims to predict cellular responses to perturbations. Existing virtual cell models rely heavily on large-scale single-cell datasets, learning explicit mappings between gene expression and perturbations. Although recent models attempt to incorporate multi-source biological information, their generalization remains constrained by data quality, coverage, and batch effects. More critically, these models often function as black boxes, offering predictions without interpretability or consistency with biological principles, which undermines their credibility in scientific research. To address these challenges, we present VCWorld, a cell-level white-box simulator that integrates structured biological knowledge with the iterative reasoning capabilities of large language models to instantiate a biological world model. VCWorld operates in a data-efficient manner to reproduce perturbation-induced signaling cascades and generates interpretable, stepwise predictions alongside explicit mechanistic hypotheses.  In drug perturbation benchmarks, VCWorld achieves state-of-the-art predictive performance, and the inferred mechanistic pathways are consistent with publicly available biological evidence. Our code is publicly available at https://anonymous.4open.science/r/VCWorld-B970.

Published as a conference paper at ICLR 2026
VCWORLD: A BIOLOGICAL WORLD MODEL FOR VIR- TUAL CELL SIMULATION
Zhijian W Runze Ma Zichen Wang'
Zhongmin Li!
Shuotong Song'
Shuangjia Zhengh
Shanghai Jiao Tong University NeoLife Al Drealman_000 01, runze.ma, zichen_ shuangjia zhengggj...izgigiiaooo ( edu.
ABSTRACT
Virtual cell modeling aims to predict cellular responses to perturbations. Existing virtual cell models rely heavily on large-scale single-cell datasets, learning explicit mappings between gene expression and perturbations. Although recent models attempt to incorporate multi-source biological information, their generalization mains constrained by data quality, coverage, and batch effects. More critically, these models often function black boxes, offering predictions without interpretability or consistency with biological principles, which undermines their credibility scientific research. To address these challenges, we present VCWorld, cell-level white-box simulator that integrates structured biological knowledge with the itera- tive reasoning capabilities of large language models instantiate a biological world model. VCWorld operates in a data-efficient manner to reproduce perturbation- induced signaling cascades and generates interpretable, stepwise predictions along- side explicit mechanistic hypotheses. In drug perturbation benchmarks, VCWorld achieves mate-of-the-ar predictive performance, and the inferred mechanistic pathways are consistent with publicly available biological evidence. Our code is publicly available at https: /ttp:::///gthbb com/ GENTEL- lab/VCWorll
INTRODUCTION
Cells, the fundamental units of life, maintain organismal function and homeostasis through complex interplay biochemical processes (Alberts et al., 2002). A central challenge in modern biology and drug discovery is to understand and predict how cells respond to external perturbations, such as drug treatments or genetic edits (Liberali et al., 2015; Lotfollahi et al., 2019). The ability to forecast these cellular state changes in silico would not only illuminate the mechanisms of complex diseases but also accelerate the development of novel therapeutics by reducing the time and cost of experimental screening (Del Sol et al., 2010). The concept of the virtual cell, which leverages computational models to simulate cellular behavior, has emerged as promising paradigm to address this challenge (Bunne al., 2024).
Recent advances in deep learning, coupled with significant progress in single-cell sequencing tech- nologies, have spurred the development of various virtual cell models (Lopez et al., 2018; Lotfollahi et al., 2023; Adduri et al., 2025; Tang et al., 2025; Klein et al., 2025). These models typically learn an end-to-end mapping from given perturbation to a corresponding gene expression profile, trained on large-scale perturbation-responee datasets. However, prevailing approaches suffer from two critical limitations. First, they are heavily reliant on the scale, quality, and coverage of the training data. The data-hungry nature makes these models expensive to train. More importantly, it also limits their ability to generalize to novel perturbations that are not present in the training data (Li et al., 2024; Ahlmann-Eltze et al., 2024). Second, these models operate as black boxes. While they may yield predictive outputs, they fail to provide clear, verifiable mechanistic explanations for predic- tions (Hassija al., 2024; Noutahi et al., 2025). This lack of interpretability severely undermines their trustworthiness and utility in scientific discovery, making them difficult for biologists to rely upon for designing downstream experiments.
"These authors contributed equally this work. Corresponding author.

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Published as a conference paper at ICLR 2026
We argue that an ideal virtual cell model should not only provide accurate predictions but also be data-efficient, interpretable, and aligned with established biological principles. Rather than relying solely on statistical correlations, it ought to integrate fundamental biological knowledge to capture and anticipate cellular responses. Moreover, its reasoning process should be transparent, explicitly revealing the causal mechanisms that underlie predictions and grounding them in the well-established frameworks of cell signaling and gene regulation.
To overcome the above challenges, we introduce VCWorld, cell-level white-box simulator(Figure 1). The core of VCWorld is a biological world model that simulates the dynamic response of a cell to drug perturbations. Instead of relying solely on statistical patterns, VCWorld integrates structured biological knowledge, such as signaling pathways, protein-protece interactions, and gene regulatory networks, with the iterative reasoning capabilities of Large Language Models (LLMMs) (Dubey et al., 2024; Guo et al., 2025). This design allows the model to generalize from limited training data by leveraging a vast repository of open-world biological knowledge. Crucially, VCWorld generates transparent, traceable reasoning path for each prediction, offering step-by-step mechanistic explanation that culminates in verifiable hypotheses. Furthermore, to facilitate finer-grained modeling, introduce Gene'TAK new benchmark derived from the large-scal Tahoe- 100M dataset (Zhang et al., 2025). Gene'TAK reframes cell-drug observations into gene-centric perturbation responses, mitigating data sparsity and enabling models to focus directly on the nuanced impact of drug on individual genes. We highlight three main contributions of our study:
We propose VCWorld, a novel cell-level white-box simulator architected as a biological world model. It combines structured biological knowledge with LLM-baset reasoning, demonstrating a superior balance of data efficiency, interpretability, and predictive accuracy that overcomes the limitations of existing black-box models.
We construct and introduce GeneTTAK, a new benchmark that transforms cell-drug pertur- bation data into single-gene response profiles. This allows for more granular and robust modeling of drug effects.
We demonstrate that VCWorld achieves ofite-of-the-are performance on both the differen- tial expression (DE) and directional (DIR) prediction tasks on the GeneTAK benchmark, validating the effectiveness of our approach.
RELATED WORK
2.1 VIRTUAL CELL
Virtual cell originally a computational tool for simulating intracellular biochemical reactions, diffusion, membrane transport, and electro physiological processes (Loew & Schaff, 2001). With the development of AI, the concept has evolved into predictive cell models that integrate large-scale multi- modal biological data to forecast, explain, and guide experimental hypotheses (Bunne et al., 2024; Noutahi al., 2025). Existing research can be broadly categorized into three approaches: data-driven methods combined with prior knowledge, such as GenePT (Chen & Zou, 2024) and GEARS (Roohani al., 2024); large-scale pretrained foundational models, like scFoundation (Hao al., 2024) and scGPT (Cui et al., 2024), trained on tens of millions of cells to learn general representations and excel perturbation prediction; and generative modeling approaches, including CPA (Lotfollahi et al., 2023), STATE (Adduri et al., 2025), and CellFlow (Klein et al., 2025), which capture complex perturbation effects through decoupled latent spaces, state transitions, or flow-based generation. Further efforts, such as CellForge (Tang et al., 2025), aim to automate data analysis, literature review, and model design, pushing virtual cell toward self-directed evolution. Unlike previous approaches that mainly focus on end-to-end black-box models for cell perturbation prediction, our approach leverages a language model with reasoning and retrieval capabilities to construct a framework that emphasizes interpretability while also achieving superior performance.
2.2 LLMS FOR BIOLOGICAL REASONING AND PREDICTION
Recent research focus leveraging Large Language Models (LLMs) for interpretable biologi- cal reasoning, aiming to move beyond traditional black-box predictions. core approach in this area is the application of Shain-of--Though (CoT) prompting (Wei et al., 2022) integrate multi-
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Sorted Retrieval
WTT Augmentation
Rule-based Generation
Ferturbation Pathway
Descriptions
Rolecukk biologist
Celluior Precesi
Context
AndlyzeHow
samples
Examples
Core tusk-Peddiciinn
NON 660
Extract
Exarring brug Effeche
Deduce Perturtaah cations
RETRIEVAL
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Prppinting Similor-ios
Pathway Mechariove
Perturtation Ouncenes
SLC0133 endiia act
Drig
(GENEls differentid Expression
tinder whel Clered
Figure The pipeline of VCWorld. For a given query, VCWorld first retrieves multi-modal biological context from its integrated knowledge base, including pathway information, drug properties, and similar experimental samples. This context is then used to prompt a LLM, which used analyze mechanisms and infer how specific gene will respond. The final output is prediction for tasks such as differential expression and directional change.
source heterogeneous data. For instance, frameworks like CoToox (Park et al., 2025) and DrugRea- soner (Ghaffarzaeeh-Ssfahhee
al., 2025) combine chemical structures, biological pathways, and Gene Ontology (GO) terms to generate interpretable predictions for drug toxicity and approval processes. This paradigm has also been extended to fundamental biological contexts, such as the SUMMER framework (Wu al., 2025), which utilizes retrieval-augmentee generation (RAG) to predict perturbation experiments under gene edits. To further enhance the reasoning capabilities of these models, researchers actively exploring more advanced training methodologies. These studies (Istrate et al., 2025; Hasanaj et al., 2025) collectively demonstrate the strong potential of CoT to effectively integrate multimodal biological data within the virtual cell domain, enabling more nuanced and interpretable predictions compared to foundational models.
2.3 PERT-SEQ DATA REPRESENTATION
Large-scale perturbation datasets (Wu et al., 2024) are foundational for building predictive models of cellular behavior. Recently, scientists have gradually expanded the scope of perturbations. Replogle et al. (2022) enabled predictions of genetic perturbation effects core cellular functions. Nadig et al. (2024) first introduced the dose effect in the perturbation. Jiang et al. (2025) aided inference changes in disease scenarios. More recently, Wu et al. (2025) released PerturBase, a dedicated database for single-cell perturbation sequencing data, integrating 122 datasets from 46 studies across genetic and chemical perturbations. A landmark in chemical perturbations is Tahoe- 100M (Zhang et al., 2025), empowering AI models to predict context-dependent responses male--o drugs across vast cellular diversity. Given the large size and coverage of the Tahoe- cicee000M 100M dataset, we use it as the primary dataset.
of pathway signatures for perturbation-diie
METHOD
3.1 TASK FORMULATION
The task of predicting single-cell gene expression responses to perturbations is conventionally defined as a regression problem. Given dataset D = {(x,, Pt, 9;)) 1 where I; yi E Rd are the pre- and post-perturbation expression profiles and p; is the perturbation, the goal is to learn mapping function
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Differential Expression
Direction Of Change
PANG-1
279,316 PANG-1
Hs 766T
788T
79,588
Drug Porturatioon
HGPR2
243,746
HDPP2
HxpG/CSA
174,422
HApCCCSA
80, 422
IABEL
632
218.544
C32
124.254
Up
Upregulated Giene
2006
50k
100k
190s
2004
Downeng
Figure 2: (a) Number single-cell profiles per cell line. (b) Label distributions for the Differential Expression (DE) and Directional Change (DIR) tasks. (c) Schematic for identifying highly variable genes by comparing perturbed cells against a control group.
Rd. However, the high dimensionality and sparsity of the data pose significant challenges to achieving accurate predictions at the individual gene level. To this end, we reformulate the problem gene-centric classification task. The fundamental predictive unit triplet (c.P: g), which queries the response of a specific gene to a perturbation p in given cell lineage c. For each such triplet, we derive a corresponding binary label, let's denote the outcome for our two tine-graine classification tasks: (i) Differential Expression (DE): gene g is differentially expressed, and otherwise. (ii) Directional Change (DIR): 1 for upregulation and = 0 for downregulation. This transformation converts the original dataset D into a new, larger corpus labeled triplets suitable for training a classifier.
f Rd x X
{0, 1}. which represents
if
These tasks can be addressed by new paradigm that shifts from numerical regression to knowledge- based inference. The approach utilizes reasoning engine, such as Large Language Model (LLM) L, process not only the query but also rich set of biological context (BioContext). This context dynamically retrieved from external knowledge bases (e.g., signaling pathways, protein-protein interactions). The problem is then transformed into text-based inference function follows: f: L(c, p. g, BioContext) > Prediction.
3.2 GENETAK
To facilitate the comprehensive and fair assessment of large language models for perturbation prediction, we present Gene novel benchmark dataset. GeneTAK is curated from the Tahoe- 100M raw expression matrix and comprises distinct cell lines across 348 drug perturbations, designed explicitly to evaluate the generalization capabilities of models in this domain.
Dataset Curation. The GeneTAK expression matrix was first restricted to the 2,000 most highly variable genes, aligning it with the common input dimensions of generative perturbation prediction models. Subsequently, to ensure diverse and representative selection of cell lines, we performed Principal Component Analysis (PCA) visualization (Greenacre et al., 2022) (see Appendix A). Guided by this analysis, we selected final set of five cell lines that includes both a main cluster and several distinct outliers: C32, HOP62, HepG2C3A, A, Hs 766T, and PANC-1, Notably, this selection is consistent with the cell lines used in the work of STATE (Adduri et al., 2025), ensuring a degree of comparability and faimess with previous benchmarks.
Then we generated labels for perturbation-gent pairs, denoted as (p.g). The labels were determined by identifying differentially expressed genes (DEGs) for each perturbation using the Wilcoxon signed- rank test (Woolson, 2007). To ensure label quality, we applied strict criteria based on a significant adjusted p-vuuue and consistent expression changes across biological replicates. The resulting dataset of labeled pairs was then split by perturbation into training and test sets at a 3:7 ratio. This specific ratio intentionally chosen simulate challenging few-shot learning scenario, consistent with
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the evaluation framework used by Adduri al. (2025) and allowing for direct comparison of model performance under low-data conditions. The split maintains a similar distribution of DEGs per perturbation in both sets. The final label distribution is shown in Figure 2, with complete description of all data processing methods provided in Appendix A.
3.3 VCWORLD
Our proposed framework, VCWorld (Figure 1), transforms biological prediction into a multi-stage reasoning process powered by Large Language Model (LLM).. It consists of three key stages: (1) generating rich, symbolic representations for all biological entities; (2) retrieving a support set of causal evidence guided by open-world biological knowledge; and (3) synthesizing all information through a Chain-of- Thaouh process to produce an interpretable prediction.
3.4 CONSTRUCTION OF THE OPEN- WORLD BIOLOGICAL KNOWLEDGE GRAPH
To construct the open-world biological knowledge graph, we integrated several authoritative databases: PubChem, which provides large-scale compound structures and bioactivity data (Kim et al., 2023); DrugBank, which includes chemical, pharmacological, and clinical annotations of drugs and their target associations (Knox et al., 2024); UniProt, which offers comprehensive protein sequences and functional annotations (Consortium, 2019); Gene Ontology (GO), which defines a unified ontology across molecular function, biological process, and cellular component (Ashburner et al., 2000); Reactome, which curates systematically organized molecular reactions and pathways (Fabregat et al., 2018); STRING, which compiles protein-prott interactions from experiments and predic- tions (Szklarczyk et al., 2021); and CORUM, which catalogs experimentally validated mammalian protein complexes (Ruepp et al., 2007). Together, these databases cover multi-scale biological knowledge from compounds and drugs to genes, proteins, pathways, and complexes, ensuring the comprehensiveness of the graph.
In the knowledge graph construction process, compounds, drugs, genes, proteins, pathways. and complexes represented entity nodes, while interactions, annotations, and hierarchical rela- tionships were encoded as heterogeneous edges. Cross-database integration was achieved through standardized identifiers (e.g.. InChIKey, UniProt ID, GO terms), followed by redundancy removal and conflict resolution. The resulting graph systematically represents biomolecules and their interactions, providing foundation for cellular perturbation prediction.
3.4.1
GENERATIVE NODE FEATURE REPRESENTATION VIA LLMS
Let the open-world biological knowledge be denoted G EV,E, R)), where V is the set of biological entities (nodes), R is the set of relation types, and & C X x V is the set of triples or edges. The first stage of our framework focuses on defining each biological entity E To move beyond simple numerical vectors, we generate rich symbolic representation for each node. For each node v, we first extract local neighborhood subgraph, (v),,, defined as the set of all triples within a k--hpp distance from We then construct a structured prompt P., using template function Spompi:
Pe = fprompe (v. (t)).
This function scrializes the node's 's core attributes and its neighborhood triples into a natural language query. Subsequently, framework employs LLM to function feature generator. The LLM then processes this prompt to generate comprehensive textual description(see Appendix D), d,, serving as the node's initial feature representation:
d, d I(P.)
This process yields a context-aware representation d, E T (where T is the space of all possible texts) that preserves biological semantics, offering a more expressive alternative static embeddings.
3.4.2 GRAPH-GUIDED CA EVIDENCE FRAMEWORK
With rich node representations established, the second stage retrieves relevant experimental cases to form a basis for reasoning. Our training corpus D, consists of M labeled instances derived from the original data, can be formulated DD {(g,, 4)) Here, each query 4 is triplet
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(ct, (ct, Pa. 96), and 1 is its associated ground-truth binary label (4 E {0, 1}). Given new query Ionput (Cipput: Pinput: Itmput). our goal is to construct an evidence support set ((uaput) C D.
To ground the LLM's prediction in empirical data, introduce structured retrieval mechanism that goes beyond standard
Aetrivval-Augmntee Generation (RAG) (Lewis et al., 2020). The core of this mechanism is to identify and retrieve the most relevant historical cases from our training corpus D for given imput query Лipout- To quantify this relevance, we score the similarity between the input query Ginpar and each query di E D. Instead of relying solely on semantic similarity, our method computes hybrid similarity score, Sim (Fipptt (t), that also leverages knowledge-grape topology information:
Sim(qingat: 9:) Simgem (quua dg:) (1 a)) Sim Cactruueeguuuuuae (anput: 9:)
(3)
where Sim is the semantic similarity, calculated as the cosine similarity between the L.LM- generated feature descriptions (from Eq. 2), and (im)) is graph-based structural similarity metric (i.e., path-based similarity). The hyperparameter a E [0, 1] balances the contribution of the semantic and structural components.
This score allows us to assemble a multifaceted evidence set. Instead of retrieving single list of similar items, we retrieve two disjoint subsets by searching within predefined outcome groups as follows:
Analogue Cases (Sanalog): The top-ka instances from the subset of D with positive outcome (i.e., label I = 1), ranked by their similarity to the input query.
Sanalog (uppu))
arg top-ka Sim(quput 9:) 9. (vED [(=1]
(4)
[==]]
Contrast Cases (Scontrast): The top-ke instances from the subset of D with negative outcome (i.e., label I = 0), similarly ranked by similarity.
Suontaaa (Timpat) = arg top-hic Sim (umputs 4s)
The final evidence support set the union: (((npu))) Sanaog Scontrast This curated set provides balanced, contextual foundation upon which the final reasoning process is built.
3.4.3 EVIDENCE SYNTHESIS CHAIN-OF-THOUGGI REASONING
In the final stage, the LLM acts as a computational biologist, which we formulate as Chain-of- Thought (CoT) reasoning task. A final prompt, PCCO. is synthesized from the symbolic representation of the query, d and the retrieved evidence set, S(Imput:
POO fCoT_ppompp (dlaepu Smalog. Sconrast))
(6)
The function fCOT ScoT_prompt formats these inputs using a structured template that first presents the biological query, followed by the lists of analogue and comparison cases evidence. Finally, it instructs the LLM to provide step-by-step reasoning. (A detailed example is in Appendix D).
The LLM then processes this prompt to generate text string, Orinal, we then use parsing function, fparse. to extract the structured prediction i and the textual explanation E from the output:
O noal L(PcoT)
(i, E) fpaee (Onna)
This process compels the LLM to explicitly integrate qualitative knowledge (daagw) with empirical evidence (S( (S( (gimpa)), producing a self-validating and fully interpretable output. In this work, we use Gemeni2.5-Flaad (Comanici et al., 2025) reasoning model.
EXPERIMENT
4.1 BASELINE
We benchmarked performance against several baselines using GeneTAK, including RANDOM, which is naive baseline that is assumed to have statistically accurate prediction of 50% for any

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Table Overall accuracy on DE and DIR tasks. The best results are shown in bold, and the second- best results are shown with underlines.
Task
Model
C32
HepG2C3A
HOP62
Hs 766T
PANC-1
RANDOM
0.50 + .00
0.30 + .00
0.50 + .00
0.30 + .00
0.50 .00
GAT
0.62 + .03
0.54 + .02
0.64 + .04
0.57 + .01
0.58
.01
CPA
0.17 + .04
0.18 + .02
0.21 + .03
0.30 + .04
0.17
.02
scVI
0.66 + .02
0.48 è .01
0.64 = .02
0.61 + .01
0.68
.02
STATE
0.17 + .02
0.38 1 .01
0.41 = .01
0.09 + .02
0.47
.01
DE
VCWorld (Retrieval-only)
0.57 + .01
0.53 +
0.58 .01
0.57 + .02
0.58 .01
VCWorld (w/o Biocontext)
0.52 .03
0.54 + .03
0.51 + .02
0.32 + .01
0.50 .03
VCWorld (w/o CoT)
0.61 + .03
0.57 + .00
0.59 - .01
0.60 + .02
0.56 + .03
VCWorld (Llama3-8) 8B)
0.35 +: .01
0.36 + .00
0,41 + .01
0.36 + .00
0.39 = .02
VCWorld (Qwen3-4pp
0.41 + .00
0.47 + .01
0.46 - .00
0.49 + 02
0.46 - .01
VCWorld (Qwen2.5-7B)
0.54 .01
0.54 .00
0.55 - .00
0.58 - .01
0.64 .02
VCWorld (Qwen2.5-14B)
0.65 + .02
0.66 + .01
0.72 = .01
0.67 - .0
0.76 .01
VCWorld (Gemini-2.5-Flash)
0.70 + .00
0.68 + .00
0.71 = .22
0.68 + .00
0.61 .02
RANDOM
0.50 +:
0.50 +:
0.50 .00
0.50 +
.00
GAT
0.58 + .03
0.61 + .01
0.54 = .22
0.35 + .01
0.52 = .01
CPA
0.22 + .01
0.19 # .03
0.17 = .01
0.21 # .00
0.15 = .01
scVI
0.47 + .01
0.46 + .01
0.41 = .03
0.40 + .01
0.38 = .02
STATE
0.49 + .01
0.51 + .03
0.50 + .01
0.35 + .02
0.51 + .01
DIR
VCWorld (Retrieval-only)
0.47 .01
0.47 + .02
0.47 + .01
0.50 + .00
0.51 .01
VCWorld (w/o Biocontext)
0.37 .00
0.36 + .01
0.36 + .01
0.17 .03
0.34 .02
VCWorld (w/o CoT)
0.42 .01
0.45
0.51 = .01
0.47 +
0.45 .00
VCWorld (1lama3-88)
0.37 + .01
0.34 + .02
0.38 + .04
0.40 + .01
0.35 .01
VCWorld (Qwen3-48)
0.54 + .01
0.55 + .00
0.52 + .00
0.48 + .02
0.50 .01
VCWorld (Qwen2.5
0.56 + .02
0.56 + .01
0.55 - .00
0.51 + .00
0.53 01
VCWorld (Qwen2.5-14BB 14B)
0.67 +: .01
0.59 1 .00
0.65 - .01
0.71 + .00
0.66 1 01
VCWorld (Gemini-22) 2.5--2225
0.72 0.01
0.68 4 00
0.67 .00
0.66 + .00
0.69 1 .00
binary classification task without any training or prior knowledge; three published deep learning models: Vc (Lopez et al., 2018) is conditional variational self-encoder that conditionally models perturbation effects in potential space, CPA (Lotfollahi al., 2023) recognizes novel perturbations by learning disentangled and linearly combined latent embeddings. STATE (Adduri et al., 2025), the current SOTA model that demonstrates the best overall performance in the drug perturbation prediction task.
4.2 METRICS
We evaluate model performance on two primary aspects: differentially expressed gene (DEG) prediction performance and LLM reasoning robustness. For fair comparison, predictions from baseline models that output continuous expression values (e.g., STATE, scVI) are first converted into binary value. This achieved by applying the Wilcoxon Signed-Rank Test (Woolson, 2007) to the predicted profiles, mirroring the ground-truth label generation process. All metrics below are calculated on these standardized outputs.
Metric for DEG Prediction Performance. We evaluate DEG prediction performance using four standard classification metrics: accuracy, precision, recall, and Fl score. Accuracy provides an overall measure of the proportion of correctly classified genes, but because most genes are not differentially expressed (resulting in substantial class imbalance), Accuracy alone can be misleading. To obtain more informative assessment of performance on the positive class (DEGs), we therefore also report Precision, which reflects the ability to avoid false positives, and Recall, which reflects the ability to avoid false negatives. The Fl score, defined the harmonic mean of Precision and Recall, provides a single, balanced summary of these two aspects. In addition, to avoid bias toward models trained with regression-style objectives, also compute the AUROC and AUPRC.
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Table Performance comparison of different models on DE and DIR tasks.
Task
Model
Metric
C32
HepG2C3A
HOP62
Hs 766T
PANC-1
Average
Precision
0.63
0,56
0.58
0.50
0.63
0.58
GAT
Recall
0).42
0.43
0,46
0.39
0.47
0.43
FI-Score
0.50
0.49
0.51
0.44
0.54
0.49
Precision
0.12
0.10
0.18
0.10
0.19
0.13
STATE
Recall
0.15
0.32
0.36
0.09
0.37
0.26
DE
FI-Score
0.14
0.14
0.24
0.10
0.25
0.17
Precision
0.11
0.10
0.14
0.07
0.17
0.12
CPA
Recall
0.02
0.02
0.02
0.01
0.02
0.02
FI-Score
0.03
0.03
0.04
0.02
0.04
0.03
Precision
0.13
0.09
0.18
0.08
0.19
0.13
ScVI
Recall
0.48
0.39
0.52
0.38
0.51
0.46
F1-Score
0.21
0.14
0.27
0.14
0.28
0.21
Precision
0.60
0.64
0.61
0.57
0.52
0.59
VCWorld
Recall
0.70
0.68
0.71
0.68
0.61
0.68
F1-Score
0.65
0.66
0.66
0.62
0.56
0.63
Precision
0.62
0.54
0.64
0.57
0.58
0.59
GAT
Recall
0.37
0.39
0.49
0.44
0.52
0.44
F1-Score
0.46
0.45
0.56
0.50
0.55
0.50
Precision
0.14
0.18
0.17
0.09
0.10
0.14
STATE
Recall
0.18
0.35
0.26
0.16
0.25
0.24
F1-Score
0.16
0.24
0.21
0.12
0.15
0.18
DIR
Precision
0.10
0.10
0.15
0.10
0.03
0.10
CPA
Recall
0.02
0.02
0.02
0.03
0.19
0.06
F1-Score
0.03
0.03
0.04
0.05
0.05
0.04
Precision
0.08
0.08
0.10
0.11
0.09
0.09
SC
Recall
0.49
0.46
0.46
0.51
0.46
0.48
F1-Score
0.14
0.14
0.17
0.18
0.15
0.16
Precision
0.61
0.54
0.58
0.58
0.60
0.58
VCWorld
Recall
0.72
0.68
0.67
0.66
0.69
0.68
F1-Score
0.66
0.60
0.62
0.62
0.64
0.63
Metric for LLM Reasoning Robustness. We define Q-score to measure the robustness of the LLM's reasoning process. It is the frequency with which the model abstains from making prediction for a given query (e.g., by stating has insufficient information). A lower rate signifies higher robustness. The detailed formulas for all metrics are provided in Appendix B.I.
4.3 GENE-LEVEL PERFORMANCE
We consider the ability to capture gene-level expression changes the most fundamental evaluation criterion. To systematically assess this, the task stratified into two components: Differential Expression (DE) prediction. which identifies whether gene's expression changes significantly; and Directional Change (DIR) prediction, which determines whether the expression up-regulated or down-regulated. We use Accuracy evaluate the models' performance in correctly predicting the ground-truth labels. The results are detailed in Table 1.
Differential Expression Task Performance. As shown in Table 1, our model, VCWorld, achieves the highest overall accuracy, frequently exceeding 0.70 across multiple cell lines. Among the baseline models, performance is highly polarized. scVI and GAT demonstrate strong capabilities in detecting gene changes, showing competitive performance that even rivals some LLM variants. This indicates that these traditional models are effective recognizing that perturbation has occurred. Conversely,
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Table Comparison of AUROC and AUPRC on Differential Expression (DE) prediction.
Task
Metric
Model
C32
HepG2C3A
HOP62
Hs766T
PANC-1
RANDOM
0,50
0,50
0,50
0.50
0,50
GAT
0.65
0.59
0.69
0.62
0.68
CPA
0.20
0.17
0.15
0.18
0.14
ScVI
0.61
0.47
0.68
0.60
0.54
STATE
0.62
0.49
0.63
0.57
0.58
AUROC
VCWorld (Llama3-8B)
0.32
0.34
0.28
0.40
0.33
VCWorld (Qwen2.5-7B)
0.45
0,51
0.56
0.51
0,53
VCWorld (Qwen3-4B)
0.35
0.42
0.35
0.34
0,36
VCWorld (Qwen2.5-144)) -4BB)
0.42
0.40
0.38
0.56
0,48
VCWorld (Gemini-2.5- 1ammini-2.5-Flstte
0.51
0.42
0.47
0.46
0.52
DE
RANDOM
0.61
0.58
0.68
0.73
0.78
GAT
0.43
0.38
0.47
0.42
0.46
0.21
0.32
0.24
0.22
0.19
ScVI
0.42
0.48
0.52
0.56
0.65
STATE
0.37
0.42
0.44
0.36
0.44
AUPRC
VCWorld (Llama3-8B)
0.62
0,58
0.49
0.57
0.52
VCWorld (Qwen2.5-7B)
0.80
0.60
0.83
0.82
0.88
VCWorld (Qwen3-4B)
0.76
0.54
0.76
0.72
0.76
VCWorld (Qwen2.5-14p)
0.85
0.71
0.82
0.87
VCWorld (Gemini-22) 2.5-Flash)
0.83
0.72
0.84
0.81
0.84
CPA and STATE perform poorly on this task, with accuracies falling far below the random baseline of 0.50, suggesting that these models fail capture the basic perturbation patterns this specific experimental setup, likely producing highly skewed or incorrect predictions.
Directional Change Task Performance. The DIR task is inherently more challenging as it requires inferring the direction of expression changes. Most traditional baselines suffer significant accuracy drop from the DE task, exposing their inability to determine regulatory directions. The only exception GAT (0.52-0.61), indicating that modeling gene relationships via graphs provides some directional signals. Conversely, VCWorld achieves stable and superior accuracies (0.65-0.72), By leveraging the biological knowledge and reasoning of LLMs, VCWorld overcomes traditional limitations, accurately inferring not just that gene is perturbed, but exactly how it is regulated.
4.4 CELL-LEVEL AND POPULATION-LEVE. ANALYSIS
We believe that examining the collective response of gene populations offers a more accurate representation of cellular changes. To evaluate this, quantified the number of DEGs predicted across the GeneTAr dataset (see Appendix B).
The results show clear patterns. VCVI predicts 3-5 times more DEGs than the ground truth. STATE less stable, but on average predicts about twice as many DEGs. These results indicate that both models amplify perturbation effects and classify genes as DEGs too aggressively. This explains their high Accuracy but low Precision, Recall, and F1 Score, which limits their reliability. In contrast, CPA consistently underestimates DEG counts, reflecting a limited ability capture perturbation effects.
To obtain a more balanced view, we further computed Precision, Recall, Fl score (Table 2), and the threshold-free metrics AUROC, AUPRC (Table 3). The results confirm the above observations: (i) VCWorld achieves the best overall performance, with high and balanced precision (0.59) and recall (0.68), resulting in the highest Fl score (0.63); (ii) SC attains relatively high recall (0.46) but low precision (0.13), yet still stands out as the strongest baseline; (iii) STATE shows mediocre performance across all metrics, with a low overall Fl score of only 0.17; and (iv) both STATE and scVI amplify perturbation effects, with Sc predicting several times more DEGs than the ground truth and STATE predicting about twice as many. The threshold ffree evaluation further sharpens these conclusions: although baselines such as GAT and VI reach moderate AUROC values (approximately (60-0.65), AUROC can be overly optimistic in highly imbalanced settings like gene perturbation, where non-DEGs vastly outnumber DEGs. In contrast, AUPRC, which focuses

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Published as a conference paper at ICLR 2026
0.69
0.7
0.3
0.25
0.23
0.2
0.77
0.13
0114
0.0
HepS220AA
HOPSI
PANC
VoWids 8amz8
VoWord (wlo BloContzut
VCWord (wo OoT)
VONord Owe 2.144
G Rash
Figure 3: The ablation experiments of VCWorld.
the positive class (perturbed genes), provides more decisive assessment; here, VCWorld clearly dominates, achieving an average AUPRC above 0.80 (e.g., g., 0.85 on C32), while the baselines typically remain in the 0.40-0.50 range.
These findings suggest that deep learning models often lack explicit constraints to regulate DEG counts. They fail to capture the true intensity and scope of perturbation effects. In contrast, the design of VCWorld directly addresses these limitations, which explains its superior performance.
4.5 ANALYSIS AND ABLATION STUDY
To evaluate the model's tendency avoid answering questions, we introduce Q-score as dedicated metric for assessing LLM performance. Q-score is formally defined as a measure of the proportion of questions that are not answered normally, reflecting the model's avoidance behavior when faced with complex or sensitive queries. Further details on the computation and usage of Q-score are provided in Appendix 1.1.4. Figure reports the Q-scores after ablating different components of the model. The ablation results reveal three key mechanisms driving VCWorld's 's success:
LLM Reasoning Capability is Critical: Performance scales with the intelligence of the backbone model. We observe a clear trajectory from Llama3-8B (0.37) - Qwen2.5- 14B (0.65) + Gemini-2.5-Fllas (0.70) on the C32 cell line. This 84% performance leap (Llama3- 8B vs. Gemini 2.5-Flash) confirms that the task requires advanced reasoning, not just pattern recognition.
BioContext as a Cornerstone: Removing biological context (VCWorld w/o BioContext) causes performance to plummet to near-random levels (0.51 average). This validates that the model relies on retrieved biological priors rather than hallucinating guessing.
Thoughof-Thought (CoT) Standardization: Enabling CoT improves the mean score by approximately 15% (0.59 to 0.68). CoT acts as a regularizer, ensuring the model follows logical inference path consistent with biological principles.
Thought
5 CONCLUSION
We demonstrate the efficacy of VCWorld in the task of predicting changes in gene expression following chemical perturbations. Our experiments show that VCWorld far outperforms current state-of-the-art black-box models. Its main contribution lies in aspects that crucial for scientific utility. Through extensive ablation studies and case studies, we demonstrate that VCWorld is highly interpretable, with human readablee, verifiable chain of reasoning for each prediction.
VCWorld still has room for improvement, with future directions including integrating a multi-agent framework enhance autonomous reasoning and sample retrieval, extending generalization across diverse perturbation types such as gene, pathway, and combinatorial perturbations, and establishing more systematic benchmarks to comprehensively evaluate and compare specialized prediction models.
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ACKNOWLEDGMENTS
This study has been supported by the National Natural Science Foundation of China 62041209, Natural Science Foundation of Shanghai 1440604 the Science and Technology Commission of Shanghai Municipality 24510714300, the Lingang Lab Fund LGL-8888.
REFERENCES
Abhinav K Adduri, Dhruv Gautam, Beatrice Bevilacqua, Alishba Imran, Rohan Shah, Mohsen Naghipourfar, Noam Teyssier, Rajesh Ilango, Sanjay Nagaraj, Mingze Dong, et al. Predicting cellular responses to perturbation across diverse contexts with state. bioRxiv, pp. 2025-06, 2025.
Constantin Ahlmann-Elte Wolfgang Huber, and Simon Anders. Deep learning bease predictions of gene perturbation effects do not yet outperform simple linear baselines. BioRxiv, pp. 2024-09, 2024.
Bruce Alberts, Alexander Johnson. Julian Lewis, Martin Raff. Keith Roberts, and Peter Walter. Cell movements and the shaping of the vertebrate body. In Molecular Biology of the Cell. 4th edition. Garland Science, 2002.
Michael Ashburner, Catherine A Ball, Judith A Blake, David Botstein, Heather Butler, J Michael Cherry, Allan Davis, Kara Dolinski, Selina Dwight, Janan Eppig, al. Gene ontology: tool for the unification of biology. Nature genetics, 25(1):25 29, 2000.
Charlotte Bunne, Yusuf Roohani, Yanay Rosen, Ankit Gupta, Xikun Zhang, Marcel Roed, Theo Alexandrov, Mohammed AlQuraishi, Patricia Brennan, Daniel B Burkhardt, et al. How to build the virtual cell with artificial intelligence: Priorities and opportunities. Cell, 787(25):7045-7063 2024.
Yiqun Chen and James Zou. Genept: a simple but effective foundation model for genes and cells built from chatgpt. bioRxiv, pp. 2023-10, 2024.
Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, al. Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities. arXiv preprint arXih:2507.0626. 2025.
UniProt Consortium. Uniprot: a worldwide hub protein knowledge. Nucleic acids research, 47 (D1):D506-DDD 2,166-551,,20 2019.
Haotian Cui, Chloe Wang, Hassaan Maan, Kuan Pang, Fengning Luo, Nan Duan, and Bo Wang. segpt: toward building foundation model for single-cell multi-omics using generative ai. Nature methods, 21(8):1470 1ee01147888888 14800-1-10,0000 2024.
Antonio Del Sol, Rudi Balling, Lee Hood, and David Galas. Diseases network perturbations. Current opinion in biotechmology. 21(4):566-57 5716 2010.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang. Angela Fan, et al. The llama herd of models. arXiv e-prints, pp. arXiv-2407, 2024.
Antonio Fabregat, Steven Jupe, Lisa Matthews, Konstantinos Sidiropoulos, Mare Gillespie, Phani Garapati, Robin Haw, Bijay Jassal, Florian Korninger, Bruce May, et al. The reactome pathway knowledgebase. Nucleic acids research, 46(D1):D649-D655 2018.
Mohammadreza Beitarzzadeh-Effahann Ali Motahharynia, Nahid Yousefian, Navid Mazrouei. Jafar Ghaisari, and Yousof Gheisari. Drugreasoner: Interpretable drug approval prediction with a reasoning-augmentte language model. arXiv preprint arXiv:2508. /8579, 2025.
Michael Greenacre, Patrick JF Groenen, Trevor Hastie, Alfonso lodice d'Enza, Angelos Markos, and Elena Tuzhilina. Principal component analysis. Nature Reviews Methods Primers, 2(1):100, 2022.
11

---

Published as a conference paper at ICLR 2026
Daya Guo, Dejian Yang. Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, Runxin Xu, Ruoyu Zhang, Shirong Ma, Xiao Bi, et al. Deepseck-rl incentivizes reasoning in llms through reinforce- ment learning. Nature, (45(8081):6333663) 638. 2025.
Minsheng Hao, Jing Gong, Xin Zeng, Chiming Liu, Yucheng Guo, Xingyi Cheng, Taifeng Wang, Jianzhu Ma, Xuegong Zhang, and Le Song. Large-scale foundation model on single-cell transcrip- tomics. Nature methods, 21(8): (18):1481-149) 2024.
Euxhen Hasanaj, Elijah Cole, Shahin Mohammadi, Sohan Addagudi, Xingyi Zhang, Le Song, and Eric P Xing. Multimodal benchmarking of foundation model representations for cellular perturbation response prediction. bioRriv, pp. 2025-06, 2025.
Vikas Hassija, Vinay Chamola, Atmesh Mahapatra, Abhinandan Singal, Divyansh Goel, Kaizhu Huang, Simone Scardapane, Indro Spinelli, Mufti Mahmud, and Amir Hussain. Interpreting black-box models: review on explainable artificial intelligence. Cognitive Computation, 16(1): 45-74, 2024.
Ana-Maria Istrate, Fausto Milletari, Fabrizio Castrotorres, Jakub M Tomczak, Michaela Torkar, Donghui Li, and Theofanis Karaletsos. rbiol -broinnra scientific reasoning Ilms with biological world models as soft verifiers. bioRxiv, pp. 2025-08, 2025.
Longda Jiang, Carol Dalgarno, Efthymia Papalexi, Isabella Mascio, Hans-Hermann Wessels, Huiy- oung Yun, Nika Iremadze, Gila Lithwick- Yanai, Doron Lipson, and Rahul Satija. Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens. Nature Cell Biology, pp. 1-13, 2025.
Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He. Siqian He, Qingliang Li, Benjamin Shoemaker, Paul A Thiessen, Bo Yu, et al. Pubchem 2023 update. Nucleic acids research, 51(D1): D1373-D1380, 2023.
Dominik Klein, Jonas Simon Fleck, Daniil Bobrovskiy, Lea Zimmermann, Sören Becker, Alessandro Palma, Leander Dony, Alejandro Tejada-Lapuerta, Guillaume Huguet, Hsiu-Chuan Lin, et al. Cellflow enables generative single-cell phenotype modeling with flow matching. bioRxiv, pp. 2025-04, 2025.
Craig Knox, Mike Wilson, Christen M Klinger, Mark Franklin, Eponine Oler, Alex Wilson, Al- lison Pon, Jordan Cox, Na Eun Chin, Seth Strawbridge, et al. Drugbank 6.0: the drugbank knowledgebase for 2024. Nucleic acids research, 52(D1):D1265-D122
2024.
Wencheng Kong, Hangzhang Zhu, Sixing Zheng, Guang Yin, Panpan Yu, Yuqiang Shan, Xinchun Liu, Rongchao Ying. Hong Zhu, and Shenglin Ma. Larotrectinib induces autophagic cell death through ampk/mtor signalling in colon cancer. Journal of cellular and molecular medicine, 26(21): $539-5550, 2022.
Patrick Lewis, Ethan Perez, Alcksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, al. Retrieval-augment genera- tion for mowledge-intensingg nlp tasks. Advances in neural information processing systems, 33: 9459 9474, 2020.
Lanxiang Li, Yue You, Wenyu Liao, Xueying Fan, Shihong Lu, Ye Cao, Bo Li, Wenle Ren, Yunlin Fu, Jiaming Kong, et al. A systematic comparison of single-cell perturbation response prediction models. biolxxv,, pp. 2024-12, 2024.
Prisca Liberali, Berend Snijder, and Lucas Pelkmans. Single-cell and multivariate approaches in genetic perturbation screens. Nature Reviews Genetics, 16(1):18 18-322, 2015.
Leslie Loew and James C Schaff. The virtual cell: a software environment for computational cell biology. TRENDS in Biotechnology, 19100)4001446, 2001.
Romain Lopez, Jeffrey Regier, Michael B Cole, Michael Jordan, and Nir Yosef. Deep generative modeling for single-cell transcriptomics. Nature methods, 15(12):0033-1053 2018.
Mohammad Lotfollahi, Alexander Wolf, and Fabian J Theis. segen predicts single-cell perturbation responses. Nature methods, 16(8):715-721, 2019.
12

---

Published as a conference paper at ICLR 2026
Mohammad Lotfollahi, Anna Klimovskaia Susmelj, Carlo De Donno, Leon Hetzel, Yuge Ji, Ignacio L Ibarra, Sanjay R Srivatsan, Mohsen Naghipourfar, Riza M Daza, Beth Martin, et al. Predicting cellular responses to complex perturbations in high-throughput screens, Molecular systems biology. 19(6)e11117, 2023.
Ajay Nadig, Joseph M Replogle, Angela N Pogson, Steven A McCarroll, Jonathan S Weissman, Elise B Robinson, and Luke O'Connor. Transcriptome-wie wide characterization of genetic perturba- tions. BioRxiv, 2024.
Emmanuel Noutahi, Jason Hartford, Prudencio Tossou, Shawn Whitfield, Alisandra Denton, Cas Wognum, Kristina Ulicna, Michael Craig, Jonathan Hsu, Michael Cuccarese, et al. Virtual cells: Predict, explain, discover. arXiv preprint arXiv:2505. 2025.
Jueon Park, Yein Park, Minju Song, Soyon Park, Donghyeon Lee, Seungheun Back, and Jaewoo Kang. Cotox: Blain-of-thoughhee arXiv:2508.033 03, 2025.
molecular toxicity reasoning and prediction. arXiv preprint
Joseph M Replogle, Reuben A Saunders, Angela N Pogson, Jeffrey A Hussmann, Alexander Lenail, Alina Guna, Lauren Mascibroda, Eric J Wagner, Karen Adelman, Gila Lithwick- Yanai, al. Mapping information-rich senotype-phenotyy landscapes with genome-scale perturb-seq. Cell, 185(14):2559-277) 2022.
Yusuf Roohani, Kexin Huang, and Jure Leskovec. Predicting transcriptional outcomes of novel multigene perturbations with gears. Nature Biotechnology, 42(6):927-935, 2024.
Andreas Ruepp, Barbara Brauner, Irmtraud Dunger-Kaltenban
Goar Frishman, Corinna Mon- trone, Michael Stransky, Brigitte Waegele, Thorsten Schmidt, Octave Noubibou Doudieu, Volker Stümpflen, et al. Corum: the comprehensive resource of mammalian protein complexes. Nucleic (etoppp__):D666DDP0
acids research,
2007.
Sebastian Schmid, Zachary R Russell, Alex Shimura Yamashita, Madeline E West, Abigail G Parrish, Julia Walker, Dmytro Rudoy, James Z Yan, David C Quist, Betemariyam N Gessesse, et al. Erk signaling promotes resistance to trk kinase inhibition in ntrk fusion-driven glioma mouse models. Cell reports, 43(10), 2024.
Damian Szklarczyk, Annika I. Gable, Katerina C Nastou, David Lyon, Rebecca Kirsch, Sampo Pyysalo, Nadezhda T Doncheva, Marc Legeay, Tao Fang, Peer Bork, et al. The string database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic acids research, 49(DI)D605-DD11 2021.
Xiangru Tang, Zhuoyun Yu, Jiapeng Chen, Yan Cui, Daniel Shao, Weixu Wang, Fang Wu, Yuchen Zhuang, Wenqi Shi, Zhi Huang, et al. Cellforge: Agentic design of virtual cell models. arXiv preprint arXiv:2508.0222 2025.
Jason Wei, Xuezhi Wang. Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thouulle prompting elicits reasoning in large language models, Advances in neural information processing systems, 35:24824-244 -4837,, 2022.
Robert F Woolson. Wilcoxon signed-rank test. Wiley encyclopedia of clinical trials, pp. 1-3, 2007.
Menghua WU, Russell Littman, Jacob Levine, Lin Qiu, Tommaso Biancalani, David Richmond, and Jan-Christian Huetter. Contextualizing biological perturbation experiments through language. arXiv preprint arXiv /290, 2025.
Yan Wu, Esther Wershof, Sebastian M Schmon, Marcel Nassar, Blažej Osiński, Ridvan Eksi, Zichao Yan, Rory Stark, Kun Zhang, and Thore Graepel. Perturbench: Benchmarking machine learning models for cellular perturbation analysis. arXiv preprint urXiv:2408. 10609, 2024.
Jesse Zhang, Airol A Ubas, Richard de Borja, Valentine Svensson, Nicole Thomas, Neha Thakar, Ian Lai, Aidan Winters, Umair Khan, Matthew Jones, et al. Tahoe- 100m: A giga-scale single- cell perturbation atlas for context-dependent gene function and cellular modeling. BioRxiv, pp. 2025 02, 2025.
Zhaopeng Zhang, Jishou Ruan, Jianzhao Gao, and Linn-Xiang Wu. Predicting essential proteins from protein-protee pootein interactions using order statistics. Journal of Theoretical Biology, 480:274-283 2019.
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A GeneTAK
14
A.1
Data Processing
14
A.2 Distribution of Gene
14
Experiment Details
15
B.1 Metries
15
B.2 Baselines
16
B.3 Large Language Models
16
B.4 Inference Parameters Settings
17
B.5 Cost and Latency Analysis
17
B.6 Evaluation Strategy
17
B.7 Knowledge Texp Pipeline for Prompt Generation
18
B.8 Rule-Based Templated Verbalization
19
B.9 Fact Aggregation and Prompt Assembly
19
C Case Study
19
D Prompts
22
A GENETAK
A.1 DATA PROCESSING
We normalized all gene counts log(TP10k + 1) values (log-transformed UMI count per 10k), where the count Cij of gene j in cell is mapped to:
log Σ Gj
10,000 +
To focus on the most variably expressed genes, the top 2,000 highly variable genes (HVGs) were selected using the Seurat v3 Python package, and retained in the expression matrix for down- stream analyses, with the HVG list exported for reproducibility. To determine differentially expressed genes (DEGs), correction (Benjamini & Hochberg, 2000) between non-targeting control (NTC) cells and perturbed cells, for each perturbation.
the Wilcoxon signed-rank test (Wilcoxon, 1945) with Benjamini Hochhirg
We set FDRuseshlld and logFCthreshold to screen perturbations with phenotypic effects, and LVAL-mnshold to screen negative samples (samples with significant change in gene expression). Specifically, when
FDRaheshold < 0.05 & logFCrhnestoll threshold 0.25
(i.e., more than 1.28-fold change expression), consider them as samples with phenotypic effects. Based on this, when
PVAL threshold 2 0.1,
we consider them as negative samples. addition. we set Nuog = 200 to limit the number of negative samples, avoid category imbalance.
A.2 DISTRIBUTION OF GENETAK
To keep consistent with the experimental settings in the STATE paper, the dataset of each cell line was split into training and testing sets at ratio of 30:70. the experiment, VCWorld only retrieved samples from the training set. Further details regarding the dataset and data split statistics provided in Table 4 and 5.
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Table Number of cells each cell line.
Cell line
Control
Perturbed
Sum
C32
29651
993514
1023165
HepG2C3A
20451
751387
771838
HOP62
39683
1467690
1507373
Hs 766T
17802
695932
713734
PANC-1
43878
1534738
1578616
Table 5: Gen statistics for differentially expressed genes.
Cell line
Split
Total genes
Non-DE genes
Differentially expressed genes
Total
Up-regulated
Down-regulated
Train
128293
55200
73093
24746
48344
C32
89961
38800
51161
16643
34518
Train
102253
55200
47053
16820
30233
HepG2C3A
Test
72169
38800
33369
11629
21740
Train
154969
55200
99769
36615
63154
HOP62
Test
108777
38800
69977
25472
44505
Train
101707
55200
46507
20774
25733
Hs 766T
Test
71891
38800
33091
14420
18671
Train
163748
55200
108548
54081
54467
PANC-1
Test
115568
38800
76768
38131
38637
B EXPERIMENT DETAILS
B. METRICS
B.1.1 ACCURACY
Accuracy measures the proportion of correctly predicted samples among all samples. It is defined as:
TP + TN
Accuracy
TP+TN+ + F
where TP, TN, FP, and FN represent the numbers of true positives, true negatives, false positives, and false negatives, respectively.
Definition of Confusion Matrix Components. model predictions ý E {0, 1}:
Given binary ground-truth labels y E {0, 1) and
Σ TP
[ LA 1),
TN
I(y: = 0A = 0),
i=1
i=
N FP Σ
N
I[y = 1),
FN Σ
Ily = 1 LA =),
where II(-) is the indicator function.
B.1.2 FI-SCORE
F1-score is the harmonic mean of Precision and Recall, balancing prediction correctness and coverage of true positives. The metrics are defined as:
TP
TP TP + FN'
Precision =
Recall
TP + FP
Precision Recall
Fl 2
Precision Recall
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B.1.3 AUROC & AUPRC
The Area Under the Receiver Operating Characteristic Curve (AUROC) measures the model's ability to distinguish between classes. The True Positive Rate (TPR) and False Positive Rate (FPR) are defined as:
TP
FP
TPR
FPR
TP FN
FP+TN
AUROC is computed as:
AUROC I. (PR(F d(FPR).
The Area Under the Reecision-Recall Curve (AUPRC) quantifies the trade-off between Precision and Recall:
L Precision(
AUPRC
Precision(Recall)) (Reaal)) deReall,)
where Precision and Recall are computed as defined above.
B.1.4 Q-SCORE
AP
Q-score QP AP
where AP means the number of answered prompts, QP means the number of prompts without normal answer.
B.2 BASELINES
All baselines were trained on 30% of the 45 cell lines from T00oe-100m and test cell lines, and all of our GeneTAKK data was extracted from the remaining 70%, which eliminates the possibility of data leakage, Table5 the result obtained from the inference of baseline models on five cell lines.
GAT. We moved beyond the disjoint subgraph design mentioned in the original manuscript. We integrated multiple knowledge sources (PubChem, DrugBank, GO, UniProt, Reactome) into a single connected graph structure, using standardized identifiers to align entities across databases. We used 70% the labeled Gene'TAK instances for training the GAT model, with the prediction task being a three-class classification comprising up, down, and no change. The source code of training is available at wwt.t:::///// /ttp//gtttttttt/tt//// com/ GENTEL- lab VCWorld.
CPA is framework learn the effects of perturbations at the single-cell level. CPA encodes and learns phenotypic drug responses across different cell types, doses, and combinations. The source code of CPA is available https: : / g thub. com/ the alab/ccaappp cpa. In our experiments, use the official implementation with the hyperparameters.
seVI is family of probabilistic models that perform many analysis tasks across single-cell, multi- omics, and spatial omics data. The source code of scVI is available at https: : //////g/ //gggttthh/ com/ scverse/ scvi-tools. our experiments, adopt the standard scVI model with the hyperpa- rameters in the official GitHub repository.
STATE is machine learning model that predicts cellular perturbation responses across diverse contexts. The source code of STATE is available at https: /tttps://ittii thub. com wwt./ArrInnntttiti state. our experiments, we use the official implementation with LLaMA-style transformer backbone and the key hyperparameters.
B.3 LARGE LANGUAGE MODELS
Llama-3.1 is Meta's 8B- SB-parameter instruction-tur tuned chat model optimized for multilingual dia- logue, outperforming many open and closed models standard benchmarks. The source code of L-88 Instructt is available at https: : http///uugggggggggg////gggggg/g co/ a 1mmammmmm/ammmmmmmmm aoma/Llamme L1amm--3 1-8B-1 Instruct.
Qwen2.5-7B-Instruu is a 7.6B 16B-parameter instruction-tume open- open-source model with strong per- formance and efficiency multilingual tasks, long-context understanding, and structured output
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generation. The source code of Qwen2.5-7B. Innstrructtttttu is available at https://huggingfaae //////ee//wwnae 5- 7B- Instruct.
Qwen3-4B-instruct-550 is the updated 4B- -B-parameter Qwen3 instruction model with marked improvements in instruction following, reasoning, math, coding, and tool use, well-suited for lightweight multi- deployment. The source code of Twen3-4B-Instrruce is available at co/ Qwen /wwenn/wwn33-e /tt/://////e--------n--------- Inst 2507.
model in the Qwen2.5 family, offering significantly improved coding and math reasoning capabilities over Qwen2 for more demanding applications. The source code of Qwen2.5-144 1-B-------- is available at https:://// httg//uuggiiig face... face. co co/Que /o//wen//e //wwe//wwe2.....////////m/ Qwen2. 5- 144B- Instruct.
Qwen225-144-Insttuud is 14B-pan earammmeteer instruction-tun
Gemini-2.5-Flash is Google's s priic-peffommancc-optiizzz Gemini model with built-in "thinking" capabilities, designed for large-scale, low-latency, high-throughput and agentic workloads.
B.4 INFERENCE PARAMETERS SETTINGS
For all models, we standardized the inference parameters with temperature=0. 6 and top_k 0 to balance creativity and determinism.
B.5 COST AND LATENCY ANALYSIS
We monitored cost and latency using the OpenRouter API. To ensure fair benchmark, the input prompt length was standardized to approximately 2,600 tokens for all models. The inference output tokens varied based the reasoning length of each model. The detailed cost/latency breakdown for processing 1,000 samples is provided in Table 6.
Table 6: Model Cost and Latency(s)
Model Name
Input Price
Output Price
Input Token
Output Token
Latency(s)
Total Cost($)
LLAMA3 8B
0.03
0.06
2600
1300
0.31
0.156
Qwen2.5-7B
0.04
0.1
2600
2700
0.42
0.374
Qwen2.5-14B
0.05
0.22
2600
2700
0.51
0.724
Qwen3-4B 4-B
0
2600
2700
0.68
0
Gemini-2.5-Plann
0.30
2.50
2600
1400
0.56
4.28
Table Statistics of predicted number DEGs
Task
Model
Count
C32
HepG2C3A
HOP62
766T
PANC-1
Truth
Number
128293
102253
154969
101707
163748
Prediction
157758
366238
317589
86684
316434
STATE
Correct
19488
33122
55717
9077
60640
DE
Prediction 21475
21204
19524
19838
19253
CPA
Correct
2424
2076
2755
1394
3178
Prediction 460477
466868
449047
470606
435034
SCVI
Correct
60973
40108
79858
38969
83801
B.6 EVALUATION STRATEGY
Our evaluation strictly adheres to zero-shot Drug Prediction protocol. Specifically:
When predicting the effect of test drug Prest, the retrieval corpus D consists exclusively of drugs from the training set.
The model never has access the ground truth the entity of Ptest itself during retrieval.
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Inference relies solely identifying functionally analogous cases (based on MoA and chemical structure) from the training set to extrapolate the potential effects of the unseen drug.
B.7 KNOWLEDGE GRAPH-TO- TEXT PIPELINE FOR PROMPT GENERATION
To ensure the factual grounding and mitigate the risk of hallucination in our language model, we developed a deterministic pipeline programmatically construct input prompts from multiple struc- tured biological knowledge graphs (KGs). This process transforms machine readable graph data into human- reanalle, factual context that guides the model's summarization task. The pipeline consists of three primary stages: data integration and pre-processing, rule-based templated verbalization, and fact aggregation for prompt assembly.
machine-readable
B.7.1 DATA INTEGRATION AND PRE- PRE-PROCESSING
Our pipeline integrates information from diverse set of public biological knowledge bases, including Ensembl and UniProt for descriptive annotations, and graph-structured databases such as the Gene Ontology (GO), Reactome, CORUM, BioPlex, and STRING for relational information. Upon ingestion, pre-processing step is applied to enhance data quality. Notably, we filter out high-degree nodes from relational graphs (e.g., generic GO terms like "protein binding" highly promiscuous interactors in STRING). This step reduces noise and focuses the downstream context on more specific and informative biological relationships.
B.7.2 RETRIEVAL STRATEGY ANALYSIS
Sensitivity Analysis. To validate this choice, we conducted ablation study increasing the neighborhood depth from I to (Table 8). As shown in the additional results, increasing the depth significantly increased computational resource consumption and context length but resulted in negligible performance gains. This confirms that a I-hop neighborhood is the optimal trade-off between efficiency and information density for this task.
Table 8: Accuracy Performance On Neighborhood Depth Variations Experiment
Task
Hop
C32
HepG2C3A
HOP62
DE
1
0.70
0.68
0.71
2
0.72
0.65
0.70
3
0.69
0.67
0.70
B.7.3 DECOUPLED RETRIEVAL STRATEGY
We employ a decoupled retrieval strategy for drugs and genes, utilizing a weighted combination of semantic and structural similarity.
Drug Retrieval (a = 0.7). We assign a weight a 0.7 to MoA-based semantic similarity and 0.3 to cructure-based similarity (molecular fingerprints). This reflects the assumption that Mechanism of Action serves as the most direct reference for drug perturbation effects, while molecular structure provides complementary information related scaffolds and functional groups.
Gene Retrieval (a = 0.7). We set the weight for functional semantic annotation to = 0.7 and for PPI-based structural similarity curated and generally high-confidence nature. Conversely, recent studies (Zhang et al., 2019) indicate that PPI networks may contain false- posstivv links. To mitigate noise, we assign lower weight the graph-derived component.
a 0.3. Semantic annotations are prioritized due their
B.7.4 RETRIEVAL- -ET SIZE
We evaluate retrieval-set sizes of 5 and 10 (Table 9). The main effect lies in the model's willingness answer. When the size is reduced the definitive response the C32 cell line drops from
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87% 64%, as the model more frequently abstains due to insufficient evidence, effectively reducing hallucination but also degrading overall performance.
Table 9: Response Rate On Retrieval-set Size Experiment
Task
Retrieval
C32
HepG2C3A
HOP62
Average
5
0.64
0.67
0.60
0.64
DE
10
0.87
0.87
0.88
0.87
B.8 RULE-BASED TEMPLATED VERBALIZATION
The core of our methodology is a rule- based verbalization engine. Recognizing that each knowledge source possesses a unique data schema, we designed specific template translate its structured entries into natural language declarative statements.
For instance:
A relationship from CORUM, representing (Gene_ A, is member of, Complex_X, x, fa__eel__ingg Y), verbalized as: Gene A is a member of the Complex X in cell line Y.
A protein-protein interaction from STRING, representing (Gene_A, Interacts_with Gene_R) with associated evidence types [experimental, databasel, is translated to: Based on evidence from experimental evidence and database evidence, Gene may physically interact with Gene B.
Functional annotations from UniProt are extracted and formatted as statements like: Functions Gene include: [function description].
This templated approach ensures that the conversion is consistent, deterministic, and preserves the semantic content of the original source.
B.9 FACT AGGREGATION AND PROMPT ASSEMBLY
For each target gene, aggregate declarative statements (facts) collected from all integrated sources into single set. To automatically remove duplicates arising from overlapping databases, we use set data structure. To keep the prompt concise and reduce the cognitive load on the model, we limit the maximum number of facts included, giving priority high-confidence sources such UniProt and curated pathway databases.
The resulting collection is then formatted bulleted list and inserted into a master prompt template under the heading "Domain Knowledge:" This final prompt conveys to the language model its role, clearly defined task, operational constraints, and the essential, factually grounded context required to produce an accurate and reliable summary.
Given the imbalanced nature of the TeK dataset, we agree that AUPRC provides more informative assessment of performance on the minority class (differentially expressed genes).
C CASE STUDY
To qualitatively assess the explainability and reasoning capability of VCWorld, as well as alignment with relevant wet-lab findings, we illustrate a representative case. The example focuses the model's prediction of the effect of Larotrectinib on the gene MKI67.
Larotrectinib is precision-targeted therapy that inhibits TRK kinase activity, thereby blocking the proliferative and survival signaling pathways driven by NTRK fusions in tumors. MKI67 encodes the nuclear protein Ki-67. eell-established marker of cellular proliferation. High expression of Ki-67 indicates active cell division and frequently used oncology to assess tumor proliferation, guide treatment selection, and predict prognosis.
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Target Inference (Larotrectinib)
Evidence Chain (VCWorld Reasoning)
Retrieval Augmentation
Larotrectinib (Inhibitor)
TRK Kinase
Afatinib (Inhibitor)
Inhibits TRK Kinase Activity
Afatinib Inhibits EroB Pathway Reduces MKI66 Expression
Mechanism Similarity Comparison
Blocks NTRK-Driver
2. Cross-Pathway Effect Generalization Infer Causal Relationship
Proliferation Survival Signaling
MK167 Levels
Cytarabine Belumosudil
MKI67 (Ki-67 marker) Gene
PREDICTION: Larotrectinib |
MKI67 Expression
Figure 4: VCWorld Inference (Larootrcttiib.MKI16)) The schematic illustrates the inference process for Larotrectinib-indueel MK167 downregulation, integrating direct target inference with retrieved biological evidence (BioContext). The LLM synthesizes these inputs via Chain-of-Thhouge (CoT) reasoning to predict causal relationships.
Although direct evidence has previously linked Larotrectinib with MK167 expression, VCWorld provided plausible reasoning pathway that supports their association (Figure 4). The model first retrieved Afatinib- drug with similar mechanism-ar positive reference. Evidence showing that Afatinib suppresses the ErbB signaling pathway and reduces MKI67 expression suggested that inhibition of upstream kinases can influence proliferative markers. Furthermore, perturbations induced by Cytarabine and Belumosudil also led to alterations in MKI67 levels, reinforcing its role as a reliable indicator of anti proliffeiertee effects.
Based this reasoning chain, VCWorld inferred that Larotrectinib downregulates MKI67 expression, a conclusion consistent with recent findings reporting that "treatment with larotrectinib led to a reduction in proliferating cells (5.73% Ki67-positive cells)" (Schmid al., 2024). Consistently, immunoftuorescence (IF) analysis revealed decreased Ki-67 signal intensity following Larotrectinib treatment (Kong al., 2022).
Besides, we also show the original output results of the model used for reasoning about (Larotrectinib, MKI67), presented using the original markdown text.
Answer for Drug: pertteecinib, Gene: MK167
1) *-Which of the observed perturbing drugs are most similar to Larotrectinib (if any, including Larotrectinib itself) ?** ?**
The most similar observed perturbing drug to Larotrectinib (Example +)++,
**fatinib
Is an inhibitor of tropomyosin receptor kinases (Trk) A, B, and C. It prevents neurotrophin-IIk interactien and subsequent Trk activation, inhibiting downstream signaling pathways that regulate cell growth, differentiation, and survival.
wwllarotrectinib:s
**Aattinib:* Is n irreversible ErbB family blocker that covalently
binds to and inhibits the kinase domains of EGER, HER2, and HER4.
This inhibits tyrosine kinase autoghosphorylatiion, downregulating
ErbB signaling, and affecting genes involved in cell proliferation
and survival.
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Both arugs kinase inhimitors that target receptor tyrosine kinases (Trk Larotrectinib's case, ErbB in Afatinib's case) and primarily impact downstream signaling pathways related to cell proliferation and survival.
2) **hen using Larotrectinib similar drugs, what downstream pathways or genes differentially expressed? Justify your answer with the observed outcomes.**
Based on the description of Larotrectinib, it is expected to suppress the expression of genes involved in **cell growth, differentiation, and survival+* that are regulated by Trk signaling.
The most similar drug, e*ffatinib (Example *)*,, which also inhibits receptor tyrosine kinases involved in cell proliferation and survival, resulted in exdifferential expression of MKI67**. This indicates that porturdations of receptor tyrosinc kinase pathways affecting cell growth and proliferation indeed lead to changes in MKI67 expression, Other drugs impacting MKI67 (e.g., Belumosudil affecting STAT3/S, Cytarabine causing DNA damage/cell cycle control) also point to pathways that regulate cell proliferation and division as being differentially expressed.
3) What perturbations of upstream pathways or genes result in differential expression of MKI67 or similar genes (if any) ? Justify your answer with the observed cutcomes. MKI67 is marker of cell prolifcration and is impacted cell cycle perturbations The examples shew that various perturbations targeting cell growth, cell sycle control, and cellular stress responses result in di differential expression of MK167:
IInhibition of growth-related receptor tyrosine kinases:** **Afatinibe* (ErbB family blocker, affects cell proliferation and survival, Example 9) resulted in differential expression of MK167. *MModulation Rho-associated coiled-coil containing protein kinases (ROCK) and STAT pathways:** *=Belumcsudil+* (ROCK2 inhibitor, modulates STAT3/STATS, Example 2) resulted in differential expression of MKI67. SIAT pathways eften involved in cell growth and differentiation.
=*Induction of DNA damage and replication stress:** **Cytarabinewv (DNA polymerase inhibitor, causes DNA damage and replication stress, affecting cell cycle control, Example 10) resulted in differentia. expressien of MKI67.
**induction of oxidative stress/callular stress respanse:**
**verteporfin*> (photosensitizer generates reactive oxygen species, incucing oxidative stress and cellular stress response, Example 6) resulted in differential expression of MKI67.
*AActvvation of metabolic pathways:** *Rosiglltazonee (PPAR-gamma agonist, affects glucose metabelism, Example 7) resulted in differential expression of MKI67, suggesting link between metabolism and proliferation.
Perturbations that interfere with fundamental processes like DNA replication, cell eycle progression, growth signaling, induce signi ficant cellular stress tend impact MK167 expression.
4) Is a perturbation of larotrectinib in MK167 likely to result in di differenial expression of MKI67? For example, if 2) and 4) are will observe
unrelated only indirectly related, it is unlikely differential expression. On the other hand, if 2) and significantly overlap specific genes or pathways, we may observe differential expression. Your final answer should end with one of these three options and nothing clse.
Yes. Perturbation of Iarotrectinib results in differential expression of MKI67.
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User Prompt
In Hs766T cells, how does Tofacitinib affect the expression of FTH1 ?
Generated Prompt
[Start Prompt]
You are an expert molecular biologist who studies how small molecule drugs affect gene expression using Perturb-seq. Your goal to determine: Does a drug perturbation Tofacitinib Hs766T cells result differential expression of FTH1?
You are given as input:
Description the drug (Tofacitinb): description drug that is to perturb the cell
Description gene interest (FTH1): description of gene, the impact on which you wish to infer
Context: description of call line which genes expressed
Examples: set of experimental observations that describe the impact of small molecule perturbations on related genes, contextualize your answer
Output: Please extract the most relevant parts of the examples that address these five questions. Be specific.
Which of the observed perturbing drugs are most similar to Tofacitinlb any. including Tofacitinib itselfy?
When using Tofacitinib or similar drugs, what downstream pathways or genes are differentially expressed? Justify your answer with the observed outcomes.
Which of the observed genes interest are most similar to FTH1 any, including FTH1 itselfy?
What perturbations of upstream pathways genes result in differential expression FTH1 similar genes any?? Justify your answer with the observed outcomes.
in FTH1 likely to result differential expression FTH1? example, and 4) are unrelated or only indirectly related, it unlikely we will observe differential expression. On the other hand, if 2) and 4) significantly overlap specific genes or pathways. may observe differential expression. Your final answer should end with of these three options and nothing else.
perturbation of
No. Perturbation Tofacitinib does not impact FTH1.
Yes. Perturbation Tofacitinib results in differential expression FTH1.
There is insufficient evidence to determine how Perturbation of Tofacitinib affects FTH1.
[End Prompt]
[Start Imput]
Description of molecule drug (Tofacitinib): Tofacitinib is reversible Janus kinase (JAK) inhibitor that prevents the phosphorylation and activation of STAT transcription factors. This action blocks the JAK-STAT signaling pathway, which involved hematopoiesis and immune cell function. This perturbation affect the transcription of polokine-responsiee genes that are regulated by the JAK-STAI pathway and are involved inflammaatrr responses.
Description of gene of interest (FTH1): FTH1 is a ferritin heavy chain subunit enabling iron ion binding and homeostasis. overioad, iron deficiency,
sequestering, involved
storage intracellular oxidative stress could impact FTH1 expression to regulate cellular iran levels.
adenocarcinoma (PDAC) line, established lymph node metastasis of 73-year-old female patient. It carries hallmark pancreatic cancer alterations including KRAS G12D mutation, TP53 mutation, and CDKN2A inactivation, reflecting the canonical molecular adherently with epithelial morphology. display doubling time
Context: 766T human pancreatic
landscape of PDAC. The cells -80- 70 hours, and are microsatellite stable (MSS). Hs 766T is widely used in studies of pancreatic tumor progression, metastasis, stromal interactions, and therapeutic resistance, and is included large-scale resources such as the NCI-60 panel for pharmacogenomic profiling.
Examples:
Example 1:
Drug: o-Kii-IN-
Gene: FTH1
D PROMPTS
We provide the prompts used by VCWorld and the model's response in the Appendix D. It clearly demonstrates that VCWorld is interpretable, capable of aligning with world biological knowledge. and has a traceable thinking process and final result.
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Drug Description: c-Kit-IN-1 as ATP-competitive inhibitor, binding the kinase domain the c-Kit receptor tyrosine kinase. This prevents the autophosphorylation of the receptor and the activation of downstream signaling pathways. Perturbation with c-Kit-IN-1 may affect the expression of genes regulated by the o-Kit signaling pathway, such as those in the MAPK and PI3K pathways.
Gene Description: FTH1 is a ferritin heavy chain subunit enabling iron ion binding and sequestering, involved iron storage and intracellular iron homeostasis. Iron overload, iron deficiency. or oxidative stress could impact FTH1 expression regulate cellular iron levels.
Result: Perturbation of this drug results differential expression of the gene of interest.
Example 2:
Drug: AZD2858
Gene: FTH1
Drug Description: AZD2858 binds the bromodomains BET proteins. preventing them from binding to acetylated histones. This mechanism disrupts the transcriptional activation of key oncogenes, such as MYC. Perturbation with AZD2858 may affect the expression BET target genes involved cell proliferation.
Gene Description: FTH1 is a ferritin heavy chain subunit enabling iron ion binding and sequestering, involved in Iron storage and intracellular iron lon homeostasis. Iron overload, iron deficiency, or oxidative stress could impact FTH1 expression to regulate cellular iron levels.
Result: B) Perturbation this drug results differential expression of the gene interest.
Example 3:
Drug: BI-78D3
Gene: FTH1
Drug Description: BI-78D3 a small molecule inhibitor of the protein-protein interaction between the transcription factor NRF2 and its negative regulator KEAP1. 1. By disrupting this interaction, modulates the activity of the NRF2 pathway. Perturbation with BI-78D3 may affect the expression of genes regulated by the NRF2 antioxidant response element.
Description: FTH1 is ferritin heavy chain subunit enabling iron storage and intracellular iron ion homeostasis. Iron overload, iron deficiency, oxidative stress could impact FTH1 expression to regulate cellular iron levels.
binding sequestering, involved
Result: B) Perturbation of this drug results in differential expression of the gene of interest.
Example 4:
Drug: Vemurafenib
Gene: FTH1
Drug Description: Vemurafenib competitive inhibitor mutated wwFF--erinettrrennirr kinase, particularly the BRAF V600E mutation. blocks the downstream MAPK signaling pathway to inhibit tumor growth and trigger apoptosis. This perturbation may affect the expression of genes regulated by the MAPK/ERK pathway, which are involved in cell growth and proliferation.
Gene Description: FTH1 is a ferritin heavy chain subunit enabling iron ion binding and sequestering, involved iron storage and intracellular iron homeostasis. Iron overload, iron deficiency, or oxidative stress could impact FTH1 expression to regulate cellular iron levels.
Result: Perturbation of this drug does not impact the gene of interest.
Example 5:
Drug: Ciclopirox
Gene: FTH1
Drug Description: Ciclopirox is an antifungal agent thought to function by chelating polyvalent metal cations, which inhibits enzymes involved processes like mitochondrial electron transport. This perturbation may affect the expression genes involved cellular metabolism, regulation.
responses, DNA repair, and cell cycle
Gene Description: FTH1 is a ferritin heavy chain subunit enabling iron ion binding and sequestering, involved in iron storage and intracellular iron ion homeostasis. Iron overload, iron deficiency, or oxidative stress could impact FTH1 expression to regulate cellular iron levels.
Result: Perturbation this drug results differential expression the gene interest.
Example 6:
Drug: Afatinib
Gene: FTH1
Drug Description: Afatinib is an irreversible ErbB family blocker that covalently binds to the kinase domains of EGFR, HER2, and HER4. This binding irreversibly inhibits tyrosine kinase autophosphorylation, resulting in the downregulation ErbB signaling. This perturbation may affect the expression genes downstream of the ErbB signaling pathway that involved in cell proliferation and survival.
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Gene Description: FTH1 ferritin heavy chain subunit enabling binding and sequestering. involved iron storage and intracellular iron ion homeostasis. Iron overload, Iron deficiency, oxidative stress could impact FTH1 expression to regulate cellular iron levels.
Result: B) Perturbation of this drug results in differential expression of the gene of interest.
Example 7:
Drug: BI-3406
Gene: FTH1
Drug Description: BI-3406 binds the catalytic domain of SOS1, preventing it from engaging with and activating KRAS. This leads to the inhibition of KRAS signaling and reduction in the proliferation of KRAS- dependent cancer cells. Perturbation with B1-3406 may affect the expression of genes downstream of the KRAS signaling pathway.
Gene Description: FTH1 is a ferritin heavy chain subunit enabling iron ion binding and sequestering, involved iron storage intracellular
homeostasis. Iron overload, deficiency, or oxidative stress could impact FTH1 expression to regulate cellular iron levels.
Result: Perturbation of this drug results in differential expression of the gene of Interest.
Example 8:
Drug: AZD1390
Gene: FTH1
Drug Description: AZD 1390 a potent inhibitor Taaiia-elanngietaati Mutated (ATM) kinase. This action prevents the activation of DNA damage checkpoint and disrupts DNA repair processes. Perturbation with
AZD1390 may affect the expression of genes involved in the DNA damage response and cell cycle checkpoints regulated by ATM kinase.
Gene Description: FTH1 is a ferritin heavy chain subunit enabling iron ion binding and sequestering, involved iron storage and intracellular iron ion homeostasis, Iron overload, iron deficiency, or oxidative stress could impact FTH1 expression to regulate cellular iron levels.
Result: Perturbation this drug not impact gene interest.
Example 9:
Drug: Palmatine (chloride)
Gene: FTH1
Drug Description: Palmatine (chloride) has multiple mechanisms action, including acting as a poison for topoisomerases, which leads DNA damage. It also inhibits indoleamine 2,3-dioxyeenase (IDO-1), an tolerance. Perturbation genes involved in DNA replication and repair, well immune response pathways.
enzyme involved
Palmatine (chloride) may affect the expression
Gene Description: FTH1 is a ferritin heavy chain subunit enabling iron ion binding and sequestering, involved iron storage and intracellular iron ion homeostasis. Iron overload, iron deficiency, oxidative stress could impact FTH1 expression to regulate cellular iron levels.
Result: A) Perturbation of this drug does not impact the gene of interest.
Example 10:
Drug: Regorafenib
Gene: FTH1
Drug Description: Regorafenib is a small molecule inhibitor of multiple membrane-boune and intracellular kinases, including VEGFR, KIT, PDGFR, FGFR, and RAF. This broad inhibition targets pathways involved in oncogenesis, tumor angiogenesis, and the maintenance the tumor microenvironment. This perturbation may affect the expression genes regulated by these various kinase signaling pathways that are involved in proliferation and angiogenesis.
Gene Description: FTH1 is ferritin heavy chain subunit enabling in iron storage and intracellular iron ion homeostasis. Iron overload, iron deficiency, oxidative stress could impact FTH1 expression to regulate cellular iron levels.
binding and sequestering. involved
Result: A) Perturbation of this drug does not impact the gene of interest.
[End Input]
[Start Output]
3)
[End of Output]
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29
Thoughts
Investigating Tofacitinib's Impact
I'm focusing on Tofacitinib, a JAK inhibitor. and its potential to influence FTH1 expression in Hs766T colls. I've begun by examining Tofacitinib's mechanism, specifically role blocking JAK-STAT signaling pathways. This understanding
crucial predicting its effect on FTH1, target gene. My step involves researching the link between JAK-STAT and iron metabolism to understand the downstream effects on FTH1 expression.
Exploring Drug Similarities
I'm now comparing Tofacitinib to the example drugs, focusing their mechanisms of action. see o-Kit-IN-1, tyrosine kinase inhibitor, as potentially relevant kinase-inhibiting nature, though directly targeting JAK-STAT. The other examples seem quite different, acting on epigenetic regulators cellular stress responses. I'll need assess shared downstream pathways might indirectly link them to FTH1 regulation.
Comparing Drug Kinase Inhibitors
I'm deep into the analysis kinase inhibitors. Several example drugs share this mechanism with Tofacitinib, though they target distinct Regorafenib particularly relevant, due to their inhibition kinases involved various signal transduction pathways. I'm focusing now identifying potential shared downstream pathways that might connect these drugs to FTH1 regulation, despite their diverse primary targets.
families.
c-Kit-IN-1, Vemurafenib, Afatinib, AZD1390 and
Refining Kinase Connections
Now delving deeper the potential between the kinase inhibitors and FTH1. Given examples, that while Tofacitinib directly blocks JAK-STAT, other drugs like c-Kit-IN-1. Vemurafenib, Afatinib, AZD1390, and Regorafenib might indirectly influence FTH1 through shared downstream pathways. I'm focusing those pathways activated or inhibited by these kinase drugs to evaluate their relevance to FTH1 regulation. This will necessitate cross-referencing published literature.
Identifying JAK-STAT Targets
I'm focusing identifying the specific cytokine-reeponsive genes regulated by the JAK-STAT pathway, which is affected by Tofacitinib. The goal is determine if any of these target genes might indirectly influence FTH1 expression. While no drugs are directly analogous, some kinase inhibitors (c-Kit-IN-1, Afatinib) showed FTH1 differential expression, highlighting the need to understand potential shared pathways. I'm cross- referencing literature to link Tofacitinib's pathway to FTH1's regulators.
Evaluating Pathway Links
I'm focusing on how the JAK-STAT pathway, targeted Tofacitinib, might intersect with FTH1 regulation. Considering the lack of direct JAK-STAT inhibitors in the examples, I'm examining inflammatory responses, key feature of JAK-STAT signaling, could indirectly influence Iron homeostasis and FTH1 expression. This will require further literature exploration.
Linking JAK-STAT to Iron I'm focusing how the JAK-STAT pathway, which targeted Tofacitinib. might intersect with iron regulation Hs766T cells. While I've examined cytokine-responsive genes, I'm now exploring potential connections between inflammatory responses and FTH1 expression, requiring extensive literature review.
Evaluating JAK-STAT Impact
connection between Tofacitinib's JAK-STAT pathway FTH1 regulation under review. I'm focusing inflammatory responses driven this pathway. these might influence iron homeostasis. also examining the literature regarding STAT transcription factor regulation of iron-related genes to predict FTH1 expression shifts.
Predicting Tofacitinib's 's Effect I'm now integrating information JAK-STAT and iron regulation to predict if Tofacitinib will alter FTH1 Hs766T. Given that the JAK-STAT pathway influences inflammatory responses and iron metabolism, appears plausible that FTH1 expression will shift with Tofacitinib. This requires further review.
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I've determined that Tofacitinib, JAK-STAT inhibitor, has plausible link to FTH1 regulation in the Hs766T calls, primarily through the effects of inflammation iron homeostasis. This requires further scrutiny, there is currently no direct evidence from the examples.
Analyzing JAK-STAT's Influence
I'm now considering how Tofacitinib, which inhibits JAK-STAT pathway, interacts with regulation. Although there direct JAK-STAT inhibitors among the examples, this is plausible mechanism. FTH1 known to be regulated by oxidative stress, metal chelation, and the KRAS pathway,
which linked.
The link between Tofacitinib, a JAK-STAT inhibitor, and FTH1 in Hs766T cells is clear. The JAK-STAT pathway is involved in inflammation, which impacts iron homeostasis. KRAS, deregulated this cell line, feeds into JAK-STAT pathway, which shown impact FTH1.
A
Answer
Which of the observed perturbing drugs are most similar to Tofacitinib (if any, including Tofacitinib itself)?
Tofacitinib
kinase (JAK) inhibitor that blocks the JAK-STAT signaling pathway. None the drugs the provided examples JAK Inhibitors. The most similar class of drugs are other kinase inhibbtoos, although they target different pathways. These include: c-Kit-IN-1: A c-Kit receptor tyrosine kinase inhibitor.
Vemurafenib: A BRAF serine-threonine kinase inhibitor.
Afatinib: ErbB family tyrosine kinase inhibitor.
AZD1390: ATM kinase inhibitor.
Regorafenib: multi-kinase inhibitor targeting VEGFR, KIT, PDGFR, FGFR, and RAF.
When using Tofacitinib or similar drugs, what downstream pathways or genes are differentially expressed? Justify your answer with the observed outcomes.
Tofacitinib described affecting cytokine-responsive genes regulated by JAK-STAT pathway. The provided examples not include drugs that target this pathway, so direct evidence is unavailable. The outcomes for other kinase inhibitors are inconsistent:
Perturbation with c-Kit-IN-1 (affecting MAPK and PI3K pathways) and Afatinib (affecting ErbB signaling) resulted differential expression of FTH1.
Perturbation with Vemurafenib (affecting MAPK/ERK), AZD1390 190 (affecting DNA damage response), and Regorafenib (affecting multiple pathways including RAF) result differential expression FTH1. This indicates that inhibition some, but not all, kinase signaling pathways of FTH1.
differential expression
Which of the observed genes of interest are most similar to FTH1 (if any, including FTH1 itself)? The only observed gene interest pravided examples FTH1 itself.
What perturbations of upstream pathways genes result in differential expression of FTH1 similar genes any)? Justify your answer with the observed outcomes.
Based on the examples, differential expression of FTH1 results from perturbations of several distinct pathways: Kinase Signaling: Inhibition of c-Kit (Example 1: c-Kit-IN-1 1) and ErbB family kinases (Example 6: Afatinib). KRAS Signaling: Inhibition of the SOS1-KRAS interaction (Example 7: BI-3406). This is particularly relevant as Hs766T cell has KRAS mutation.
AntioxidanU/Srres Response: Modulation the NRF2 pathway (Example 3: BI-78D3), which directly regulates genes involved in oxidative stress, including FTH1.
Metabolic Stress/lon Homeostasis: Chelation of metal cations (Example 5: Ciclopirox), which directly relates to FTH1's function in iron storage.
Epigenetic Regulation: Inhibition of BET bromodomain proteins (Example 2: AZD2858), which affects broad transcriptional programs.
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Published as a conference paper at ICLR 2026
Is a perturbation of Tofacitinib in FTH1 likely to result in differential expression of FTH1? For example, 2) and 4) are unrelated or only indirectly related, it unlikely we will observe differential expression. other hand, 2) and 4) significantly overlap specific genes or pathways, we may observe differential expression.
The pathways impacted by Tofacitinib (JAK-STAT, inflammation, cylokine signaling) are highly related to pathways shown to regulate FTH1 (stress response, KRAS signaling). There is well-established biological link between inflammation, cytokine signaling (mediated by JAK-STAT), and the regulation of iron homeostasis, where FTH1 a key player. Furthermore, the context of KRAS-mulated pancreatic cancer cells like Hs766T, ancagenic KRAS pathway known drive infiammatory cytokine production, creating dependency JAK-STAT signaling. Since the examples show that inhibiting KRAS signaling directly results differential expression of FTH1 (Example 7), it highly probable that inhibiting the downstream JAK-STAT pathway with Tofacitinib will also result in differential expression of FTH1.
Yes. Perturbation of Tofacitinib results in differential expression FTH1.
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Published as a conference paper at ICLR 2026
Prompt
You are an expert molecular biologist who studies how drug affect cellular reaction using Perturb-seq,
Task:
You writing brief overview of the small molecule drug((pert)), with focus MoA and Targets. You will be provided a set of database entries about the drug. Ensure that your overview remains faithful to this domain knowledge.
Format:
Write one sentences describing primary target ((pert)).
Write sentence describing the potential downstream impact of perturbing drug ((pert)..
Constraints:
Maintain professional tone throughout.
not comment on your own writing.
add any notes or references. Do make up additional information.
discuss the importance impact of the gene. Focus only its function.
Domain knowledge:
((entries))
Brief overview of drug ((pert)):
Prompt pt
You are expert molecular biologist who studies how are related using Perturb-seq.
You are writing brief overview the human gene ((name)), with focus on its molecular and cellular functions. You will be provided with set database entries about the gene. Ensure that your overview falthful domain knowledge.
Task:
Format:
Write one to two sentences describing the primary molecular and cellular function of gene ([name)). Write one sentence describing what types of perturbations might impact the expression of gene ((name)). For example, you might consider pathways that are upstream of the gene or compensatory mechanisms.
Constraints:
Maintain professional tone throughout.
Do not comment on own writing.
Do not add any notes or references. Do not make additional information.
Do not discuss the importance or impact of the gene. Focus only on its function.
Domain knowledge:
((entries])
Brief overview of gene ((name)):
28

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Published as a conference paper at ICLR 2026
Prompt
You are an expert molecular biologist who studies how drug affect the cellular reactions using Perturb-seq.
Task: are writing overview the small molecule drug((pert)), with focus on downstream effects perturbing ((name)).
Inputs:
You are provided
Description of perturbing drug ((pert))
Database entries relating ((pert)) to other drugs or targets.
Format:
White five sentences describing the molecular and cellular impact perturbing drug((pert)..
Constraints:
Omit the importance or impact gene. Focus only on its function.
all non-specific information obvious statements, e.g. "this gene
cellular processes:
Remain faithful domain knowledge. Maintain professional tone throughout. Do not comment your writing. Do not add notes references.
up additional information.
Description of drug ((pert)): ((summary))
Relations to other drugs/targets:
((entries))
Downstream effects of perturbing drug ((pert)):
Prompt pt
You are an expert molecular blologist who studies how genes are related using Perturb-seq.
Task: You are writing brief overview human gene ((name)), with focus an molecular and cellular perturbations that may affect the levels gene ((name]). For example, you might consider pathways that are upstream of the gene compensatory mechanisms.
Inputs: You are provided
Description gene of interest ((name))
Database entries relating ((name)) to other genes or pathways
Format:
Write five sentences describing potential molecular and cellular perturbations that may impact the levels of ([name)).
Constraints:
Remain faithful domain knowledge. make up additional information.
Summarize common aspects succinctly, point notable differences within these of genes.
Maintain professional tone throughout. not comment your writing. Do not add any notes references.
Omit the importance or impact of the gene. Focus only on its function. Omit all non-specific information and obvious statements, e.g. "this gene is involved cellular processes."
Description of gene ([name)): ((summary))
Relations to other genes:
((entries))
Perturbations that may affect the levels of ((name)):
29


Reviews:
[{"review_id": "zG4lLyoMlf", "reviewer": "Reviewer_5meR", "summary": "The paper tackles virtual cell modeling: predicting how single cells change gene expression under perturbations (primarily small‑molecule drugs). The authors argue that current end‑to‑end neural approaches are data‑hungry, generalize poorly to unseen perturbations, and provide limited mechanistic interpretability. CWorld is positioned as a white‑box “biological world model” that integrates (i) a curated, heterogeneous biological knowledge graph (KG) built from sources such as PubChem, DrugBank, UniProt, GO, Reactome, STRING, and CORUM; (ii) LLM‑generated, textual node features from local KG neighborhoods; (iii) a hybrid retrieval scheme that scores semantic similarity (cosine on LLM descriptions) and structural similarity (KG path‑based) to construct analogue/contrast evidence sets; and (iv) Chain‑of‑Thought (CoT) prompting to synthesize a stepwise explanation and a binary prediction (DE: differentially expressed or not; DIR: up vs down for DE genes).", "questions": "1. What is the exact structural similarity function and neighborhood depth for KG paths? How sensitive are results to these choices?  \n\n2. For DIR labels, what thresholds define up vs down and how are ties handled? Please report DIR P/R/F1.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well‑motivated by the need for mechanistic, inspectable predictions. The pipeline provides explicit reasoning steps and an evidence set (analogue vs contrast) that biologists can audit\n\n2. Gene‑centric reformulation. Moving from whole‑profile regression to (c,p,g) classification reduces sparsity and makes evaluation at the gene level more straightforward. The label generation process is transparent.\n\n3. Combining LLM‑semantic similarity with KG path‑based similarity is sensible and well aligned with biological priors.\n\n4. Quantitative improvements beyond accuracy. The analysis of precision/recall/F1 and predicted DEG counts shows that VCWorld is less over‑ or under‑confident than generative baselines that either inflate or deflate DEGs.\n\n5. The rule‑based verbalization is a thoughtful way to reduce hallucinations and standardize facts presented to the LLM.", "weaknesses": "1. Converting continuous predictions (STATE/scVI/CPA) to DE/DIR via Wilcoxon on predicted profiles is one way to compare, but it can bias against models trained/optimized for regression‑style metrics. Please add threshold‑free metrics (per‑gene AUROC/AP, balanced accuracy) and calibration to avoid dependence on one downstream test. (Table 1/2 focus on Accuracy and F1 for DE only; DIR lacks P/R/F1.)  \n\n2. Reproducibility and dependence on a proprietary LLM. The core gains hinge on Gemini 2.5‑Flash; the Llama‑3 variant performs much worse. There is no open‑weights replication (e.g., Qwen, DeepSeek‑R1) to show that the method (KG+retrieval+CoT) is the driver rather than the closed model. Also, hyperparameters for retrieval (α, k_a, k_c), prompt templates, temperature/seeds, and cost/latency are not fully specified. \n\n3. Accuracy can be misleading given heavy class imbalance (Table 4); per‑drug/per‑gene AUROC, AUPRC, macro/micro averages, and confusion‑matrix analyses would strengthen claims. DIR evaluation is especially thin beyond Accuracy.  \n\n4. Retrieval is said to use only the training set, but the analogue/contrast selection and KG‑based similarity could still exploit signals close to test perturbations if drug families or near‑duplicates exist; an explicit “zero‑shot by drug” and “zero‑shot by target/pathway” protocol would clarify generalization.", "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": "qWLkAOirW1", "reviewer": "Reviewer_Vroo", "summary": "This paper introduces VCWorld, a novel computational framework for predicting cellular responses to drug perturbations. First, VCWorld reframes the prediction task, moving from a high-dimensional regression problem (predicting exact gene levels) to a gene-level binary classification task. To generate a prediction, the model retrieves relevant information from the constructed KG, along with relevant experiments from its training data, and then feeds the information to the LLM, which generates a step-by-step CoT to support its final answer.", "questions": "1. I think state does not use only 5 cell lines in tahoe, but it is trained instead on entire 50. Please clarify the setup to ensure fairness of comparison.\n\n2.The model uses a hybrid similarity (part text, part structure) to find similar experiments. Why is this complex metric necessary, and what is its impact compared to a simpler, purely semantic retrieval?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The idea of generating a CoT rationale to provides a human-readable, verifiable hypothesis for the model prediction is original;\n2. The paper introduces GeneTAK, a novel benchmark that simplifies the prediction proble;\n3. By leveraging a pre-existing KG, it can reason about new drugs or genes as long as they are present in its knowledge base.", "weaknesses": "1. The gene-centric, one-at-a-time prediction model raises scalability concerns. This formulation also ignores the cell's pre-perturbation state and treats genes as independent entities, which is not biologically accurate.\n2. The KG's construction is described at a high level. It is not explained if the resulting graph consists of a single connected network or many disconnected pieces, which impacts the \"graph-based structural similarity\" metric.\n3. The model works by retrieving similar past experiments to guide its reasoning. It is not explained why this was this chosen over a more direct Graph-RAG approach that would retrieve causal paths directly from the KG to explain the (drug, gene) relationship.\n4. Finally, the paper presents its generated reasoning (the CoT) as evidence of interpretability. However, a generated explanation is not a guaranteed, faithful transcript of the model's actual internal process. Therefore, **reasoning and interpretability should not be treated as interchangeable**.", "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": "hRNrnfbSOa", "reviewer": "Reviewer_inaw", "summary": "This paper introduces VCWorld, a system for predicting gene expression changes following drug perturbations in cells. The authors reformulate the traditional cell-level regression problem into a gene-centric binary classification framework addressing two tasks: differential expression detection (DE) and directional change prediction (DIR). VCWorld integrates a biological knowledge graph built from seven databases with LLM-based retrieval and chain-of-thought (CoT) reasoning using Gemini 2.5 Flash to produce mechanistic explanations. A new benchmark, GeneTAK, derived from Tahoe-100M, includes five cell lines and 348 drug perturbations with a 30:70 train-test split by perturbation. VCWorld achieves an average accuracy of 0.68 on both tasks, outperforming existing deep learning baselines: STATE (0.30–0.51), scVI (0.42–0.61), and CPA (0.23–0.44).\n\nThe experimental methodology shows both strengths and weaknesses. Positively, the work uses appropriate statistical testing (Wilcoxon signed-rank), clear dataset construction, extensive ablations, and transparent reporting. However, several issues limit validity. The term “world model” is used incorrectly; in established RL literature (Ha & Schmidhuber 2018; Hafner et al. 2020), world models learn temporal dynamics, which VCWorld does not. Key baselines are missing: no graph neural networks (GCN, GAT, GraphSAGE) on the same knowledge graph, no classical ML using graph features, no simpler retrieval pipeline without LLM reasoning. Consequently, it is unclear whether performance gains stem from graph structure, LLM reasoning, or both. While the ablations are informative, the large performance gap between Gemini 2.5 Flash and Llama3-8B (0.68 to 0.37 DE accuracy) without cost or compute analysis raises reproducibility concerns. Finally, the “white-box” interpretability claim is unvalidated; the paper includes no quantitative audit verifying that reasoning traces correspond to real biological mechanisms.\n\nThe paper is well-organized and clearly written, with strong visual presentation (pipeline diagram and tables). The GeneTAK dataset and labeling procedures are described in adequate detail, and the appendices provide useful prompt examples. Nonetheless, the “world model” framing is misleading and should be replaced with a more accurate term such as knowledge-grounded retrieval-augmented classification system. The novelty is somewhat overstated: VCWorld is essentially retrieval-augmented generation (RAG) applied to biological perturbation prediction, combining known components (knowledge graph integration, hybrid retrieval, CoT reasoning) rather than introducing a new algorithm. Discussion of existing RAG literature could be expanded, and the paper should include clearer descriptions of knowledge-graph maintenance, conflict resolution, and update policies.\n\nEmpirically, the paper contributes valuable resources and results; methodologically, it is modest. Strengths include the GeneTAK benchmark, the gene-centric reformulation improving data efficiency, strong empirical performance demonstrating that knowledge-grounded reasoning outperforms purely data-driven models, and interpretable natural-language reasoning outputs. However, the architecture primarily reuses established components: graph construction, hybrid semantic-structural retrieval, contrastive case retrieval, and CoT prompting (Wei et al. 2022). There are no fundamentally new algorithms or theoretical insights. The system’s dependence on proprietary Gemini 2.5 Flash, with much weaker results for Llama3-8B (0.37 DE, 0.56 DIR) limits reproducibility and accessibility. Without graph-based or classical ML baselines, it remains unclear whether LLM reasoning is necessary or whether the knowledge graph alone drives performance improvements.", "questions": "1.\tCan you provide GNN baselines (GCN, GAT, GraphSAGE) using your knowledge graph?\n2.\tHow does performance compare when using smaller fine-tuned models?\n3.\tHave you validated mechanistic reasoning against literature, what fraction of explanations are biologically correct?\n4.\tWhat are the computational and API costs per query relative to baselines?\n5.\tCan you ablate the LLM entirely (graph-based k-NN or scoring) to test the necessity of language reasoning?\n6.\tHow does accuracy vary with retrieval-set size or when retrieving only positive/negative examples?\n7.\tProvide failure analysis by perturbation and gene coverage; how are out-of-graph entities handled?\n8.\tReplace the “world model” terminology with a more precise descriptor. Major issue which needs to be addressed for acceptance.\nThis paper offers solid empirical advances, especially the GeneTAK benchmark and strong results showing knowledge-grounded reasoning can outperform data-hungry deep learning. However, several weaknesses must be addressed: misleading terminology, incomplete baselines (no GNNs or retrieval-only models), overstated novelty, lack of cost analysis, and unvalidated interpretability claims. With revisions adding graph-based and classical baselines, testing open LLMs, validating reasoning quality, reporting costs, and reframing the contribution as a knowledge-grounded retrieval-augmented classifier, the work would merit acceptance.\nI will consider raising it to 5 if authors revise the misleading world model framing and perform GNN baseline tests. I would further raise it to 6 (accept) if authors perform cost analysis and address interpretability claims.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1.\tAddresses an important problem in computational biology with strong empirical performance: VCWorld reaches 0.68 accuracy on both DE and DIR, while existing models perform near chance on DIR.\n\n2.\tGeneTAK provides a useful community benchmark with rigorous construction and consistent evaluation protocols.\n\n3.\tGene-centric formulation effectively mitigates data sparsity and improves interpretability.\n\n4.\tProvides mechanistic textual explanations alongside predictions, improving transparency over black-box deep models.\n\n5.\tThorough ablation studies demonstrate component importance: removing BioContext drops accuracy to ~0.51, removing CoT to ~0.59, and replacing Gemini 2.5 with Llama3-8B to ~0.37 (DE).\n\n6.\tIntegrates structured biological knowledge from seven authoritative databases in a coherent, data-efficient system.", "weaknesses": "1.\tVCWorld does not learn temporal dynamics or simulate cellular trajectories and this misframing creates false expectations about the system's capabilities. Please revise throughout.\n\n2.\tMissing critical baselines: no GNNs, traditional ML with graph features, or retrieval-only ablations.\n\n3.\tOverstated methodological novelty: essentially standard RAG adapted to a biological context.\n\n4.\tInterpretability claims unvalidated: no systematic check that explanations align with known biology.\n\n5.\tMissing analyses: computational cost, statistical significance, error analysis by perturbation type, and retrieval-parameter sensitivity.\n\n6.\tUnusual 30:70 train-test split is only lightly justified.\n\n7.\tHeavy dependence on Gemini 2.5 Flash without open alternatives demonstrated limits reproducibility and accessibility for most researchers.", "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": "Xh3iwdft3j", "reviewer": "Reviewer_rJsH", "summary": "VCWorld is a framework for simulating cellular perturbation responses, integrating a structured biological knowledge graph with the reasoning of Large Language Models (LLMs). Using a Chain-of-Thought (CoT) process, it generates step-by-step, interpretable predictions. The paper also introduces GeneTAK, a benchmark curated from the Tahoe-100M dataset, and claims state-of-the-art, interpretable performance.", "questions": "See weaknesses.", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The design, which combines a knowledge graph with LLM reasoning, seems a novel approach that improves LLM performance on perturbation prediction.", "weaknesses": "- STATE has a particularly low performance, which contradicts the reported performance in [1]. The reason remains unclear and can arise from suboptimal implementation or insufficient training data. This should be better clarified in the work.\n- The contribution is unclear. GeneTAK should not be emphasized as a contribution since it is just reorganizing an existing database. Current claims exaggerate the contribution of the work.\n- It is unclear how the results compare to well-established benchmarks in [1-2].\n- Importantly, additional results with modern reasoning language models should be presented, such as the results with more widely used LLMs. From the current results, the real usefulness of VCWorld remains unclear; maybe simply using the latest generation of LLMs will remove the need of the knowledge base.\n\n[1] Adduri, Abhinav K., et al. \"Predicting cellular responses to perturbation across diverse contexts with State.\" bioRxiv(2025): 2025-06.\n[2] Ahlmann-Eltze, Constantin, Wolfgang Huber, and Simon Anders. \"Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines.\" Nature Methods (2025): 1-5.", "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)