MAGNet: Motif-Agnostic Generation of Molecules from Scaffolds
Authors: Leon Hetzel
Johanna Sommer
Bastian Rieck
Fabian J Theis
Stephan Günnemann
Venue: ICLR 2025 Spotlight (2025)
Keywords: graph generative models
2d molecules
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Recent advances in machine learning for molecules exhibit great potential for facilitating drug discovery from in silico predictions.
Most models for molecule generation rely on the decomposition of molecules into frequently occurring substructures (motifs), from which they generate novel compounds. 
While motif representations greatly aid in learning molecular distributions, such methods fail to represent substructures beyond their known motif set, posing a fundamental limitation for discovering novel compounds.
To address this limitation and enhance structural expressivity, we propose to separate structure from features by abstracting motifs to scaffolds and, subsequently, allocating atom and bond types. 
To this end, we introduce a novel factorisation of the molecules' data distribution that considers the entire molecular context and facilitates learning adequate assignments of atoms and bonds to scaffolds. Complementary to this, we propose MAGNet, the first model to freely learn motifs. Importantly, we demonstrate that MAGNet's improved expressivity leads to molecules with more structural diversity and, at the same time, diverse atom and bond assignments.

Published as a conference paper at ICLR 2025
MAGNET: MOTIF-AGNOSTIC GENERATION OF MOLECULES FROM SCAFFOLDS
Leon H- Johanna Sommer" 1,2 Bastian Rieck Fabian Theis & Stephan Günnemann" 1.2
1 School of Computation, Information and Technology, Technical University of Munich
2 Munich Data Science Institute, Technical University of Munich
3 Center for Computation Health, Helmholtz Munich
4 Department of Computer Science, University of Fribourg
{1 wetheeteee zel, jm. sommer, b. b.rieck, thehi, s. g.guennemana umm. de
ABSTRACT
Recent advances machine learning for molecules exhibit great potential for facilitating drug discovery from in silico predictions. Most models for molecule generation rely on the decomposition molecules into frequently occurring sub- structures (motifs), from which they generate novel compounds. While motif representations greatly aid learning molecular distributions, such methods fail represent substructures beyond their known motif set, posing fundamental limitation for discovering novel compounds. To address this limitation and enhance structural expressivity, we propose separate structure from features by abstract- ing motif's to scaffolds and, subsequently, allocating atom and bond types. To this end, we introduce novel factorisation of the molecules' data distribution that considers the entire molecular context and facilitates learning adequate assignments atoms and bonds to scaffolds. Complementary to this, we propose MAGNet, the first model to freely learn motifs. Importantly, we demonstrate that MAGNet's improved expressivity leads to molecules with more structural diversity and, at the same time, diverse atom and bond assignments.
INTRODUCTION
Generative models have become powerful tool for generating novel compounds, finding applications in fields like drug discovery, material science, or chemistry (Bian Xie, 2021; Butler al., 2018; Choudhary al., 2022; Hetzel et al., 2022; Moret et al., 2023), Such deep learning-based molecular generators offer a promising avenue for efficiently navigating the chemical space and generating unique molecules with specific properties (Zhou et al., 2019; Hoffman et al., 2022). A crucial factor contributing to the impressive performance these models is the incorporation molecular fragments, known motifs. Motifs make it possible to explicitly include complex structures, such as cycles, into model and provide a powerful inductive bias for the generative process (Sommer et al., 2023; Wollschläger al., 2024; Jin et al., 2018 8; Maziarz al., 2022; Geng et al., 2023; Kong et al., 2022). However, motif- included. As a result, these methods resort including only the top-k most frequently occurring motif's (Maziarz et al., 2022; Kong et al., 2022), claiming that remaining complex structures can be decoded atom-for- atom.
approaches often lead to a prohibitively large vocabulary if all motif's
This claim, however, does not hold in practice, and we demonstrate that vocabulary of the most commonly occurring motifs is insufficient to capture the vast structural diversity of the molecular space. A simple experiment exposes this shortcoming: As demonstrated in Fig. a, state-of-the-ae molecular models fail to reconstruct key structures of FDA-approved drugs. This because these structures are absent from its vocabulary. If model struggles to handle structures of known drugs that cannot be trivially built from its vocabulary, how can it be effective discovering new drugs? This inability raises significant questions about the usefulness of previous generative models and calls for reassessment of molecular generators from the perspective structural diversity.
"Equal contribution
Project Page: www.cs.cit.noo wtt.:/s.....ttmdde//oo wtt..ummmdddddm//maeeeet

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Generative models miss key structures
Motil Vocabulary Vee
Single Atoms
Uncommon structure
Rimegepannt
MAGNet Separate structure
Zuranslon
featuree
Too rare
Amoxkdllin
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Figure 1: (a) Motif-based generative models fail to capture key molecular structures, which limits their expressivity. (b) MAGNet overcomes these limitations by separating structures and features. The reduced inductive bias allows for a more expressive vocabulary while enabling the modeling of a wider range of molecular structures by freely learning the mapping from scaffolds motifs. (c) This separation is particularly relevant for "uncommon structures" that pose difficulties for state-of-the-art models; see App. D.2.
We address this fundamental limitation of previous motif-based methods by separating the molecular structure from its features. We achieve this by abstracting motifs to their more general representation: scaffolds. In our case, these scaffolds are related to generic Murcko scaffolds but technically do not hold any feature information about bonds or atoms, see Fig. b. Hence, our approach reduces the combinatorial complexity needed to effectively capture the molecules' structural diversity, thereby enhancing expressivity within fixed-sizz size vocabulary. At the same time, it requires learning the features, i.e. atom and bond types, enabling the generation of motif's beyond those contained in fixed set.
To realise generative model based on this abstraction, derive novel factorisation of the data distribution that splits a molecule into distinct components. The resulting factorisation utilised in two ways. First, develop a fragmentation scheme generate structurally expressive scaffold vocabulary. Second, we introduce a new hicrarchical method, MAGNet, designed facilitate the sampling of diverse molecular structures and enable the free learning of scaffold features into motifs, thereby generating molecules in a motif-agnostic manner.
Furthermore, find that established evaluation benchmarks do not sufficiently evaluate the structural diversity of generated molecules. To address this gap, we propose several new analyses to complement existing distribution learning benchmarks. Our primary focus lies on evaluating model's ability to reliably decode uncommon molecular structures, such as those illustrated in Fig. 1 c, including also those rare substructures that were encountered during training. Additionally, we assess models the motif level, comparing MAGNet'' 's feature prediction to its motif based competitors. We specifically examine how diverse and accurate the generated allocations of atom and bond types are.
In summary, our contributions are:
We improve structural expressivity by abstracting motifs to scaffolds. To this end, we introduce a novel factorisation scheme that facilitates the creation of a more expressive scaffold vocabulary as well as a novel hierarchical generation procedure, MAGNet.
MAGNet freely learns the featurisation of scaffolds, enabling the generation of greater variety of atom and bond type allocations than motif-based approaches.
We propose alternative evaluation analyses for effectively assessing model's ability to decode complex and uncommon substructures at both the scaffold and motif level.
2 RELATED WORK
Molecule generation Existing generative models can be divided into three categories (Zhu et al., 2022: Yang et al., 2022; Du et al., 2022): (1) string-based models, relying on string representations et al., 2018; Segler et al., 2018; Flam- Shepherd et al., 2022: Fang et al., 2024; Adilov, 2021; Grisoni, 2023), which do not leverage structural information, (2) based models, which model the molecular graphs, and (3) geometry- models, which
like SMILES SELFIES
Hloeez-Bombare
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represent molecules by atomic point clouds (Luo & Ji, 2022; Ragoza et al., 2020; Satorras et al., 2021; Luo et al., 2021a; Gebauer et al., 2022; 2019;
Hoogeboom et al., 2022; Xu et al., 2023; Huang et al., 2022; Vignac et al., 2023b; Huang et al., 2024; Qiang et al., 2023). Within the area of molecular geometry generation, Adams & Coley (2023), Chen et al. (2023) and Long et al. (2022) focus on the shape-constrained design of conformers within 3D context and consider surface areas as generation largets. These molecular geometry generators can, in tur,, be integrated into frameworks like the one proposed by Ayadi al. (2025), facilitating their adaptation various downstream tasks in drug discovery. Moreover, graph-based approaches involve models that represent molecular graphs (i) primarily at the atom level or (ii) predominantly through motifs. Zhu et al. (2022) categorise the generation process further into sequential methods, building molecules per fragment while conditioning on a partial molecule (Khemchandani et al., 2020; Shi et al., 2020; Popova et al., 2019; Mercado et al., 2021; Luo et al., 2021b; Liu et al., 2018; 8: Li et al., 2018; Assouel et al., 2018; You ct al., 2019; Yang ct al., 2021: Lim et al., 2020; Kajino, 2019; Jin et al., 2020; Bengio et al., 2021; Ahn et al., 2021; Shirzad et al., 2022), and one-shot (OS) approaches that create each aspect of the molecular graph in a single step (Kong et al., 2022; Simonovsky & Komodakis, 2018; Ma et al., 2018; Liu et al., 2021; De Cao & Kipf, 2018; Zang & Wang. 2020; Bresson & Laurent, 2019; Flam-Shepherd et al., 2020; Samanta et al., 2020). Note that diffusion-based models iteratively refine the entire graph, making them difficult categorise as sequential App. C for more detailed discussion on the classification of models into the one-shot or sequential category. While these models are predominantly used in the 3D context, Vignac et al. (2023a) and t processes that fit category (2).
OS. We refer the reader to
Ketata et al. (2024) propose diffusion
Fragmentation and scaffold representation Various techniques are available for constructing fragment vocabularies, with distinction between cemmically-inspiree and data-driven approaches. For example, both HierVAE (Jin et al., 2020) and MoLeR (Maziarz et al., 2022) adopt a heuristic strategy known "breaking bridge bonds" to decompose molecules into rings and remainder fragments, emphasising chemically valid substructures. In a similar vein, JA- (Jin et al., 2018) employs fragmentation guided by the construction of junction trees. In contrast, PS-VAE (Kong et al., 2022) and MiCaM (Geng et al., 2023) take data-driven bottom-up approach, creating fragments by merging smaller components, starting from single atoms. MiCaM even integrates attachment points, resulting in larger, "connection-awy -wware" vocabulary.
3 FACTORISING THE DATA DISTRIBUTION PP())
We provide a general mathematical description of our framework below. However, this section is not essential for understanding the main concepts of the remainder of this work and we introduce the matching model in Sec. 4. We consider molecule G to be defined by its underlying graph, i.e., by its graph structure (nodes and edges), together with its node and edge features, describing atoms and bonds, respectively. We consider factorisation of the probability mass function P(G), which is illustrated in Fig. 2, that decouples structure from features:
P(G) Pg Gs) P(Gs)
since
P(Gs
9= =1
where G refers to the full atom-level graph and Gs to its abstraction to scaffolds. Gs represents coarse view of a molecule's 's topology by specifying the scaffolds that make up the molecule as well as their connectivity. Representing Gs by the multiset of scaffolds S and their (typed) connectivity A (|S) where A defines the possible values of A, Gs (S. A), P(Gs) becomes:
IPGs) = P(A P(S).
Moving forward to the atom level, a node S in the scaffold graph Gs can be expanded into its binary adjacency matrix of its [S] nodes, i.e. S {0. 1]ax and is equipped with node and edge features, corresponding to atom and bond types, respectively. We consider this feature-equipped representation of S to be a typed subgraph M, or motif, of the input graph G. and denote the associated multiset of motifs within the molecule by M.
The connectivity between two scaffolds, signified by Au + O, indicates that, the atom level, the two scaffolds share a common join node (atom) j. To determine node j, we define the set of join
nodes J. which includes all join nodes j that are contained in two motifs: J {i E Mg, e
Mr, Agt # 0, Sg, S₁ E S}.
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MAGNet's Generation
scaffoids
joins T
connectivity
motifs M
leaved
Scaffold Level G3
Atom Level g
Figure 2: On the scaffold-level, MAGNet predicts the scaffold multiset S and its connectivity A. Progressing the atom-level, Gs informs the generation of motifs M. To fully define the molecular graph G. the join node positions T and the leaf nodes L are predicted.
Note that we can effectively provide information about the atom type of the join node j by considering A {0, C,N,... ... to include atom types, which is reflected by the ablation study A in App. D.7. Lastly, we do not include all atoms in the scaffold graph to allow for a concise set of structurally distinct scaffolds,
Sec. 4.1. Specifically, we define the of leaf nodes describe nodes with degree di = and neighbours & A with dy = 3.
In conclusion. G is defined by the combination of motif's join nodes and leaf nodes C. enabling to factorise P()) as follows:
P(G) = P(C.J.M) = P(C J)P[ M, P(Gs).
where we use 9s to factorise the distribution. Note that I is conditionally independent of given the motif's M. Similarly, L is conditionally independent of A given J.
MODELLING MOLECULES FROM SCAFFOLDS
Building upon the presented factorisation, we continue introduce our method derive a concise scaffold vocabulary and discuss the matching generative model: MAGNet. MAGNet is graph-based model that employs a unique approach by generating each hierarchy level in single step. positions itself between the traditional categories of single-atom and fragment- -based models by utilising scaffolds as building blocks and subsequently generating appropriate atom and bond attributes. To facilitate this generation of diverse motifs and molecular structures, fragmentation is designed to achieve concise representations.
4.1 IDENTIPYING CONCISE SET OF SCAFFOLDS Vs FROM DATA
Given a dataset of molecules, our fragmentation scheme aims to represent a molecule through clear structural elements, for which we provide examples in App. A. For this, we by removing all leaf nodes L across the graph, following the approach outlined in previous works (Jin et al., 2020; Maziarz al.. 2022). This step helps to divide the molecule G into cyclic and acyelic parts. Importantly, instead of modelling the connection between two fragments M, and M, with connecting bond, we represent it by shared atom, matching the definition of a join node E from Sec. 3.
In subsequent step, we decompose the resulting acyelic fragments to reduce the number of required scaffolds as much as possible. To this end, we introduce "junctions", acyclic structures that are defined by center node of degree three or four and its neighbours. When compared to the "Breaking Bridge Bonds" decomposition (Jin et al., 2020; Maziarz et al., 2022), our approach reduces complexity and results in much smaller vocabulary by collapsing acyclic structures into distinct scaffolds. Additionally, in contrast to data-driven methods like those outlined in Kong et al. (2022) and Geng al. (2023), decomposition method maintains structural integrity through its top-down approach.
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To arrive at our scaffold vocabulary, remove atom and bond types from the resulting fragments in the final step. As a suitable generative model has to map a single scaffold to its various representations, this fragmentation further enables to model smoother transitions between scaffold representations. This is contrast to fragment-bon based methods, which have to select different tokens from a large vocabulary when two motifs differ in, e.g. just bond type, see Fig. 6 in App.
4.2 MAGNET's S ENCODER
MAGNet is trained as VAE model (Kingma & Welling, 2022), where the latent vectors are trained to encode meaningful semantics about the input data, which subsequently inform the generation process. During training, model is optimised by maximising the ELBO:
= = R (P(g =)] + Dki. (Q(( [ P) with P P~.1(0.11
where the KL-divergence DRL. serves to regularise the posterior Q(= () towards similarity with the Normal prior P in the latent space, weighted by B.
MAGNet's encoder aims to learn the approximate posterior Q(2 9). At its core. the encoder leverages graph transformer (Shi et al., 2021) for generating node embeddings of the molecular graph. Since MAGNet generates molecules in coarse to fine-grained fashion, we encode information about the decomposition of the molecules. We compute individual embeddings for the molecular graph 9 and the scaffold graph 9s by aggregating over the corresponding atom and scaffold nodes, respectively. In addition, we separately aggregate all join and leaf nodes. The resulting representation of these individual components-moles mapped to the latent space, constituting the graph embedding 2g. More details about the chosen node features as well as technical specifications of the encoder can found in App. B.I.
graph, scaffolds, join nodes, and leaf nodes-is then
4.3 MAGNET'S GENERATION PROCESS
MAGNet is designed to represent the hierarchy of the factorisation into scaffold- and atom-level from Sec. 3, see Fig. 2. That is, given a vector x from the latent space, MAGNet's 's generation process first works on the scaffold-level to predict Gs; defined by the multiset and its connectivity A, before going to the atom level defined by the motifs M, join nodes J, and leaf nodes L. Additional details on the implementation can be found in App. B.2.
Scaffold-Level On the scaffold-level. MAGNet first generates the scaffold multiset S via a trans- former decoder module- tthe same scaffold can occur multiple times in one molecule- ffre the latent representation z. More specifically, we learn P(S 22 by conditioning the generation on the latent code z and generate one scaffold a time, conditioning also on the intermediate representation of the scaffolds.
Given the scaffold multiset S, MAGNet infers the scaffold connectivity A between scaffolds S, E S from scaffold set embeddings obtained with multi-attention module. Formally, t S,z) where E {0, C... not only encodes the existence (or absence) of scaffold connection but also its atom type. We consider typed version of A to provide meaningful condition for generating the motifs M later on and provide and ablation on this in App. D.7. The scaffold-level loss is computed by Lgs = Ls + LA, where Ls and LA refer to the categorical losses of the scaffold set and connectivity. respectively.
learn P(A S) = II".j- P(A,,
Atom-Level Leveraging a molecule's scaffold-levll representation (S, A), MAGNet incorporates feature-predicting module identify appropriate node and edge attributes for each scaffold, see Fig. bb. By freely predicting the atom and bond types to define the motifs M. MAGNet gains flexibility compared its motif-based competitors. The mapping of a scaffold S; to a motif P(M; S, A, 2) is performed in two steps, starting with the prediction of the respective atom types M from the scaffold graph and latent code z by means of a transformer decoder module. Subsequently, the resulting atom embeddings M are leveraged to determine the matching bond types 2 between connected nodes.
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By conditioning A, ensure that M₁ includes all nodes needed for connectivity on the atom-level. This comes from the fact that the motifs M adhere to the join node types defined by A. In mathematical terms, this means Agt E U, ME n Mf, where signifies the exclusive consideration of atoms.
To establish the connectivity on the atom level, MAGNet proceeds to identify the join nodes J. whose types are already defined, via multi-layer perceptron. Therefore, MAGNet only needs to predict the pairs of node positions Pa that form joins, collectively creating the set of join nodes J. Mathematically, express the likelihood of merging nodes and j in motifs MN and Mj by the merge probability f(h.1) = P(p: = Pj M, =), which constitutes the join matrix ((A,)) E [0, 1] x Va,, In the final step, MAGNet utilises a transformer decoder to predict the leaf nodes L. which consists of determining the atom type and its attachment to the current atom graph C (for core molecule). Optimising the likelihood P(Cs C, 2) is done similarly to the motif prediction, only that we use the atom graph C model input. The final atom-level loss consists of Lg = LM + + where L.M Lg. and Lc describe categorical losses for motifs M,, including both atom and bond types, join nodes J. and leaf nodes C, respectively. When optimising MAGNet with reconstruction loss consisting of Lg + Lgg. we have observed that a simple KL-regularisation alone is inadequate for achieving a smoothly structured latent space. Our analysis in App. B.3 shows that the latent space suffers from over-pruning behaviour (Yeung et al., 2017). 7). To remedy this, we apply a normalising flow post-hoc to the latent space, aligning it more effectively with the prior. To this end, we rely on Conditional Flow Matching (Lipman et al., 2023) and, more specifically. use the version based on minibatch optimal transport as presented by Tong al. (2024). We specify MAGNet's hyperparameter configuration in App. B.4.
Limitations As for the limitations, we note that both sequential and one-shot molecular generation methods are susceptible to error propagation (Muenkler et al., 2023). Our results in Sec. 5, however, indicate that the separation between scaffold and atom-level does not harm MAGNet in practice, as MAGNet reliably identifies suitable atom and bond allocations for the generated scaffold graph. While MAGNet successfully generates diverse molecular structures, incorporating synthesizability considerations would further enhance its practical applicability in drug discovery pipelines. This limitation could be addressed by training on multi-component reaction datasets, potentially bridging the gap between computational predictions and experimental validation (Graziano et al., 2023).
5 RESULTS
We compare MAGNet to state-of-ther molecular generators across several dimensions of the generation process. In See. 5.1, we investigate the reconstruction and sampling of scaffolds S, as the fundamental component of MAGNet's factorisation, and show that our model, unlike the baselines, captures the diverse structural characteristics found in molecules. In Sec. 5.2, we continue to evaluate the generative performance using established benchmarks. In Sec. 5.3, we analyse MAGNet's ability to determine atom and bond allocations M freely and demonstrate that MAGNet learns to generate a larger variety of motifs in accordance with the dataset compared to motif-based approaches. Finally, in Sec. 5.4, we give an outlook on how MAGNet can be utilised for downstream applications, such as goal-directed generation and conditioning on various levels of the generation process.
Baselines In this section, our focus lies on variational autoencoder models designed to generate molecular graphs. We select models from the one-shot and sequential categories as detailed in Sec. 2. These include PS-VAE (Kong et al., 2022), a two-step generation framework that first generates subgraphs before predicting connections, from the one-shot category and MoLeR, a method that extends a partially generated graph by adding new fragments at each step, (Maziarz et al., 2022) from the sequential category. Additionally, we incorporate MiCaM (Geng al., 2023) third baseline, which is another fragment- based method that utilises connection-aware motifs, leading to a significantly larger and more fine-grained vocabulary than ours. In order contextualise MAGNet with the full spectrum of molecule generation methods, we also evaluate JTVAE (Jin et al., 2018), HierVAE (Jin et al., 2020), GraphAF (Shi* et al., 2020), SMILES-LSTM (SM-LSTM) (Segler et al., 2018) and CharVAE (Gómez-Bonne Bombarell et al., 2018) as baselines for distribution learning benchmarks.

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a
Decoding uncommon scatfolds
b
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Figure 3: (a) Reconstruction of molecules that include large cycles or complex junctions. Relying on individual atoms to build these structures is not sufficient. Only MAGNet is able to reliably decode its latent code z. (b) Percentage of reconstructed scaffolds. MAGNet substantially improves in reconstructing both common and more importantly uncommon scaffolds. (c) Comparison of sampled scaffolds their frequency in the training distribution. A ratio of is optimal.
Benchmarks and datasets To evaluate the ability to learn the underlying distribution of molecules, we employ two standard benchmarks for de novo molecule generation. The GuacaMol benchmark asseses the ability of generative model to sample by the distribution of a molecular dataset (Brown et al., 2019). Next to evaluating the uniqueness and novelty of sampled molecules, the benchmark also computes distributional distances the reference, i.e. the KL-ddiergeence and Fréchet distance score (FCD) (Brown et al., 2019). We use the MOSES benchmark (Polykovskiy et al., 2020) to report measures for the internal diversity (IntDiv) of generated molecules as well as chemical properties such as synthetic accessibility (SA), the octanol-water partition coefficient (logP), and the viability for drugs (QED). We also evaluate subset of our baselines on the GuacaMol goal-directed benchmark, which tests the ability of generative models to generate molecules that maximise certain score functions, e.g. similarity to known drug-like molecule. All models are trained on the ZINC dataset (Irwin et al., 2020) and the benchmarks conducted on the corresponding test set. We further use QM9 (Wu et al., 2018), GuacaMol (Brown et al., 2019), CheMBL (Mendez et al., 2019), and L1000 (Subramanian et al., 2017) for additional evaluations.
5.1 USING SCAFFOLDS TO REPRESENT STRUCTURAL VARIETY OF MOLECULES
Reconstructing complex structures As a continuation of Fig. 1, our first experiment provides qualitative insights into how accurately scaffolds are decoded, P(S 2). We assess the decoder's performance in reconstructing molecules from the test set, which includes uncommon scaffolds like large rings or complex junctions. Our observations reveal that the baseline models have difficulty in constructing complex scaffolds, as illustrated in Fig. 3a. We attribute this limitation to the absence of motif' which map to such scaffolds in their top-k vocabularies. Consequently, these models face the challenge of constructing scaffolds such large rings from individual atoms. In contrast, our proposed model, MAGNet, operates with a moderately scaffolds, enabling it to generate molecules that follow the latent code and the corresponding ground truth molecules (see the latent displacement analysis in App. D.4).
vocabulary that includes complex
MAGNet reliably decodes scaffolds Building on our analysis of large cycles and uncommon junctions, we extend our investigation to assess how effectively different models can reconstruct the scaffold set a general context. Given our focus on P(S 2),, we can disregard the connectivity A and motifs M. As illustrated in Fig. 3b, our findings demonstrate that MAGNet consistently outperforms all evaluated baselines, supporting the hypothesis that the other methods fail to faithfully
decode complex molecular structures from single atoms and rely primarily on the information encoded in their vocabulary.
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Table GuacaMol and MOSES Benchmark. We report mean and standard deviation using 5 random
seeds. We underline the best graph-gasssaeee based method and make the best method within each category
bold.
GuacaMol
MOSES
FCD (1)
KL (t)
IntDiv (1)
logP (U)
SA (4)
QED (L)
SM.
CharVAE
0.17 1 0.08
0.78 0.04
0.88 0.01
0.87 1 0.14
0.48 1 0.13
0.06 0.00
SM--.STM
0.93 0.00
1.00 0.00
0.87 =
0.12 0.1
0.04 : 0.82
0.00
Sequential
GraphAF
HierVAF
0.05 + 0.00
0.53 +: 0.14
0.92 0.01
0.67
= 0.01
0.87 =
0.93 + eum
0.36 =
0.41 = 0.02
0.20 +
0.88 # 0.30
0.03
0.22 0.01
MiCaM
0.63 0.02
0.94
0.87
0.20
0.51 # 0,83
0.08
ITVAE
0.75 0.00
0.94 0.00
0.86
0.28 0.13
0.34
0.01 0.00
MoLeR
0.80 0.01
0.98 0.00
0.87 0.00
0.13 0.02
0.06 0.81
0.01 0.01
One- One-Shot
PSVAE
0.28
0.83 0.00
0.89
0.34 0.02
1.18 0.06
0.05 0.00
DiGress
0.65 0,00
0.91 0.00
0.86
0.00
0.61
1 0.02
0.09 1. 0.01
0.05 0.01
MAGNet
0.76 + 0.00
0.95 0.00
0.88 0.00
0.22 0.01
0.12 + 0.01
0.01 0.00
Even with a significantly increased vocabulary for methods such as MoLeR, we find in App. D.1 that a larger vocabulary does not significantly help to model to reconstruct uncommon scaffolds. Furthermore, we observe that MiCaM, MoLeR, and PS-VAE hallucinate structures dissimilar to the scaffolds present in the dataset, App. D.8.
MAGNet matches the scaffold distribution more accurately To further analyse to what extent uncommon scaffolds not only reconstructed but purposefully sampled, we analyse the scaffold set of generated molecules. If the other models are able to represent scaffolds that are not included in their vocabulary, they should be able to reflect the reference distribution of scaffolds. For this evaluation, we decompose sampled molecules into their scaffolds. We then measure the models'
over- and undersampling behaviour based the ratio rs, Ee C.(.) En (Sx)) where e and c,
(S,)
(S.)
refer to the count function applied to the training and sampled sets, respectively. Fig. 3e shows that baseline methods fail to generate molecules in accordance with the reference scaffold distribution in practice. On common scaffolds, i.e. those that occur in more than 10% of the molecules, all evaluated models are able to match the ratio of the data distribution. For uncommon scaffolds, however, the baselines fail: while PS-VAE heavily oversamples both ring-like and chain-like scaffolds, MoLeR and MiCaM oversample chain-like scaffolds. MAGNet matches the reference distribution best across categories and we conclude that the abstraction to scaffolds benefits generation.
5.2 GENERATIVE PERFORMANCE EVALUATED ON COMMON BENCHMARKS
MAGNet performs on par with motif-based approaches The MOSES and GuacaMol benchmarks conducted 10 latent codes sampled from the prior distribution, molecules. Our results for both benchmarks are depicted in Tab. 1, where we classify the methods into their generative approaches as described in Sec. 2. We do not report Novelty and Uniqueness, as almost all evaluated models achieve 100% on these metrics. Solely GraphAF and HierVAE perform slightly worse but still achieve above 90% in Uniqueness and Novelty. For the baselines DiGress, SM-LSTM, and VAE, which are not able to achieve 100% Validity, we sample until we obtain 1 valid molecules. We report all benchmark metrics in App. D.3. While MoLeR sets the state of the art on both FCD and KI., MAGNet outperforms all other graph-based baselines. This supports the proposed factorisation in Sec. 3 while also challenging the common perception that methods for molecule generation must rely on motif vocabularies to obtain good generative performance.
P. and decoded into valid
The FCD metric insufficient for evaluating structural diversity Despite the FCD being an important metric for molecular distribution learning, we find that it fails to provide insights about the structural diversity of the generated molecules. Evaluating the benchmark on a subset of 10a molecules from the training data, which was filtered to include only the 10 most common scaffolds, results in an FCD score of 0.89. This observation offers an explanation for why models like MoLeR can achieve ofte--of-the-att FCD scores, despite not accurately capturing the scaffold distribution, as demonstrated in Sec. 5. This underscores that our analysis on structural diversity complements these benchmarks, providing valuable insights about the molecular distribution.
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3 Varicty sumpled motif fovel scaffuld MAGNet
Muei distrlattion scaffiblly
Ostanc perfect profction
FS UAKE
Sund
36
Urcomrnce
Cydlt
Acyclic
MMO
3.2
31
MAGRAENSIAR PS-ME
(lataoe Simi flane
Figure 4: (a) Example of generated fragments by MAGNet and baseline methods. (b) MMD computa- tion to quantify similarity between generated and ground truth motifs. (c) Rank comparison between predicted fragments and their original counterparts.
Condition fragment
Condition on mutiple scaffolds
x
Figure 5: Examples of conditional molecule generation with MAGNet. The generation is conditioned on (top) a complete fragment, including atoms and edges, and (bottom) two distinct scaffolds.
5.3 GENERATION OF MOTIFS M
Having established that MAGNet reliably decodes molecular structures and their scaffolds and samples diversely, continue evaluate MAGNet the motif level.
MAGNet's generated motifs are superior to fixed fragments The larger given scaffold, the more the combinatorial aspect starts to dominate: with size-limited vocabulary, it is challenging to reflect the diversity of scaffold's ' realisations to motifs during decoding. This shown in Fig. 4a, which provides qualitative view generated and sampled motifs. We extract the motif of single scaffold from the molecules sampled in Tab. and plot the two principal components of their fingerprints. For the chosen scaffold alone, there are 791 representations in ZINC. PS-VAR, MiCaM, and MoLeR do not cover the distribution fully, even though the scaffold appears commonly in the dataset. MAGNet, however, covers all parts of the distribution, even outliers. Fig. 4b shows the MMD quantification of the procedure shown in Fig. 4a, confirming that MAGNet is able to cover the entire distribution of motifs best. Note that, by learning to predict bond and atom assignments, MAGNet achieves coverage of motifs that extend upon the fixed motif sets of the other methods. This effect becomes also apparent when assessing zero-shot generalisation capabilities on other datasets, where MAGNet is able to achieve the highest similarity scores, improving over the strongest baseline by up to 20%, see App. D.6.
Allocation of atom and bonds to scaffolds Extending the sampling analysis from Fig. 4b, we quantify the process turning scaffold into a chemically valid motif in Fig. 4c. For each scaffold in the dataset, we compute the similarities between the set of all generated and ground truth motifs. Within the set of ground truth motifs and successful scaffold decodings, we compute the similarity rank between generated and ground truth pairs. In the majority of cases, MAGNet achieves rank 0 or 1 in the motif generation, indicating near werfect accuracy, with uncommon rings remaining the most challenging decode.

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5.4 MAGNET FOR DOWNSTREAM APPLICATIONS
Having analysed the generative performance of MAGNet and the benefit of the proposed scaffold fragmentation, we demonstrate that, like all VAE-based architectures, MAGNet can adapted to generate molecules with desired properties. Additionally, outline how one use MAGNet's factorisation for the generation of linkers and structure conttrrreed generation.
Goal-directed generation We evaluate to what extent the models can be used to find molecules that maximise a given score function. We compare against MoLeR and MiCaM and additionally provide random baseline that samples molecules from the dataset as lower bound. Tab. 2 shows the results and observe that MAGNet performs on par with or better than MoLeR, confirming our compet- itive performance on established benchmarks. We provide more information about the latent optimisation procedure as well as additional goal-directed results App. D.9.
Table 2: Results for the GuacaMol goal- directed benchmarks. All scores should be maximised. We report mean and stan- dard deviation and highlight the best method in bold.
MAGINe:
MoLeR
MHCaM
Rundom
Bassaas
0.34 0
0.32 0.03
0.31 0.04
0.17 = 0.12
MPO
0.74 0.06
0.70 s: 0.58
0.71 2 0.07
0.61 = 0.22
Property
832 I.22
0.11 8 0.05
882 801
0.54 0.99
Redhu::
0.17 0.00
0.24
+ 0.00
0.24 808
0.14 am
Sunilarity
0.43
0J3
0.38
= 0.04
= 0.3
0.26 = 0.08
MAGNet efficiently generates molecules conditioned fragments and scaffolds In the context of potential downstream applications, we investigate novel scaffold conditioning methods made possible by MAGNet's factorisation. Besides the latent space interpolation App. D.5, Fig. illustrates that MAGNet is capable to condition not only on single scaffold but also on multiple scaffolds, even when they are not directly connected within the resulting molecule. MAGNet makes this possible by enforcing e.g. set of conditioning scaffolds or given scaffold representations during the decoding phase. This poses a significant challenge for models like MoLeR, which rely on extending connected subgraphs for scaffold conditioning. Moreover, MAGNet enables conditioning on multiple levels of abstraction and can generate molecules conditioned on fragment as well as solely based scaffold, see Fig. 5.
6 CONCLUSION
To address fundamental limitations of motif-based approaches for molecular graph generation, we propose to separate structure from features. To this end, we introduce a novel graph factorisation that enables us to build an expressive scaffold vocabulary using a novel fragmentation scheme. Furthermore, present MAGNet, a generative model that builds upon this abstraction achieve greater structural expressivity. Our experiments demonstrate that MAGNet outperforms existing motif- based models in terms of structural diversity and performs competitively on established benchmarks. We argue that hierarchical approach. such as the one adopted in MAGNet, is crucial for effectively leveraging scaffold representations. However, modifications to this approach could be promising for future advancements building upon our proposed method.
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a
Split into motifs M [oydes, junctions, chains)
Molecule
Remove
Identify scaffolds
Eagrrentaton pocedurr
ao
248
348
*+8
Boog
Roco
2
roup amod
a00 o8
21 distinct motifs
11 distinct scaffolds
b
1 scaffold
multiple similar motifs
Figure 6: (a) Examples of the fragmentation procedure. Starting with the entire molecules, the fragmentation first removes leaf nodes f. continues to split into motifs M, and then identifies distinct scaffolds S. (b) Example for single scaffold that has multiple similar representations in terms of atom and bond types, illustrating how our scaffold abstraction can reduce combinatorial explosion and enable smooth learning.
A ABSTRACTION TO SCAFFOLDS AND DETAILS ON PROPOSED FRAGMENTATION
Fig. 6 a shows examples of the proposed fragmentation and abstraction to scaffolds. First, we identify leaf nodes L and then divide the core molecule C into structurally distinct fragments M that can be categorised into rings, chains, and junctions. Note that adjacent scaffolds share join node v E T instead of being connected through bond. This representation of connectivity between fragments is advantageous compared to the "Breaking Bridge Bonds" decomposition (Jin et al., 2020; Maziarz al., 2022), as the separation of motifs, such as rings and chains, does not require truncating the chain. Given this fragmentation, atoms can simultancously be part of a ring and chain, and MAGNet accounts for that.
Fig. 6a illustrate that many fragments share the same topology but differ in the atom and bond types. Extracting a scaffold S, from its motif M, means to reduce the typed adjacency to its binary connectivity, discarding any node features. After creating a vocabulary using all unique scaffolds from the dataset, we can check their isomorphism by comparing their hashes (Leman Weisfeiler, 1968).
By this abstraction, MAGNet can learn smoother transitions between different scaffold representations. Fig. b showcases simple example of this: the shown motifs share very similar sets of atoms and bonds, as well as their underlying structure, but they differ in the exact positions of atoms and bonds. Fragment-base based methods would be required to replace the motif token entirely, having choose its replacement from a potentially large vocabulary. By disentangling structure from features, we enable MAGNet learn such transitions smoothly.
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B DETAILS MAGNET
B.I ENCODER
We build the node features that are processed in MAGNet'ss encoder from different attributes, see Tab. 3. We include the atom type "'ttomid_ddim"
its charge ("atom_charge hirrehharrrrdm)....... as well as its _dim'). ). We proceed accordingly for the scaffold level and include the scaffold id ( ('scaffold iididddii dim"), its multiplicity chemical features ('motif feat dim') ) computed through RDKit (Landrum & others, 2013). Since the latter are not learned during training, the features are mapped to the specified dimensionality by linear map.
multiplicity value Paaommmmltiphle (ippiiitty_imm)
SScaffold_mmllipliinn
_dmm'dm), ) as well
After processing the resulting node features through the graph transformer (Shi et al., 2021) with 'num_layers_enn -annsynnnnny layers, they are aggregated in different ways and mapped to specified dimen- sions as defined by cec_<>>_dim for the atoms, scaffolds, join nodes, and leaf nodes, respectively, On top, the scaffold embeddings are additionally processed with the same transformer architecture moum_ayeers layers_scaffold..... "ndddddee"" to inform the embedding about the scaffold-level connectivity. We then concatenate the resulting graph-level embeddings and further combine them with global molecule features, again computed via RDKit and then mapped to the required dimension ('enc global dim') ), before mapping them to the latent space via the latent module which has 'num layers latent' -many layers.
Table 3: Parameter configuration of the best MAGNet runs.
Parameter
Value
Parameter
Value
Train
batch size
64
Model
node aggregation num Jayers Jatent
sum
flow batch_ttiizzi
1024
Ir
3.07 x 10-4
num layers enc
2
Ir _eccc flow
0.9801
num layers scaffold ene
4
x 10-3
num layers. hgraph
flow Ir deccc
0.99
flow patience gradelip
13
loss loss_ weightsiht
joins
leaves
Model
latent.dim enc_atom_cd
25
100
motifs
enc scaffolds dim
25
hypergraph
enc.joins .ii leaves dim enc global dim hiom_id_dim
25
25
25
beta, annealing
max
0.01
25
init
0
dim config
atom_charge .ch.rgeeeeee. diim dim atom multiplicity dim
10
step
0.0005
10
every
2500
scaffold lid dim
35
start
2000
scaffold multiplicity..comm dii
10
motif_feat feat_dim scaffold hidden scuffold gnn dim
50
256
128
mootif_seq.pos_diie Icaf hidden latent_flov flow .hidden
15
256
512
B.2 DECODER
All decoding steps are conditioned on the latent code 2. From 2, MAGNet employs two transformer decoder layers to autoregressively decode the set of scaffolds S by selecting tokens S; from the extracted scaffold vocabulary. Generation of the variable-sized sized multiset S ends with selecting stop token. In the next step, an MLP predicts the connectivity A matrix between individual nodes in permutation-invarin manner. This prediction is solely based on the learnable scaffold token and multiplicity embeddings. Indicating multiplicity is required, as multiple scaffolds in S share the same scaffold type but have to be connected in different ways.
At this point, MAGNet instantiates the atom-level graph by expanding scaffold tokens to their untyped graph objects. These graphs without features are then first assigned atom types by transformer decoder layers. Subsequently, the atom features and the respective scaffold embeddings used by MI.P
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MAGNet
Large Cycles
Large Junctions
Test
PS-VAE
MoLeR
MiCaM
x 1001 4 10-1
x 10
100
Relative Displacement
Figure 7: Displacement between latent representation of the input vs. the decoded outpur.
to assign bond types independently for every edge, thus creating M. For any connection Aig between two motifs M; and Mj. another MLP then determines the join matrix ((k.), that is used identify the shared join node which is then "collapsed". This process applies only to atoms of the same type, subject valency constraints, and has adhere the predicted join node type Aij-
After constructing the core molecule C, MAGNet creates meaningful node embeddings by employing graph neural network the core molecule. These embeddings the basis for final module consisting of transformer decoder layers that equips the core molecule's atoms with leaf nodes C. By the definition the leaf nodes, every core molecule's ''s atom can only have one leaf node. leaf node prediction includes the node's ' atom type and the bond type connecting it to its attachment atom in the core molecule. The predicted bond type again subject valency constraints.
B.3 ANALYSIS OF ACTIVE UNITS IN THE LATENT SPACE
Formally, the VAE optimises the ELBO
L= Eewo [P =)] + 3DL (Q(z ()) P)
with P~1((111)
where the posterior Q(2 g)) regularised towards the Normal prior P. In practice, finding a balance between the reconstruction loss Ezzo [P [ =)] and the KL-divergence DEL challenging. Yeung et al. (2017) and Burda et al. (2015) observe that optimising this objective learning to collapse several units to the prior to compensate for few non-Gaussian components that support reconstruction. Behaviour like this be measured through the number of active units in the latent space, defined as Covg ( (Eg~Q(= (x)) > 0.02 (Burda et al., 2015).
result in the VAE
Due to generating the entire molecular context each generation step, MAGNet heavily relies the latent representation; also, our reconstruction experiments Sec. 5.1 support this. However, this intended behaviour requires the approximate posterior Q(= 9) to be close to the Normal prior P to allow for good-quality samples. Although there are several methods available to improve the alignment between the approximate posterior and the prior, such as latent dropout (Yeung al., 2017), cyclic 3-annealing schedule (Fu et al., 2019), and the GECO loss (Rezende & Viola, 201 8), none of them have been able to achieve rate of active units over 50 % beyond simple weighting of the DRL term. As result of this analysis, we fitted normalising flow to the VAE, which was trained with low KL regularisation. For this, we follow the framework Conditional Flow Matching (Tong et al., 2024; Lipman et al., 2023) and achieve 100 % active units.
B.4 MAGNET: HYPERPARAMETER AND TRAINING
Training MAGNet for one epoch takes around 30 minutes single 'NVIDIA GeForce GTX 1080 Ti'. We trained MAGNet for 30 epochs and fitted the latent normalising flow post-hoc for 5000 epochs in total and conducted random hyperparameter sweep including the learning rate, beta annealing scheme, and the number of layers for the encoder and latent module. The MAGNet model reported in the main text has 12.6 6 M parameters and its configuration is depicted in Tab. 3. In its
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MAGNet
MoLeR
PS-VAE
GM9
CHEMBL
MiCaM
GuacaMol
L1000
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Mean Tanimoto Similarity
Figure 8: Additional quantitative evaluation shows that MAGNet is faithful to its latent code. From this follows decoding consistency even challenging cases and on unseen datasets.
current version, MAGNet processes roughly 70 molecules per second during training and samples about 8 molecules per second during inference.
C DISCUSSION ON GENERATION APPROACHES
Zhu et al. (2022) categorise graph generation approaches into One-Shot and Sequential models based on how the latent code mapped to the final graph. Models like GraphAF clearly fall into One-Shot category, while MoL.R fits within the Sequential category. However, models such as DiGress, PS- VAE, and MAGNet do not match this classification perfectly.
According to Zhu et al. (2022), sequential models "generate graph consecutively in few steps." conditioning on a partial graph, i.c. complete parts of a molecule, at each step. Importantly, PS- PS-VAE and MAGNet do not follow this scheme. Even though they generate set of motifs autoregressively using an RNN or Transformer, they never generate a graph conditioned on partial structure.
One-Shoi models, defined by Zhu et al. (2022), "generate new graph represented in adjacency matrix with optional node and edge features in one single step," first generating the set of node features and subsequently the set of edge features. This approach aligns closely with the strategies employed by PA-VAE and MAGNet. The discrepancy arises due to the autoregressive manner in which these models generate the of motifs.
Parallel to Zhu et al. (2022), Yang et al. (2022) propose similar terminology, categorising models into All-At-Once, Fragment-based (attaching fragments to a partial molecule), and node-by-node (attaching nodes to partial molecule) methods. Under this classification, DiGress is considered an All-At-Once model, while VS-VAE categorised as a Fragment-basse given that PS-VA VAE does connect any fragments until the final prediction step. Therefore, both MAGNet and PS-VAE would more appropriately fall into the All-At-Once category under this definition, while relying a fragment-based or scaffold-basee vocabulary.
method. This is inconsistent
To address these inconsistencies, we classify PS- PAE, MAGNet, and DiGress as One-Shot models, since they do not condition on a full but noisy graph). In each generation step, the module that generates motifs is invoked only once and does not depend on other modules like the bond-generating component.
partial graph during generation (with diffusion models conditioning
D ADDITIONAL EXPERIMENTS
D.1 ABLATION ON LARGER VOCABULARY SIZE
In the experiments conducted in Fig. 3c and b, we compare MAGNet to the top-k vocabulary models MoLeR and VAE. To ensure fair comparison. we limit all models to vocabulary size of 350. We conclude from this experiment, as well as e.g. Fig. 3a, that models cannot generate uncommon scaffolds, if they are not in their motif vocabulary and need to resort constructing them from single
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Scaffolds
Scaffolds
MAGNet
MAGRe:
PS VA-VAE
PS WAE
MolleR
Acccc.
MoLaR (2003)
(2000)
MiCaM
MCCM
Uncommon Scalfolds
Uncommon Scalfolds
MAGNet
MAGiee
PS VAE
PS-VAE
MaLail
MoLaR
Cydlic
MoLeR (2003)
MoLeR (2000)
Acycic
MiCaM
MIC.M
0.00
0.30
0.75
Ratio Training Distribution
Reconstructec Scoffaldds
Figure 9: Left: percentage of reconstructed scaffolds. Right: comparison of sampled scaffolds to their frequency in the training distribution. A ratio of is optimal.
MoLeR
MAGNet
Rimegepant upproved
Zuranolon (FDA appreadd 2023)
Amoxicillin
Figure 10: Qualitative examples of the reconstruction of FDA drug-like molecules.
atoms. To investigate whether model with just significantly larger vocabulary can alleviate these shortcomings, we additionally conduct analysis with the MoLeR vocabulary trained with vocabulary of size 2000 Fig. 9. Interestingly, we can observe that a larger vocabulary does help the MoLeR model in sampling However, when investigating the model's ability to reconstruct uncommon scaffolds, even MoLeR with larger vocabulary falls short. This indicates that uncommon scaffolds might be sampled at random during unconditional generation but the vocabulary becomes too large to navigate in the case of explicit signal to decode certain scaffold.
uncommon scaffolds in better accordance with the training distribution.
D.2 DECODING DRUG-LIKE MOLECULES
Following up the limitations of current molecular generators discussed in Fig. 1, Fig. 10 demon- strates that MAGNet does not display the same difficulties in decoding complex scaffolds.
D.3 FULL BENCHMARK RESULTS
In addition to the results the GuacaMol distribution learning benchmark results in Tab. 1, we provide the full benchmark results in Tab. 4.
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Table Metries obtained by the MOSES and GuacaMol benchmarks for all evaluated models. We highlight the best method in each category.
ChaNAAE
SM-LSTM
GmahAFF
HierVAE
MiCaM
JTVAE
MoLeR
PSVAE
MAGNNet
DiGress
FCD/ (4)
189 1M
0.50 E:
14.9 104
3.52 1:
2.49 +: ti
1,67
1.28 0:35
6.57 9:2
1.50 =: e:11
2.79 0.16
SNN/T (1)
0.29 on
0.40 2:
0.25 2:
0.36 uue
0.34 1: exc
0.34 2: 0.00
0.36
0.00
0.28 4: 6.0
0.34 4: 0.00
0.32
:: 0.00
Frag/T
0.85
100
0.84
0.56
0.97 +
0.98
0.98
0.78
0.99
0.97
Scal/T (t)
0.17
mm
0.21
0.37
0.34 tm
0.24
0.34 6:02
0.20
0.38 a:
0.25
InDiv (7)
0.83 om
D.77
m00
0:95 + om0
0.87 pm
0.87 + cine
0.86
0.87 0:00
0.89 8:00
0.88 9:0000
0.87 0.000
InDiv2 (t)
0.83 uB
0.56 3
0.91
0.86
0.87 1:
0.86
0.86
0.88
0.87 4: 0.00
0.86
(?)
legP (C)
2.24 b-t
0.12 mm
0.41 + om0
0.36 6.1
0.20 + p00
0.28 + 0.09
0.13 +: 6:00
0.34
0.22 + 0:24
0.40
0.00
SA (1)
0.42 on
0.04
1 MMC
0.58 3:
0.20 è 0.44
0,51 d: 0.00
0.34
0.06 2: 0:31
1.18 0.85
0.12 2:
0.38
::
QED (1)
0.03
0.01
0.03
weight (4)
32.9 624
2:45 108
96.9 439
18.6 2:
$1.1 1: 1:35
2,50 0.88
5,96 1.08
2:34 38.3
14.5 8.45
27.5 0.94
Valid (f)
0.09 om
0.96 +: pm
1.00 +
1.00 +
1.00 + 500
1.00 +
1.00 +: 0:00
1.00 +:
1.00 + 9:00
0.85 =
Unique (1)
1.000
0.99
Novelly (1)
0,95 wm
0.98 1 10D
0.91 mm
0.96 w0
0.98 tuc
1.00 0.00
1.00 2: 0.90
1.00 0.00
1.00 9:00
0.99
KL div. (1)
0.63 0.6
1.00 3
0.67 2:
0.92 om
0.94 +: 1.0
0.94
2: 0.00
0.98
=: 0:00
0.83
4: 0.00
0.95 4: 0.00
0.91 0.00
FCD
0.11 013
0.95 1
0.05
0.53 6.4
0.63 1
0.75
0.80
0.28
0.76 0.00
0.65
To provide further details on the results presented in Tab. 2, we provide the achieved scores for all evaluated methods for each of the GuacaMol goal-directed benchmark targets Tab. 5.
Table 5: Results for the individual tasks of the GuacaMol goal-directed benchmark.
Gradient Ascent
MSO MICAM
MAGNET
MICAM
MOLER
MAGNET
MOLER
Albuterol simalarity
0,508
0.425
0.485
0.725
0.563
0.775
Arpipruzzle similarity
0.622
0.558
0.585
0,768
0.712
0.841
Mestranol similarity
0.362
0.372
0.307
0.443
0.458
0.469
C11H24
0.389
0,369
0,348
0,816
0.634
0.671
C7H8N202
0.360
0.388
0.372
0.837
0.536
0.935
C9H10N202PFCI
0.515
0.461
0.479
0,787
0.665
0.743
ONS MPO
0.992
0.984
1,000
1,000
Cohimetinih MPO
0.858
0,830
0,835
0
0.899
0.895
Resofenadine MPO
0.734
0.759
0.758
0,786
0.821
Osimertinib MPO
0.832
0,810
0,831
0.855
0.852
0. 8.868
Physchem MPO
0.652
0,598
0.573
0.813
0.725
0.796
Proglitazone MPO
0.637
0.571
0.403
0.878
Runolazine MPO
0.206
0.177
0,200
0.798
0.730
0.764
Median
0,210
0.151
0.149
0.306
Celecoxib rediscovery
0.235
0.273
0,232
0.414
0.317
0.449
Thiothiseee rediscovery
0.262
Troglitazone rediscovery
0.1
0,233
0,219
0.261
0,268
0.336
QED
0.907
0.899
0.916
0.946
0,946
0.948
TPSA target: 150.0
0.821
0.868
0.894
1,000
0.984
1.000
logP target. -1.0
0.954
0.887
0.935
1,000
0.995
1.000
logP target:
0.636
0.723
0.591
1,000
0,923
0.993
D.4 DISPLACEMENT OF LATENT CODES
To quantify the discrepancy between input and reconstructed molecule visible in Fig. 3a, we measure the displacement of latent codes in Fig. 7. That is, we obtain the latent representation for the input molecule, decode this latent representation into the output molecule and then obtain the latent representation for the output molecule. This verifies what can be observed qualitatively in Fig. 3a-the evaluated baselines can not reliably decode complex scaffolds.
D.5 INTERPOLATION
Extending on Fig. 5, we additionally provide examples for latent space interpolation in Fig. 12. During interpolation, MAGNet stays faithful to the scaffolds present in the input molecules. The last row shows a failure case of MAGNet: it identifies a scaffold multiset that can not be fully connected to molecule.
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Table 6: GuacaMol and MOSES Benchmark for ablations of MAGNet
KL (t)
InDiv (t)
logP
(4)
QBD (U)
MAGNet
0.76 om
0.95 aM
0.22 uum
0.12 a.m
0.01 1:
NF
0.65
0.92
0.88
0.38
0.24 =000
0.01
Binury
0m
uce
200
D.6 TRANSFERARILLTYY OF SCAFFOLDS VTA ZERO-SHOT RECONSTRUCTION
We calculate the Tanimoto similarity in the reconstruction setting for variety of datasets, Fig. 8. For all evaluated datasets, MAGNet achieves the best similarity scores between molecules, highlighting the transferability of scaffolds across various distributions.
compute the Tanimoto scores only for those molecules that can be represented via the scaffolds that were extracted from the ZINC dataset. For the QM9 dataset, MAGNet can represent roughly 75% molecules in the dataset. This is due to unseen scaffolds which make up around 11% out of the total number of 289,966 scaffolds. For GuacaMol, MAGNet can represent around 97% of the molecules in the dataset. Out the 9,562,028 scaffolds in GuacaMol, only 0.5% are missing from the scaffolds vocabulary extracted from the ZINC dataset. We consider fragmentation into scaffolds that is more flexible and translates even better across datasets important future work.
D.7 MAGNET ABLATION STUDIES
We show additional results for ablations different parts of the MAGNet model in Tab. 6, performing the same analysis done in Sec. 5.2. MAGNet without normalising flow achieves an FCD score of 0.65, leading to a performance decrease of more than 14%. A similar decrease can be observed for MAGNet with only binary scaffold connectivity A. This result further verifies that the type of a join node j is an important conditioning for the generation of motifs M.
D.8 HALLUCINATING SCAFFOLDS
In Fig. 3 we compare the ratio between generated scaffolds and scaffolds in the dataset. However, often, the evaluated models generate scaffolds that never appear in the dataset. We refer to this behaviour as "hallucination" and quantify this observation in Fig. 11. We can observe that both PS-VAE and MiCaM often generate scaffolds that are not part of the training data and sometimes also MoLeR constructs out-of-distribution scaffolds via single atoms. By design, MAGNet samples distribution
D.9 GOAL-DIRECTED MOLECULE GENERATION
We evaluate MAGNet well baseline generative methods the GuacaMol goal-directed benchmark Brown et al. (2019). It aims to test a model's ability to explore the chemical space
2784
Number Hallucinated Stalfold
427
23
PS-VAE MCCM
Figure 11: Number of scaffolds that generated sampling time but do not appear in the dataset.
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Table 7: Additional results for the GuacaMol goal-directed benchmarks. All scores should be maximised. We report mean and standard deviation and highlight the best method in bold.
MAGNet
MoLeR
MiCaM
Random
Isomers
0.81
0.81
0.56
0.17
MPО
0.89 0.01
0.88 0.01
0.85 + 0.01
0.61 + 0.22
Property
0.99 0.00
0.99 0.00
0.93 4: 0.00
0.34 0.30
Redise.
0.35
Similarity
0.56 0.05
0.60 + 0.06
0.47 + 0.06
0.26 0.08
through a variety of single and multi-objective optimization tasks. We employ two latent optimisation mechanisms and evaluate all models under the same setting. In Tab. 2, we train proxy regressor consisting of simple MLP with default parameters Pedregosa et al. (2011) for each of the required score functions subset of 10, 000 labelled samples. The proxy regressor maps from latent space embedding to the predicted score for that embedding. During optimisation, we then utilise gradient ascent procedure find latent code with high predicted score. Across models, we use 100 gradient steps per sample with a learning rate of 0.01. We consider this latent procedure an especially indicates what extent the latent space is
important means of evaluating the generative models, organised smoothly with respect to certain properties.
Additionally, we also employ "Molecular Swarm Optimisation" as second technique for goal- directed generation Winter et al. (2019). We use configuration of one swarm per sample initialised with 50 particles and we run the optimisation procedure for rounds. We report these results in Tab. 7. We can observe from these results that especially MoLeR benefits from the random noise introduced by this latent procedure. Nevertheless, MAGNet performs competitively or better on almost all evaluated tasks.
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0.80 0.20
10.10
0.56 1020
0.221 6:33
0.14 00.37
MoLeg
MAGGN
0.26
Molel
MAGNEL
0391 6.8
026 1031
0:1 6.35
923 18.
0.25
0.300..33
033 1855
MoLeR
MAG
0.10
817
MOLeR
MAGNEL
1038
0..00.20
822 10.3
0.22 0.38
9:99 10.30
Figure 12: We provide four interpolation examples for MAGNet and MoLeR. The input molecules
(left and right) shared between the two models. We report the Tanimoto similarity as a rough estimate for the interpolation's 's goodness.
25


Reviews:
[{"review_id": "V8kdbFf5ag", "reviewer": "Reviewer_hxc4", "summary": "This paper proposes a new way to address chunk-based molecule generation. Instead of using fully specified molecular subgraphs (motifs) similarly to prior work, the authors instead abstract out motifs to their connectivity skeletons, which allows for a smaller vocabulary to cover a wider range of possible motif realizations. The authors then show a factorization of the generative procedure that first builds the scaffold by assembling these motif skeletons and then gradually fills in the atom features. The approach is verified on standard generation and optimization benchmarks, showing decent performance.", "questions": "See the \"Weaknesses\" section above for specific questions.\n\nIf my questions/concerns are addressed, I would consider raising my score.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "(S1): The authors address an important problem of molecule generation, and identify a reasonable gap in the capabilities of prior models that they then try to address. The proposed approach is motivated clearly. \n\n \n\n(S2): The paper is generally well-written. The experiments are conducted across various setups that are common/relevant in this domain. While the empirical performance isn't across-the-board amazing, MAGNet seems to be a generally capable model with good performance overall, while improving on top of previous models in some settings, as well as enabling new capabilities (e.g. more ways to condition the generation on partial information).", "weaknesses": "(W1): The authors argue the most direct comparison is to other OS models, and focus on that angle. Setting aside the fact that MAGNet is not purely one-shot as the scaffold multiset `S` is decoded sequentially and autoregressively, even if we agree MAGNet is OS, is there any inherent value that comes from being an OS model? One thing that comes to mind would be faster inference, as stepwise generation models can be expensive due to repeatedly encoding the current partial graph. So, is MAGNet more efficient than sequential decoding models, e.g. MoLeR? \n\n \n\n(W2): More interesting conditioning settings depicted in Figure 5 could be explained in more detail. The partial molecule induces a partial scaffold multiset `S`; do you then use this partial multiset and continue generating to get a full multiset, then connect the scaffolds while forcing those of the connections that are implied from the conditioning? I assume extending the multiset `S` with further scaffolds cannot directly take into account the fact that some of the scaffold connections (or scaffold instantiations into specific motifs) are already known from the conditioning, because during training the scaffold multiset extension subnetwork assumes only a multiset of generic scaffolds is known. Could this be an issue causing the model to add scaffolds that don't fit well with the partial molecule? \n\n=== Nitpicks === \n\nBelow I list nitpicks (e.g. typos, grammar errors), which did not have a significant impact on my review score, but it would be good to fix those to improve the paper further. \n\n- Line 147: Denoting the join node as `j` could be an index clash given that the scaffolds being considered are denoted as `i` and `j` earlier in the sentence. \n\n- Line 155: \"factories\" -> \"factorize\" \n\n- Line 309: missing space\n\n=== Update 26/11 === \n\nAfter the author rebuttal I decided to raise my score from 6 (borderline accept) to 8 (accept).", "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": "G9ySh84QaS", "reviewer": "Reviewer_Ry7i", "summary": "The paper presents MAGnet, a generative model for molecules. MAGnet is based on scaffolds (an abstraction of molecular fragments without atom and bond information, just the graph structure), which are introduced to factorize of the molecule distribution. MAGnet is a VAE-like architecture which generates new molecules by first predicting a scaffold set and its connectivity from latent space, then by predicting the atom and bond types for each scaffold, and finally by adding leaf nodes. Experiments show good performances in comparison with several baselines, on standard benchmarks such as GuacaMol and MOSES.", "questions": "I'll be honest here. I really liked this paper and I was going to recommend clear acceptance until I understood that MAGnet was an AR model \"disguised\" as an OS model. From that point onward, I couldn't shake the feeling that it was framed as OS only because, if put in the AR category, the results would become not so impressive (although still good). I really hope this was an unintentional mistake or misinterpretation by the authors. That's a shame in my opinion, since I believe the proposal is original and the evaluation was very thorough, the technical side of this paper is almost flawless. \n\nI will be recommending rejection of this paper for the moment, because in this form too many claims made in this paper stem from an incorrect premise. However, I am willing to hear from the authors why they consider MAGnet one-shot instead of sequential and will re-evaluate whether I could reconsider my judgment after the rebuttal phase.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 2, "presentation": 4, "contribution": 2, "strengths": "- using scaffolds instead of motifs qualifies as an innovative proposal, which could be of value to the community\n- experimental evaluation is extensive in both depth (many baselines) and width (two benchmarks)", "weaknesses": "throughout the paper, MAGnet is considered a one-shot (OS) molecule generator, when in fact it is auto-regressive (AR). Appendix B.2 states that the set of scaffolds is generated auto-regressively, so I cannot understand how it could be considered one-shot. According to the definition by Zhu et al. (2022):\n\n\n    Sequential generation refers to generating graphs in a set of consecutive steps, usually done nodes by nodes and edges by edges. One-shot generation, instead, refers to generating the node feature and edge feature matrices in one single step.\n\nThis is very different from what it is stated in Section 2 of the MAGnet paper:\n\n    Zhu et al. (2022) categorise the generation process further into sequential methods, building molecules per fragment while conditioning on a partial molecule.\n\nto justify the fact that MAGnet is OS. According to the same paper that is cited, MAGnet belongs to the AR category. If MAGnet is AR, then most claims made in the paper need to be toned down or changed because it falls in the same category as MoLeR, which has usually comparable or better performances than MAGnet. For example, the claim \"MAGnet is the best OS generator\" no longer makes sense.", "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": "trO6NI6lVp", "reviewer": "Reviewer_w2Yn", "summary": "In this work, the authors aim to address a fundamental limitation of fragment-based molecular generators. The vocabulary of such models is defined as the union of common molecular fragments, obtained from datasets of known molecules, and individual atoms, which can be used to re-generate motifs that would not be listed under the available fragments. The authors argue that this choice of vocabulary creates an inherent tradeoff in the expressivity of the model. On the on hand, including more fragments quickly increases the vocabulary of the model, with the number of motif variation increasing exponentially with their size. On the other hand, learning to model missing fragments from individual atoms is a challenging task requiring even more training data and often leading to unrealistic molecular motifs. \n\nAs a solution, the authors propose a coarse-to-fine-grained molecular generation paradigm centred around molecular scaffolds, rather than fragments, as the basic building block of the model's vocabulary. A single scaffold implicitly captures many similar fragments, allowing for a relatively small vocabulary size while retaining expressivity. The proposed model, MAGNet, a VAE-based molecular generator, operates on this multi-level factorisation paradigm, by first sampling scaffolds and only then specifying the atomic composition of the scaffolds, their joints and the leaf nodes as a successive step. They evaluate this approach on several benchmarks against a variety of baselines and show that the proposed method, with its more expressive vocabulary, can reliably generate complex molecular motifs, in addition to allow for latent code optimisation and interpolation.", "questions": "1. My understanding of the experiments presented in Figure 1-a, Figure 3 and Figure 9 carried out (Section 5.1) is that the fragment-based approaches fail to reconstruct complex motifs that are absent from their fragment vocabulary by using atom-based tokens only. In contrast, MAGNet contained these structures in its scaffold vocabulary. Why haven't both methods used the same dataset to construct their fragment/scaffold vocabularies, without limiting fragment-based methods to only the top-K fragments (i.e. including all fragments). In this case, the modeling task from the fragment-based methods using a vast vocabulary of fragments would be more difficult, but the method wouldn't lack these important fragments such as big rings, preventing them from reconstructing specific molecules. Have experiments on the baselines been carried out by varying the value of \"k\" in the top-k fragment-based vocabularies to see how vocabulary size trades-off with learning complexity? It seems to me that this might be a point where a more appropriate tuning of the baseline's hyperparameters would make a difference. And if the main advantage of the proposed factorisation is that it removes the need for such tuning when constructing the fragment vocabulary, it would be interesting to discuss these considerations in the paper.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "In general, I found the paper interesting and very well written. It clearly identifies a limitation of current fragment-based molecular generators in terms of expressivity, and present a well motivated solution based on a novel factorisation paradigm. I believe this is a good paper which brings significant contributions to fragment-based molecular generation approaches and for this reason I recommend that the paper should be accepted. I suggested some clarifications (see below) which I think would increase the clarity of the paper and complement the discussion on the limitations of the method and the general positioning of fragment-based and one-shot graph molecular generators.\n\nA few specific notes:\n1. The Related Work section is exhaustive.\n2. The proposed method is clearly introduced and detailed. In particular Figures 2 and 6 effectively convey the hierarchy supporting this paradigm.\n3. The experimental section is well structured and the authors compare to a large array of prior work and using several public datasets. The results effectively support the main claims made in the paper. In particular, the results presented in Figure 3, Figure 4 are compelling.", "weaknesses": "1. One of the main limitation of fragment-based methods is the absence of synthesizability considerations in the framework design. This significantly limits the applicability of such methods since, to be tested in physical and biological assays, the proposed molecules either require individual and expansive custom synthesis plans or have to be replaced by available analogs, thus drastically under-utilizing the expressivity of the model. It would be interesting to further discuss the limitations of the proposed method w.r.t. synthesizability in the manuscript.\n2. On line 371: \"We do not report Novelty and Uniqueness, as almost all evaluated models achieve 100% on these metrics.\" if this claim is made, then the numbers should be presented (at least in Appendix). Same for the mention right after: \"For the baselines DiGress, SM-LSTM, and CharVAE, which are not able to achieve 100% Validity, we sample until we obtain 10^4 valid molecules\", it could be informative to include these numbers in appendix (validity rate for each method).\n3. It would be useful to specify a bit more clearly how benchmarking on Guacamol is executed (lines 301-304 and 309-311). Is the model trained on ZINC and then evaluated on some reference set defined by Guacamol (Chembl)? Or is the model trained on Chembl molecules and compared to a test set also defined in Guacamol? While the provided references are useful, the description of the experiments should be standalone in the paper.\n4. In the goal-directed evaluations, it would be interesting to compare MAGNet with methods specifically aimed at goal-directed molecular design such as RL and GFlownet based methods.\n\n### Elements worth clarifying\n\n1. Figure 1 could be made clearer by further explaining parts a), b) and c) in the caption.\n2. The factorisation from graph to scaffold graph, and scaffold graph to molecular graphs, described in Section 3, would be clearer with a supporting Figure.\n3. The main baselines (PS-VAE, MoLeR and MiCaM) could be described in greater details.\n4. I found the discussion on novel conditioning capabilities very interesting (lines 469-476), however, even with this in mind, it is not clear to me what Figure 5 is showing or how it supports these claims. I think this figure could be improved to better support this discussion.\n\n### Minor comments:\n\n1. Typo on line 155, factorise*\n2. Line 150: not sure that App C.6 is the intended link here (or how it relates to the sentence)?\n3. Typo on line 302: to evaluate*\n4. Error in Figure 3: based on the text and the caption itself, it seems to be that the graph columns B and C are mixed up in Figure 3.\n5. I did not find the caption of Table 1 very natural to read. I would suggest simply specifying in parenthesis what underline and bold mean in the table, as opposed to underlining and bolding their description in the caption.", "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": "gTJJlnKpm7", "reviewer": "Reviewer_8uqV", "summary": "This paper addresses a critical limitation in substructure-based molecular generative models: the inability to capture the structural diversity in molecular space due to missing complex structures from the motif vocabulary. The authors propose a novel approach that employs a structural scaffold vocabulary, leaving atom and bond types to be predicted by the model. This approach is intended to enrich structural diversity, with specific metrics introduced to highlight the advantages of the proposed method.", "questions": "1. \nCould the authors provide the **validity** of generated molecules? Are there specific metrics used to ensure that generated molecules are fully **connected**, given that some examples (such as in the bottom row of Figure 11) suggest molecules with broken structures?\n\n2. \nA comparison with baseline methods in terms of **generation speed** would add valuable context for practical deployment. \n\n3. \nCould the authors clarify the **vocabulary size** difference between your approach and baseline models?", "limitations": "", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The paper tackles a significant issue in 2D molecular generation, and Figure 1a provides an illustrative example that emphasizes the limitations of current motif-based methods in generating novel molecular structures. This emphasis on structural diversity is both timely and impactful for the field.\n\n\n2. Experimental results demonstrate an increase in generated structural diversity.", "weaknesses": "1. **Limitations in Generating Novel Molecular Structures**\n\n1.1 A primary concern is whether the proposed solution truly resolves the problem of generating novel molecular structures. As highlighted in Figure 1a, the generation of new substructures—particularly complex fused ring systems—appears challenging. Both the original motif approach and the scaffold motif proposed in this work seem constrained in their ability to construct unseen complex fused rings because they are hard to piece together using substructures in the vocabulary.\n\n1.2 Furthermore, while the scaffold motif approach can potentially reduce vocabulary size, it still requires additional specification of bonds and atom types on rings, which might affect the validity of atom valence of the generated molecules. \n\nThus, while the problem raised is compelling, the method appears to partially address, rather than fully resolve, this issue.\n\n2. **Writing**\n\n2.1 Overlap in Sections 3 and 4: Sections 3 and 4 present overlapping content, which could benefit from a clearer delineation. Specifically, Section 4 should focus more on detailing the network structures (architecture) within each module, and provide an illustration of the model structure. The high-level probability descriptions, already covered in Section 3, could be streamlined here.\n\n2.2 Figure Captions: In Figure 3, the captions for panels (b) and (c) appear reversed.\n\n2.3 References: The authors should thoroughly review the reference list for consistency. It is full of preprint references.  Accepted papers should reference their final publication locations rather than preprints (e.g. MiCaM and ShapeMol, etc). Please also check with repeated links and unusual endings with paper abbreviations (e.g. G-SchNet, JODO, etc.).\n\n3. **Experimental Results**\n\nWhile structural diversity has been enhanced, some important metrics, such as FCD and SA, appear to drop. Additionally, there is no mention of the validity rate of generated molecules in the table 1.", "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 (Spotlight)