Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution
Authors: Leon Hetzel
Simon Boehm
Niki Kilbertus
Stephan Günnemann
Mohammad Lotfollahi
Fabian J Theis
Venue: NeurIPS 2022 Accept (2022)
Keywords: single cell
perturbation
disentanglement
transfer learning
drug discovery
unsupervised
genomics

Abstract: Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells. However, scaling high-throughput screens (HTSs) to measure cellular responses for many drugs remains a challenge due to technical limitations and, more importantly, the cost of such multiplexed experiments. Thus, transferring information from routinely performed bulk RNA HTS is required to enrich single-cell data meaningfully.
We introduce chemCPA, a new encoder-decoder architecture to study the perturbational effects of unseen drugs. We combine the model with an architecture surgery for transfer learning and demonstrate how training on existing bulk RNA HTS datasets can improve generalisation performance. Better generalisation reduces the need for extensive and costly screens at single-cell resolution. 
We envision that our proposed method will facilitate more efficient experiment designs through its ability to generate in-silico hypotheses, ultimately accelerating drug discovery.

Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution
Leon Hetzel Simon Böhm" Niki Kilbertus Stephan Günnemann', Mohammad Lotfollahi and Fabian Theis
(leon. hetzel, simon boehm, niki hailberttss@@ielmmoll-m--eeeee s guennemann@tum de, imohammad.1otfollaan fabian. tabian.theis)
Department of Mathematics, Technical University of Munich DDepartment Computer Science, Technical University of Munich Helmholtz Center for Computational Health, Munich 'Helmholtz AI, Munich
Wellcome Sanger Institute, Cambridge
Abstract
Single-cell transcriptomics enabled the study of cellular heterogeneity response to perturbations at the resolution of individual cells. However, scaling high- throughput screens (HTSs) to measure cellular responses for many drugs remains a challenge due to technical limitations and, more importantly, the cost of such multiplexed experiments. Thus, transferring information from routinely performed bulk RNA HTS is required to enrich single-cell data meaningfully. We introduce chemCPA, a new encoder-decoder architecture to study the perturbational effects of unseen drugs. We combine the model with architecture surgery for transfer learning and demonstrate how training on existing bulk RNA HTS datasets can improve generalisation performance. Better generalisation reduces the need for extensive and costly screens at single-cell resolution. We envision that our pro- posed method will facilitate more efficient experiment designs through its ability to generate in-silico hypotheses, ultimately accelerating drug discovery.
Introduction
Recent advances in single-cell methods allowed the simultaneous analysis millions cells, increasing depth and resolution to explore cellular heterogeneity (Sikkema et al., 2022; Han et al., 2020). With single-cell RNA sequencing (scRNA-seq) and high-throughput screens (HTSS) one can now study the impact of different perturbations, i.e., drug-dosage combinations, the transcriptome at cellular resolution (Yofe et al., 2020; Norman et al., 2019). Unlike conventional HTSs, ScRNA- seq HTSS can identify subtle changes in gene expression and cellular heterogeneity, constituting cornerstone for pharmaceutics and drug discovery (Srivatsan et al., 2020), Nevertheless, these newly introduced sample multiplexing techniques (McGinnis al., 2019; Stoeckius et al., 2018; Gehring et al., 201 8) require expensive library preparation and do not scale to screen thousands of distinct molecules. Even in its most cost-effective version, nuclear hashing, the acquired datasets contain no more than 200 different drugs (Srivatsan et al., 2020).
Consequently, computational methods required address the limited exploration power of existing experimental methods and discover promising therapeutic drug candidates. Suitable methods
"qqual contribution
Code available at giltah..om/ttessaaahcheeCCCCC.
36th Conference on Neural Information Processing Systems (NcurIPS 2022).

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should predict the response to unobserved (combinations of) perturbations. Increasing in difficulty, such tasks may include inter- and extrapolation of dosage values, the generalisation to unobserved (combinations of) drug-covariates (e.g., cell-type), or predictions for unseen drugs. In terms of medical impact, the prediction of unobserved perturbations may be the most desirable, for example for drug repurposing. At the same time, it requires the model to properly capture complex chemical interactions within multiple distinct cellular contexts. Such generalisation capabilities can not yet be learned from single-cell HTSs alone, as they supposedly do not cover the required breadth of chemical interactions. In this work, we leverage information across datasets to alleviate this issue.
We propose a new model that generalises previous work on Fader Networks by Lample et al. (2017) and the Compositional Perturbation Autoencoder (CPA) by Lotfollahi et al. (2021) to the challenging scenario of generating counterfactual predictions for unseen compounds. Our method is as flexible and interpretable as CPA but further enables us to leverage lower resolution but higher throughput assays, such as bulk RNA HTSs, to improve the model's generalisation performance on single-cell data (Amodio et al., 2021). Our main contributions are:
1. We introduce chemCPA, a model that incorporates knowledge about the compounds' struc- ture, enabling the prediction of drug perturbations at a single-cell level from molecular representations.
2. We propose and evaluate transfer learning scheme to leverage HTS bulk RNA-seq data in the setting of both identical and different gene sets between the source (bulk) and target (single-cell) datasets.
3. We show how chemCPA outperforms existing methods on the task of predicting unobserved drug-covariate combinations. At the same time, we demonstrate chemCPA's versatility and evaluate chemCPA on generalisation tasks that cannot be modeled using any previously existing method.
2 Related Work
Over the past years, deep learning (DL) has become an essential tool for the analysis and interpretation
of scRNA-seq data (Angerer et al., 2017; Rybakov et al., 2020; Lopez et al., 2020; Hetzel et al., 2021). Representation learning in particular, has been useful not only for identifying cellular heterogeneity and integration (Gayoso et al., 2022), or mapping query to reference datasets (Lotfollahi et al., 2022), but also in the context of modelling single-cell perturbation responses (Rampášek et al., 2019; Seninge et al., 2021; Lotfollahi et al., 2019; Ji et al., 2021).
Unlike linear models (Dixit et al., 2016; Kamimoto al., 2020) or mechanistic approaches (Fröhlich et al., 2018; Yuan et al., 2021), DL is suited to capture non-linear cell-type-specific responses and easily scales to genome- wide measurements. Recently, Lotfollahi et al. (2021) introduced the CPA method for modelling perturbations on scRNA-seq data. CPA does not generalise to unseen compounds, hindering its application to virtual screening of drugs not yet measured via scRNA-sey data, which is required for effective drug discovery.
For bulk RNA data, on the other hand, several methods have been proposed to predict gene expression profiles for de novo chemicals (Pham et al., 2021; Zhu et al., 2021; Umarov et al., 2021). Crucially, the L.1000 dataset, introduced by the LINCS programme (Subramanian et al., 2017), greatly facilitated such advances on phenotype- based compound screening. However, it remains unclear how to translate these approaches to single-cell datasets that include significantly fewer compounds and, in many cases, rely on different gene sets.
3 Chemical Compositional Perturbation Autoencoder
We consider dataset D {Z,,, vo), {z,, (d,, Si; C; c))] where I; E R describes the nd-imensional gene expression and Yi an attribute set. For ScRNA-seq perturbation data, we usually consider the drug and dosage attributes, d, {drugs in D) and s; E R. respectively, and the cell-line c, of cell i. Note that this set of attributes y depends on the available data and could be extended to covariates such as patient, or species.
possible approach to predicting counterfactual combinations is to encode a cell's gene expression I invariantly from its attributes y, latent vector 2,, called the basal state. Being provided such

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(1) Encoder- Decoder::
(2) Attribute embeddings:
(3) Adversarial classifiers:
Cell lines
Fassbiacstat
A
basal
A
drugs
Eanophitol
Ee
D R
Perturbation network Ps
NCFF
basal
A
144 14
M
X
t0 8
K362
2d4
8
S
24
2d
gi
hdrug 85
Figure 1: Architecture of chemCPA. The model consists of three parts: (1) the encoder-decoder architecure, (2) the attribute embeddings, and (3) the adversarial classifiers. The molecule encoder G can be any graph- or language-basee The MLPs S and M trained map the embeddings the perturbational latent space. There, Ed, added to the basal state 2; and the covariate embedding 2c,- In this work, the latter always corresponds to cell lines. The basal state 2; (x;); is trained to be invariant through adversarial classifiers A and the decoder D& gives rise the Gaussian likelihood Max; Ms o,).
model as long as it generates fixed-sized embeddings ftdrugs-
disentangled representations, 2, can be combined with attributes, 2d, and že, encode any attribute combination v & V/, and decoded back to gene expression state 2; that corresponds to this new set of chosen attributes.
To this end, we divide our model, the Chemical Compositional Perturbation Autoencoder (chemCPA), into three parts: (1) the gene expression encoder and decoder, (2) the attribute embedders, and (3) the adversarial classifiers, see Figure for an illustration.
3.1 Gene encoder and decoder
Following Lotfollahi et al. (2021), our model is based encoder-decoder architecture combined with adversarial training. The encoding network Eş I= - IR' is a multi-layer perceptron (MLP) with parameters 9 that maps measured gene expression state 2, R² to its (-dimensional latent vector 2; = Eg (.r)) Through adversarial classifiers, 2; is trained to not contain any information about its attributes ye- This gives control over the latent space in which update 2; with additive attribute embedding of our choice and obtain 2//-
The decoder D R + 2e22 is an MLP that takes 2/ as input and computes the component wise parameters of the underlying distribution IP of the gene expression data. Dependent on whether 2; is raw or pre-processed, I can follow a negative binomial or Gaussian distribution. Assuming a mean and variance parametrisation, we get both cases μ (2") and o² = D (z') for the description of the decoded gene expression state. While chemCPA supports both settings, we observed better convergence with Gaussian likelihood for which the reconstruction loss becomes:
(D - x3)
(0, N pi o4) - In (D (z))
with z' EE(x)) Zartribute
Do (+))
Next, we provide intuition about how can meaningfully interpret latent space arithmetics and how we encode drug and cell-line attributes.
3.2 Attribute embedding and additive latent space
We assume an additive structure of the perturbation response in the latent space:
2; + Zatrrbute 2; + Ze, + 8;2d,
where Ze, and zd. correspond to the latent cell-line and drug attributes, and &; encodes the dosage.
3

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This choice of linearity makes the model interpretable for users such as biologists since it permits to analyse and ablate components individually, e.g.. allowing interpolation or extrapolation of the dose values. Another advantage of the additive structure is its permutation invariance and that it allows for adding new covariates, e.g., during fine-tuning. While remaining interpretable, chemCPA is able to model complex relationships through the non-linear decoder.
Due to their different nature, we encode the drug and cell-line attributes separately in the latent space. For the cell-lines, we use the same approach as Lotfollahi et al. (2021), where a (-dimensional latent representation 2e is optimised for each cell-line c. For the drugs, we propose new embedding network P..
Perturbation Network The network P. maps molecular representations such as its graph or SMILES representation and the used dosage to its latent perturbation state. This perturbation network P. consists of the molecule and perturbation encoders, G and M, well as the dosage scaler S, see Figure (2).
The molecule encoder G - Rmmh encodes the molecule representation gi E G as a fixed size embedding h4; E RT. In a subsequent step, the perturbation encoder M : IIm - IK' takes the molecular embedding ha, as input and generates the drug perturbation 2d, RR that used in chemCPA's latent space.
The dosage scaler S Im++1 - R also uses ha, and maps it together with the dosage 8; to the scaled dosage value 83. We chose S to map back to a scalar value as this allows us to compute drug-res resgores curves in easy fashion. In addition, this way of encoding matches the idea that 2d, encodes the drug's 'sugs general effect, which is dosage independent. Put together, we end up with
$ 2d, (g s;) = S(ha, s:) x M (ha,) with hd, = G(g.)
The molecule encoder G can be any encoding network that maps molecular representations to fixed-size embeddings. Due to the limited number of drugs available in scRNA-seq HTSs, propose to rely on a pretrained encoding model and freeze G' during training. We tested multiple different options for G and include detailed benchmark in the Appendix A.1. We found that RDKit features performed well in our setting and report all following results for chemCPA with RDKit as the molecule encoder G. By design, can choose a new set of attributes for the drug and cell line and compute the new latent state as 7 = Σ₃ + Zartribute at test time. Due to the perturbation predict drug perturbations for molecules that have not been
network, chemCPA makes it possible experimentally observed (d & DD. In contrast, CPA can only make predictions for molecules that were present during training (d E D). In both cases, the latent representation is computed as:
z = 23 + Fatrrbbuee 2; + &cN + 8;7d,
We next describe how we "strip z, from its attribute information" to obtain a basal state representation.
3.3 Adversarial classifiers for invariant basal states
To generate invariant basal states and produce disentangled representations 21, 2d, and Zeg we use adversarial classifiers A and g Both adversary networks A/, RR' - RR take the latent basal state 2; as input and aim to predict the drug that has been applied to example as well as trained to improve classification performance, we also
its cell-line e,. While these classifiers add the classification loss with a reversed sign to the training objective for the encoder Fgg. Hence, the encoder attempts produce latent representation 2; which contains information about the attributes. Note that this explicit separation of basal, drug, and covariate information, which we call disentanglement, is an approximation to make the problem tractable. At the same time, such separation is useful for attributing perturbation effects to specific sources, e.g., drug or cell line, which is relevant for biological applications and downstream analyses.
We use the cross-entropy loss for both classifiers
drugs
CE( A (=:)), de) and C CE AA (z),,
Following the CPA implementation from Lotfollahi et al. (2021), 1), we add a zero-centered gradient penalty to the loss function of the adversarial classifiers, to minimise
1 Σ | (2)))))
1
pen
k:
k

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This gradient penalty was shown to make the discriminator more robust to noise and enable local convergence, when applied to generative adversarial networks (Mescheder et al., 2018). During training, we alternate update steps between the following competing objectives
(A v;, vl = Lree 4) Adis Σ Cassss [0| () and
Ladv (Ø1 (0) - Caass (010) + Apen (pn
where Àdis balances the importance of good reconstruction against the encoder Eg constraint generate disentangled basal states 2;. The gradient penalty is weigthed with Xpen-
Datasets and transfer learning
We use the sci-Plex3 (Srivatsan et al., 2020) and the L1000 (Subramanian et al., 2017) datasets for the main evaluation on single-cell data and pretraining on bulk experiments. respectively.
Datasets The L1000 data contains about 1.3 million bulk RNA observations for 978 different genes. includes measurements for almost 20k different drugs, some of which are FDA-approved, while others are synthetic compounds with no proven effect on any disease. Compared to scRNA-seq data, the L1000 data allows explore a more diverse space of molecules which makes ideal for pretraining.
The sci-Plex3 data is similar in size and contains measurements for 649,340 cells across 7561 drug- sensitive genes. On three human cancer cell A549, MCF7, and K562 sing--ssinll-coopuund perturbations for 188 drugs at four different dosages- 10 nM, 100 nM, μM, and 10 μM- are examined. Note that all cell lines and about 150 compounds overlap with the L1000 data. In addition, Srivatsan et al. (2020) assigned all compounds one of 19 different modes of action (MoA), also called pathways. In contrast to the mechanism of action, which is related to the biochemical interaction between a molecule and cell, the MoA describes the anatomical change that results from the exposure of cells to drug-like molecule.
Transfer learning As we train chemCPA with Gaussian likelihood loss, the dataset was first normalised and then log(x + 1)-transformed. Depending on the experiment, we further reduced the number of genes included in the single-cell data. In Section 5.1, we first subsetted both datasets to the same 977 genes which were identified via ensemble gene annotations. For the final experiment in Section 5.2, the considered gene set increased as we hypothesize that more than the 977 L1000 genes are required to capture the variability within the single-cell data. To assess whether pretraining on L1000 still beneficial in this scenario, we included 1023 highly variable genes (HVGs) from the sci-Plex3 data. That is, we consider 2000 genes in total.
For the extended gene set, chemCPA's input and output dimensions have to be adjusted to match the total number of 2000 genes. This is realized by adding two non-linear layers hene Titeeee - R and fade: : IR 20puraaa + 2 to the autoencoder. The encoder becomes Ég = Eg (he (z)) and the decoder becomes D. haee (z')) In our example, we have Pprretriin 977 and Minne-tune 2000. We train all layers during fine-tuning, including the newly added ones. This architecture surgery differs from the procedure introduced Lotfollahi et al. (2022), where individual neurons are added (instead of whole layers) and the transfer is performed on dataset labels (instead of gene sets).
5
Experiments
Our evaluation strategy tests chemCPA's ability to produce counterfactual predictions. To this end, it important to measure both the predictive performance of a trained model as well as the degree to which the latent space components are disentangled.
Counterfactual predictions To perform a counterfactual prediction, chemCPA first encodes an unperturbed control observations, a cell treated with dimethyl sulfoxide. The resulting basal state is then combined with the encoding of the desired drug and cell line, and chemCPA decodes the result. As are free to choose any drug encoding, we refer to this process counterfactual prediction.
5

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Table 1: Comparison of multiple models on their performance on generalisation unseen drug-
covariate combinations for dosage values of μM and 10 μM.
Dose
Model
E[r²] all
E[r²]
DEGs
Median r² all
Median r DEGs
Baseline
0.69
0.51
0.82
0.62
scGen
0.73
0.59
0.77
0.68
μM
CPA
0.72
0.54
0.86
0.67
chemCPA
0.74
0.60
0.86
0.66
chemCPA pretrained
0.77
0.68
0.85
0.76
Baseline
0.50
0.29
0.48
0.12
scGen
0,62
0.47
0.66
0,49
10 μM
CPA
0.54
0.34
0.52
0.26
chemCPA
0.71
0.58
0.77
0.64
chemCPA pretrained
0.76
0.68
0.82
0.79
Evaluation strategy Throughout our experiments, we use the coefficient of determination r² as the main performance metric. This score is computed between the actual measurements and the counterfactual predictions on all genes and the 50 most differentially expressed genes (DEGs). It necessary to consider all genes to evaluate the background and general decoder performance. However, the resulting -cores can get inflated since most genes stay similar their controls under perturbation. contrast, the DEGs capture the differential signal which reflects drug' 's effect. To further stress the importance to report both scores, note that the DEGs are unknown for unseen drugs as they depend on the drug and cell type. Hence, the combination is essential to gauge the accuracy of the model's 's predictions.
In order to classify the degree of disentanglement during evaluation, we train separate MLPs with four layers over 400 epochs and compute the prediction accuracy for drugs and covariates given the basal state. We consider the resulting accuracies as our disentanglement scores. An optimally disentangled model achieves scores that match the ratios of the most abundant drug and cell line, respectively. Since no model achieves perfect scores, we subset to models that are sufficiently disentangled. Throughout experiments the sci-Plex3 data, we set the thresholds for perturbation and cell line disentanglement to < 10% and < 70%. respectively, while values of 3% and 51% are optimal. Note that poor disentanglement will automatically lead to low scores due computing the test scores the counterfactual predictions.
5.1 Comparing ChemCPA against existing methods unseen drug- Arug-covariate combinations
Before evaluating how well chemCPA can generalise to unseen drugs, we have to establish its com- petitive performance in less ambitious setting. For this, we consider the scenario of generalisation to unobserved combinations of drugs and cell lines on the sci-Plex3 data and compare chemCPA against scGen (Lotfollahi et al., 2019) and CPA (Lotfollahi al., 2021).
As scGen cannot distinguish between different dosage values, we perform two separate experiments for the second and highest dose values, 1 μM and 10 μM, respectively. Moreover, as both CPA and scGen require each individual component (drug d, cell line e) to be part of the training data 2, we create three distinct splits with only two of the three different drug set and cell line combinations being present during training, the third one being left for testing.
We choose to test nine different compounds: Dacinostat, Givinostat, Belinostat, Hesperadin, Quisi- nostat, Alvespimycin, Tanespimycin, TAK-901, and Flavopiridol. These drugs mostly belong to three MoA epigenette regulation, tyrosine kinase signalling, and cell cycle regulation-ain were reported among the most effective drugs in the original publication (Srivatsan et al., 2020).
As discussed in the previous paragraph, we report mean and median p. values, which we averaged over the three splits and all drugs, for the sets of all genes and differentially expressed genes (DEGs). We consider a model that discards all perturbation information one can understand the improvement over the baseline as a result of the additional drug encoding. Moreover, use the L1000 data for the pretraining of chemCPA and subset to the same 977 genes
our baseline. As consequence,

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E(r] on atl genes
1.0
1.0
Nodel type
DEGs
8:8
pretreined ton-pretrained
base ine
[F on
8.4
0.2
0.2
8.0
0.0
0.01
6.1
0.01
0.1
Dosage 1n PM
Dosage in PM
Figure 2: Performance of chemCPA on both the complete gene set (977 genes) and the compound specific DEGs (50 genes). both cases, the pretrained model shows the best performance. 10 pM on the DEGs, more than 50% of the predictions have an r score > 0.6 while the baseline's median below 0.2.
Table 2: Performance of chemCPA on the shared gene set. Since drug effects are stronger for high dosages, we present scores for a dosage value 10 μM.
Model
E[r2] all
E[r")
DEGs
Median y² all
Median 22 DEGs
Baseline
0.50
0.29
0.49
0.12
chemCPA
0.51
0.32
0.47
0.24
chemCPA pretrained
0.68
0.54
0.75
0.64
for the fine-tuning on sciPlex-3. This way, we are able to evaluate chemCPA in its pretrained and non-pretrained version.
We report the results of this experiment in Table 1. ChemCPA outperforms both scGen and CPA, demonstrating that the perturbation network together with pretraining leads to SOTA performance. Note also that the base version of chemCPA performs better than both CPA and scGen, indicating that the additional regularisation that comes from the perturbation networks P. has beneficial effects on single-cell perturbation modelling.
To make fair comparison, optimised all CPA and chemCPA models identically and swept over the same set of hyperparameters (random, 10 samples). seGen was optimised with default parameters and an adjusted KL annealing scheme match the set number epochs. Note that both CPA and chemCPA can take control cells z, from all cell lines as input ({A549, K562, MCF7) + (A549, K562, MCF7)), while the cell line input for scGen has to match the test set (A549 + A549, K562 + K562, MCF7 - MCF7).
5.2 Using ChemCPA to predict single-cell responses for unseen drugs
For the application chemCPA to predict perturbation responses for unseen compounds, we use the same nine drugs from sci-Plex3 data as in Section 5.1. In addition to the shared gene set, also consider an extended gene set, ef. Section 4. We include HVGs to account for the technological difference between bulk and single-cell and to capture the variance of single-cell data. To this end, the 977 genes present in both datasets are extended with 1023 HVGs of the sci-Plex3 data. Note that through this larger genes the 50 DEGs become subset of the HVGs which makes it considerably more difficult for pretrained models to leverage learned bulk expressions directly.
Shared gene set Table 2 shows the test performance of chemCPA, averaged over all drugs and the three cell lines, for the same gene set as used in Section 5.1. The pretrained chemCPA model consistently outperform the baseline and its base version.
The high baseline scores Figure shows that the drugs have almost no effect low dosages. At high dosages, however, we see how chemCPA's predictions improve over the baseline. Looking at the prediction for all genes, the pretrained model has significant advantage over its non-pretrained version. As expected, the performance is lower for the DEGs. Nevertheless, also in this scenario,

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Table 3: We show the performance of chemCPA on the extended gene set. Since drug effects are stronger for high dosages, we present scores for dosage value of 10 pM.
Model
E|ra]
all
[|r"]]
DEGs
Median f² all
Median , DEGs
Baseline
0.37
0.19
0.16
0.00
chemCPA
0.46
0.22
0.35
0.00
chemCPA pretrained
0.69
0.47
0.79
0.62
chemCPA, especially the pretrained version, can explain gene expression values that must result from the drugs' influence.
In Figure latent perturbations za visualised. Note that the difference between the lowest and highest doseage values results only from the non-linear dosage scaler S.
Extended gene sets The extension the larger gene set introduces more difficult task for chemCPA. In Table 3, we show the same analysis as for the shared gene set. Again, the advantage of the pretraied chemCPA model translates to this scenario, while the base version only slightly better than the baseline. A more comprehensive view for all dosages is shown in Figure 4, see also A.5 for results with different molecule encoders G.
This a promising result, as it suggests that the transfer from abundant bulk RNA perturba- tion screens can be leveraged even in scenarios where the gene sets do not match. Crucially, this enables users to benefit from the proposed trans- fer learning and chemCPA's modelling capacity while simultaneously accounting for the special requirements of scRNA-seq data. In Figure 5 we show an explicit example for chemCPA's perfor- mance for two histone deacetylation lation drugs, see also Figure 13 in the appendix for more details.
1.8
Dacinostat
DE65
0.8
0.6
0.4
0..2
8.8
1.8
Hesperadin
9.88
9.6
8
0..4
Model. type
Yet, for real applications it is essential to make limitations concerning the data and method transparent. To this end, we propose an uncer- tainty measure that addresses some of the lim- itations related to the generalisation to unseen compounds.
0.2
nen-gnetraiied baseline
0.8
10-2
18-3
109
301
Dosage
in pM
Figure 5: Perturbation prediction for Dacinostat and Hesperadin for chemCPA across all three cell- lines for the shared gene set.
TeK-432
Pathway
Tyrosine Kinase signaling darage repair Cell. cycle regul ation Histone descetylation
ElviNistat
Ravairiml
Avirectat Aall fereti
Mangtride
Aedsliontat
tirostat
Alsesplryci
Alvesplaycia
Uncertainty high low
T Tarmpieyci
UNAP Zu a dosage 10pM
Figure 3: Illustration of the the scaled perturbation embedding s x for 10 μM. The left part illustrates how the perturbation embeddings žu are clustered according to some of the pathways. Most notably, the histone deacetylation drugs show a clear separation. Further context is provided by the uncertainty score on the right, showing regions of high and low confidence for the drug embeddings za. The nine test compounds are labeled.
8

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1.0
1.0
E(r] on atl genes
Nodel type
pretreined
DEGs
non-pre pran-eeetrrddd
base ine
8.4
0.2
8.2
8.0
0.0
0.01
6.1
0.01
0.1 Dosage in PM
Dosage 1n PM
Figure 4: Performance of chemCPA model on the extended gene set, see also Figure 2. The pretrained model shows the best performance with non-pretrained model failing to beat baseline at lower dosages. At 10 μM the DEGs, the pretrained model's median much higher that the baseline.
Table 4: Uncertainty score for all nine unseen drugs, The last column shows the improvement Ar of chemCPA over the baseline. The number of considered neighbours was nine for all drugs.
Drug
Uncertainty score u
Ar DEGs
Dacinostat
0.570
0.00
Givinostat
0.660
0.00
Belinostat
0.623
0.12
Hesperadin
0.197
0,40
Quisinostat
0.052
0.65
Alvespimycin
0.058
0.74
Tanespimycin
0.092
0.75
TAK-901
0.049
0.88
Flavopiridol 0.011
0.99
5.3 Measure uncertainty on the drug embedding
Generalisation can only be achieved within the limits of the dataset model is trained on. For the sci-Plex3 data, less than 20% of the drug-dost dose combinations deviate from the controls' phenotypes by more than 35% in its r² scores. In additon, we know from the original sci-Plex3 publication that only drugs from few pathways-tyrosine kinase signaling, DNA damage and repair, cell cycle regulation, and epigenetic regulation-shor a clear effect. We assume that this technological noise is the reason why the non-pretrained chemCPA version struggles to outperform the baseline on the extended gene set, whereas the pretrained model is more robust. These data challenges are also reflected in the left part Figure as we would expect chemCPA's perturbation latent space to cluster according to the drugs' MoA, similar to the cluster of histone deacetylation drugs.
We found that an imperfect clustering often correlates with high baseline scores and, as a result of that, chemCPA not being able to identify distinct perturbations, see Figure 15 in the appendix. To make the generalisation ability more transparent, we employ measure of uncertainty. good indicator for chemCPA's ability to generalise is the MoA prediction from the KNN-graph of the perturbation embedding space. We further combine this measure with the average distance neighbouring drugs as we recognise that larger distances indicate a distinct perturbation:
U1
Σ JEN.
X H(X)
log (d(i.))
where d is the Euclidean distance, H is the Shannon entropy, and X the normalised pathway prediction deduced from the neighbours N, of drug i. This uncertainty measure combines two things: First, chemCPA's confidence the drug', MoA, measured by H. and second, whether chemCPA expects the drug to have a distinct perturbation effect on the cell, measured by the inverse distance.
We report an analysis of the uncertainty for the chemCPA model in Figure and Table 4. A plot that shows chemCPA's performance and uncertainty for all compounds is part of the appendix A.6. The results show that the uncertainty score for unseen drugs correlates well with the accurate prediction

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of perturbed cells. This illustrates how chemCPA's compositional latent space can be leveraged for additional insights in order to evaluate its generalisation ability.
6 Conclusion
In this paper, we introduced chemCPA, model for predicting cellular gene expression responses for unseen drug perturbations by encoding the drugs' molecular structures. We showed how chemCPA outperforms CPA and scGen on shared tasks, while generalising over existing methods by being appli- cable to the novel task of generalising to unseen drugs. Applied single-cell data, we demonstrated how pretraining on bulk HTSS improves chemCPA's generalisation performance. This applies even when the gene set the single-cell dataset differs from the genes of the pretraining bulk RNA HTS dataset. We further provided an uncertainty measurement that correlates well with the chemCPA's generalisation ability to unseen drugs. Taken all results together, we are confident that chemCPA will benefit from higher-quality scRNA-seq HTSs in the future and can become powerful aid in the drug screening and drug discovery process.
Acknowledgments and Disclosure of Funding
LH is thankful for valuable feedback from Fabiola Curion and Carlo De Donno. LH is supported by the Helmholtz Association under the joint research school "Munich School for Data Science MUDDS" ML is grateful for financial support from the Joachim Herz Stiftung. NK and FJT ackowledge support by Helmholtz Association's Initiative and Networking Fund through Helmholtz AI (ZT-I-PF-5-01), FJT further acknowledges support by the BMBF (OLIS 18053A). FJT consults for Immunai Inc.. Singularity Bio V. CytoReason Ltd, and Omniscope Ltd, and has ownership interest in Dermagnostix GmbH and Cellarity. This work was supported by the German Federal Ministry of Education and Research (BMBF) under Grant No. 011S18036B.
References
Matthew Amodio, Dennis Shung, Daniel B Burkhardt, Patrick Wong, Michael Simonov, al. Generating hard-to-obtain information from casy-to-obtain information: applications in drug discovery and clinical inference. Patterns, 2021.
Philipp Angerer, Lukas Simon, Sophie Tritschler, F Alexander Wolf, David Fischer, and Fabian J Theis. Single cells make big data: New challenges and opportunities in transcriptomics. Current Opinion in Systems Biology, 2017.
Atray Dixit, Oren Parnas, Biyu Li, Jenny Chen, Charles P Fulco, al. Perturb-seq: dissecting molecular circuits with scalable single-cell rna profiling of pooled genetic screens. cell, 2016.
Fabian Fröhlich, Thomas Kessler, Daniel Weindl, Alexey Shadrin, Leonard Schmiester, et al. Efficient parameter estimation enables the prediction of drug response using mechanistic pan-cancer pathway model. Cell systems, 2018.
Adam Gayoso, Romain Lopez, Galen Xing, Pierre Boyeau, Valeh Valiollah Pour Amiri, et al. python library for probabilistic analysis of single-cell omics data. Nature Biotechnology, 2022.
Jase Gehring, Jong Hwee Park, Sisi Chen, Matthew Thomson, and Lior Pachter. Highly multiplexed single-cell ma-seq for defining cell population and transcriptional spaces. BioRxiv, 2018.
Xiaoping Han, Ziming Zhou, Lijiang Fei, Huiyu Sun, Renying Wang, et al. Construction of human cell landscape at single-cell level. Nature, 2020.
Leon Hetzel, David S Fischer, Stephan Günnnemann, and Fabian Theis. Graph representation learning for single-cell biology. Current Opinion in Systems Biology, 2021.
Yuge Ji, Mohammad Lotfollahi, F Alexander Wolf, and Fabian J Theis. Machine learning for perturbational single-cell omics. Cell Systems, 2021.
Kenji Kamimoto, Christy M Hoffmann, and Samantha Morris. Celloracle: Dissecting cell identity via network inference and in silico gene perturbation. bioRxiv, 2020.
10

---

Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic Denoyer, et al. Fader networks: Manipulating images by sliding attributes. Advances in neural information processing systems, 2017.
Romain Lopez, Adam Gayoso, and Nir Yosef. Enhancing scientific discoveries in molecular biology with deep generative models. Molecular Systems Biology, 2020.
Mohammad Lotfollahi, Alexander Wolf, and Fabian J Theis. segen predicts single-cell perturbation responses. Nature methods, 2019.
Mohammad Lotfollahi, Anna Klimovskuia Susmelj, Carlo De Donno, Yuge Ji, Ignacio L Ibarra, et al. Learning interpretable cellular responses to complex perturbations in high-throughput screens. bioRxiv, 2021.
Mohammad Lotfollahi, Mohsen Naghipourfar, Malte D Luecken, Matin Khajavi, Maren Büttner, et al. Mapping single-cell data to reference atlases by transfer learning. Nature Biotechnology, 2022.
Christopher $ McGinnis, David M Patterson, Juliane Winkler, Daniel N Conrad, Marco Y Hein, et al. Multi-seq: sample multiplexing for single-cell ma sequencing using lipid-tagged indices. Nature methods, 2019.
Lars Mescheder, Andreas Geiger, and Sebastian Nowozin. Which training methods for gans do actually converge? In International conference on machine learning. PMLR, 2018.
Thomas M Norman, Max A Horlbeck, Joseph M Replogle, Alex Y Ge, Albert Xu, et al. Exploring genetic interaction manifolds constructed from rich single-cell phenotypes. Science, 2019.
Thai-Hoang Pham, Yue Qiu, Jucheng Zeng, Lei Xie, and Ping Zhang. A deep learning framework for high-throughput mechanism-drivee phenotype compound screening and its application to covid-19 drug repurposing. Nature machine intelligence, 2021.
Ladislav Rampášek, Daniel Hidru, Petr Smirnov, Benjamin Haibe- Kains, and Anna Goldenberg. Dr. vae: improving drug response prediction via modeling of drug perturbation effects. Bioinformatics, 2019.
Sergei Rybakov, Mohammad Lotfollahi, Fabian Theis, and F Alexander Wolf. Learning interpretable latent autoencoder representations with annotations of feature sets. Machine Learning in Computational Biology (MLCB) meeting. Cold Spring Harbor Laboratory, 2020.
Lucas Seninge, Ioannis Anastopoulos, Hongxu Ding, and Joshua Stuart. Vega is an interpretable generative model for inferring biological network activity in single-cell transcriptomics. Nature communications, 2021.
Lisa Sikkema, Daniel C Strobl, Luke Zappia, Elo Madissoon, Nikolay S Markov, et al. An integrated cell atlas of the human lung in health and disease. bioRxiv, 2022. doi: 10.1101/2022.05. 10.483747.
Sanjay R Srivatsan, José L McFaalin--Fiuuero, Vijay Ramani, Lauren Saunders, Junyue Cao, et al. Massively multiplex chemical transeriptomics at single-cell resolution. Science, 2020.
Marlon Stoeckius, Shiwei Zheng, Brian Houck-Loomis, Stephanie Hao, Bertrand Z. Yeung, al. Cell hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome biology, 2018
Aravind Subramanian, Rajiv Narayan, Steven M Corsello, David D Peck, Ted E Natoli, et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell, 2017.
Ramzan Umarov, Yu Li, and Erik Amer. Deepcellstate; An autoencoder- based framework for predicting cell type specific transcriptional states induced by drug treatment. PLoS Computational Biology, 2021.
Ido Yofe, Rony Dahan, and Ido Amit. Single-cell genomic approaches for developing the next generation of immunotherapics. Nature medicine, 2020.

---

Bo Yuan, Ciyue Shen, Augustin Luna, Anil Korkut, Debora S Marks, et al. Cellbox: interpretable machine learning for perturbation biology with application to the design of cancer combination therapy. Cell systems, 2021.
Jie Zhu, Jingxiang Wang, Xin Wang, Mingjing Gao, Bingbing Guo, et al. Prediction of drug efficacy from transcriptional profiles with deep learning. Nature biotechnology, 2021.
12


Reviews:
[{"review_id": "xecSyvbBZ6e", "reviewer": "Reviewer_32go", "summary": "The paper suggests a new model (coined chemCPA) that predicts the transcriptomic perturbation of drugs on specific cell lines.\nOne of the highlights is that chemPCA uses some transfer learning approach to predict those perturbation at single cell level (for data is generally noisy and not widely available) while leveraging the vast amount of available bulk RNA-seq screens, even in the setting where the available set of genes are different between the source and the target domains.\nBuilding on the previously published CPA model, chemPCA relies on an  a similar autoencoder architecture but uses existing pre-trained structure embeddings of the drugs to allow generalization to unseen compounds (or unseen combinations of seen compounds).\nAfter introducing and briefly justifying the chemCPA architecture, the authors evaluate the performance of their method in an extensive setting, benchmarking first the choice of various chemical encoders, then validating their transfer learning (from bulk RNA-seq to the scRNA-seq domain) and finally the generalization performance to unseen drug compounds.\nLast, the authors suggest an uncertainty measure on the drug embedding which experimentally seems to correlate well with how well chemPCA generalizes to unseen compounds.\n\n\n09/08/2022 : after carefully reviewing the author's response to all reviewers, as well as the amended manuscript, I am updating my rating of the presentation from 2 to 3 and my overall rating of the paper from 5 to 8.", "questions": "In addition to comments on the points I raised in the previous section, here is a list of more specific questions I would love to see the authors answer:\n 1) While it may be a very reasonable idea, I am under the impression that assuming that basal states and cell lines can be completely disentangled is somewhat of a strong statement. Do you have any argument or insight that support this choice of modelling? Or is it an approximation that is necessary to make the problem tractable? Some further comments would be very appreciated (and would, I think strengthen the manuscript if included).\n 2) If I understood correctly (but I admittedly did not dig very deep into [Lotfollahi et al. '21]), the current setting is limited to predicting the perturbation induced in a fixed, known in advance, set of cell lines. Do you think this work could be extended to generalize to different cell lines, or, perhaps more interestingly to predicting perturbations in cells that are not coming from a cell line (e.g. from a biopsy)? Do you have insights on whether it would be completely unrealistic to use a vector of gene expression levels before perturbation as an input rather than an encoding of the cell line?\n 3) A Gaussian likelihood reconstruction loss is used (and the data is log-normalized and standardized as a preprocessing step). Why not using a (Zero-Inflated?)Negative Binomial model that should be a better fit to such count data? Is it something that you tried or considered?\n 4) There is no discussion on the choice of this additive model for the perturbations. I could imagine that the non-linearities induced by the encoder and decoder architectures mean that such an additive model may very well not be limiting the expressivity of chemCPA. It would be nice to have some comments about it, though.\n 5) Related to the last question but more specific, reducing the dosage attributes to a single scalar value applied to the drug embedding vector intuitively feels like a very strong underlying assumption. Do you have any comment on that?\n 6) A gradient penalty term (inspired from the Improved WGAN paper) is added to the adversarial loss. First, in [Gulrajani et al. '17], it was penalizing the gradients to have norm close to 1 (not a penalty on the norm directly). Second, having gradient norms equal to 1 was a proxy to having a 1-Lipschitz critic / discriminator because that is what made optimizing the WGAN objective equivalent to minimizing the Wasserstein distance between real and generated samples. Here, I don't see the connection at all. Any comment on the choice of this regularizer?\n", "limitations": "No discussion about the societal impact was included in the manuscript. But like the authors, I also do not foresee any impact so I don't see it as a problem.\nIn general, as written in the previous sections, my sense is that the manuscript does not comment enough on the modelling choices that were made. As such, I find the limitations of their work not to be very carefully addressed. Please refer to the other sections of this review for more details.", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Given the amount of efforts and resources spent on screening, in vitro, chemical compounds in the field of drug design, developing efficient and reliable in silico models that are able to predict the perturbations induced by such compounds on a transcriptomic level is of utmost importance.\nWhile this problem has been the object of a lot of research over the last decades, multiple limitations still makes it an open problem.\nAlthough I am not an expert of this subfield, I think that addressing specifically the following questions: 1) how to generalize predictions to unseen drugs (or combination of) and 2) if it is possible to transfer information from the very abundant and less noisy (but \"lower resolution\") bulk HTS data to the emerging (hence sparsely available for the moment) single cell HTS, is key to the development of the field.\nWhile chemPCA admittedly inspires from already existing ideas, their combination enables novel applications and use cases. As such, I consider the novelty of this work to be satisfactory and its significance high.\n\nI have some concerns about the clarity of the manuscript.\nThe experimental results of this paper are more extensive than the average NeurIPS submission with settings that require a lot of explanations.\nI have the impression that this work would benefit from a longer format to improve (e.g. a bioinformatics or computational biology journal).\nFor a ML venue such as NeurIPS, I would have appreciated a bit more insights on some of the choices / assumptions made (see the Questions below for more details).\nSome important details seem to be completely eluded. For instance, there is a mention of an \"architecture surgery\" to match the larger dimension of the extended set of genes of the target domain.\nHowever, no real explanation is provided as to how this is executed. Is it related to the scArches paper [Lotfollahi et al. '22]? If yes an explicit reference to it would be welcome and the manuscript would benefit from more explanation (at least in the appendices).\nAlso, on a more technical level, many of the abbreviations (CPA, MoA, DMSO etc.) used are not defined properly (not defined at all, or after they were first used). It is a simple detail but fixing this would improve the clarity.\nIn Figure 3, the test compounds are labelled, but it is very difficult to know which label corresponds to which point. That makes it hard to even see if the test compounds are actually clustered at all with other drugs affecting the same pathway.\n\nI also have concerns about the experimental results.\nI find the setting and design of the experiments to be very thorough and interesting.\nMy main concern is that it is mostly a very elaborate ablation study where the impact of the choice of the drug encoder, the impact of pretraining are studied (also in terms of generalization to unseen drugs).\nHowever, there is no comparison to any competing method or baseline (other than using the unperturbed measurements).\nI understand that chemCPA has a quite unique positioning and that there are likely no other methods that predict perturbations of unseen drugs on single cells.\nHowever, I think it would be relevant to test chemCPA on less ambitious settings where it can be compared to existing methods (e.g. predicting the perturbation of unseen pairs of cell lines and seen drugs).\nSignificantly improving over the SotA on such tasks would not be necessary, but showing that the results are not bad in such settings would, in my opinion, make the manuscript stronger.\n\nI am well aware that my remarks go in the direction of adding even more content to an already very dense paper. Maybe some of the experimental details and results could be relegated to the appendices.\n\nOverall, I have no doubt that the work underlying this submission is novel and significant enough to be the object of a relatively high impact publication. However, I think that in its current form, the paper is not reflecting the high quality and potential of this work.\nIf the authors answer my concerns convincingly and have a plan to update their manuscript accordingly, I could very well be convinced to improve my rating.\n", "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": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "m0AgK82sHz", "reviewer": "Reviewer_Qepn", "summary": "The authors describe a method for predicting the gene expression response of a cell line to drug treatment. They make use of an autoencoder framework, encoding the gene expression profile using a dense neural network layer. They use adversarial losses to discourage the model from encoding the cell line and drug treatment in the latent space. Finally, they derive features from the molecular structure of the drug using various approaches. Embeddings of the expression profile, cell line, and drug molecule are decoded with dense neural network layers and compared to the observed gene expression from the experiment by a loss function.\n\nAug 9 - I increased my ratings after reading the authors' revision and rebuttal.", "questions": "Line 90, Gaussian likelihood is inappropriate for RNA-seq.\nLine 141, L1000 isn’t bulk RNA-seq, it’s a platform called NanoString.\nFigure 3, in the caption, why are latent embeddings “identical” for molecules that target the same pathway?\nTable 4, caption incorrectly describes table.", "limitations": "Yes, limitations have been addressed.", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Predicting cell responses to drugs is an important problem.\n\nI don’t understand the evaluation. Why is the term “counterfactual” prediction used? It sounds like it refers to model encoding and decoding with DMSO as the molecule, but I don’t understand why this would be relevant. Are the authors evaluating performance on other molecules than DMSO? I’m confused by the R2 metric, which seems to be computed across genes for each sample, and the authors use of the term “disentanglement”. The authors describe Table 1 as if it evaluates disentanglement, but the metric is R2. What are the drug and cell line columns of Table 1 showing? In the case of DEGs, do the authors consider distinct DEGs for each molecule?\n\nThese ideas have been published previously. The authors don’t distinguish their method, and they don’t compare to previously published methods. E.g. see Lotfollahi, M. et al. Learning interpretable cellular responses to complex perturbations in high-throughput screens. bioRxiv 2021.04.14.439903 (2021) doi:10.1101/2021.04.14.439903. https://www.biorxiv.org/content/10.1101/2021.04.14.439903v2", "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": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "NjZxT0ExHW6", "reviewer": "Reviewer_mn3q", "summary": "In this paper, the authors propose a new model called chemCPA to study the perturbational effects of unseen drugs by incorporating knowledge about the drug's molecular structure.   They introduce new encoder-decoder architecture and benchmark against other molecule encoding networks. Also, the authors combine this model with a transfer learning scheme that improves generalization by utilizing existing HTS bulk RNA-seq data. ", "questions": "1) I am not entirely clear on how subsetting of 977 genes is done via ensemble gene annotations?\n2) have the authors looked into other datasets mentioned in the Compositional perturbation autoencoder paper?", "limitations": "Limitations are scattered across multiple sections. It may be much more helpful to assemble them in the discussion section for improved readability ", "rating": 7, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "The authors propose a model for predicting cellular gene expression responses for unseen drug perturbations by encoding the drugs’ molecular structures. The approach is well motivated and is described clearly. Authors have demonstrated that the pretraining on the abundance of bulk HTS datasets improves generalization. The chemCPA has several advantages like a latent space disentanglement and the induced interpretability of the perturbations in the context of cell state and covariates, incorporating molecular structure over regular encoders. \n\nOne concern is that the performance of chemCPA models on DEGs is worse compared to all genes. Also, it may be beneficial to benchmark chemCPA against CPA or scGen in less complicated scenarios with a few perturbations. \n\nOn line 60, computational perturbation auto-encoder (CPA) should be changed to Compositional perturbation autoencoder", "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": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}]

Author rebuttal:

Decision: Accept