TABASCO: A Fast, Simplified Model for Molecular Generation with Improved Physical Quality
Authors: Carlos Vonessen
Charles Harris
Miruna Cretu
Pietro Lio
Venue: Accepted by TMLR ()

Abstract: State-of-the-art models for 3D molecular generation are based on significant inductive biases: SE(3) equivariance, permutation invariance and graph message‑passing networks to capture local chemistry, yet the generated molecules struggle with physical plausibility.
We introduce TABASCO which relaxes these assumptions: The model has a standard non-equivariant transformer architecture, treats atoms in a molecule as sequences and does not explicitly model bonds. The absence of equivariant layers and message passing allows us to simplify the model architecture and scale data throughput.
On the GEOM‑Drugs and QM9 benchmarks TABASCO achieves state-of-the-art PoseBusters validity and delivers inference roughly 10x faster than the strongest baseline, while exhibiting emergent rotational equivariance without hard-coded symmetry. 
Our work offers a blueprint for training minimalist, high‑throughput, unconditional generative models and the resulting architecture is readily extensible to future conditional tasks.
We provide a link to our implementation at https://github.com/carlosinator/tabasco.

Published in Transactions on Machine Learning Research (02/2026)
TABASCO: A Fast, Simplified Model for Molecular Generation with Improved Physical Quality
Carlos Vonessen*
cronesseneell..ck
ETH Zurich
Charles Harris
och57@com. ac. uk
Miruna Cretu"
uk
Pietro Liò
pl21 12219@0ummccmm ac. uk
University of Cambridge
Reviewed on OpenReview: https: openreview. net/ forum? id= Kg6CSSbbb
Abstract
Slate-of-the-arl models for 3D molecular generation based significant inductive biases: SE(3) equivariance, permutation invariance and graph message-passinng networks to capture local chemistry, yet the generated molecules struggle with physical plausibilityy We introduce TABASCO which relaxes these assumptions: The model has a standard non-equivariant transformer architecture, treats atoms in a molecule as sequences and does not explicitly model bonds. The absence of equivariant layers and message passing allows us to sim- plify the model architecture and scale data throughput. On the GEOM-Drugs and QM9 benchmarks TABASCO achieves state-of-the-ar POSEBUSTERS validity and delivers infer- roughly 10x faster than the strongest baseline, while exhibiting emergent rotational equivariance without hard-coded symmetry. Our work offers blueprint for training mini- malist, high-throughput, unconditional generative models and the resulting architecture is readily extensible to future conditional tasks. We provide a link to our implementation at https://githut. htt.:/aa....siittrroraaaaaa.a...
1
Introduction
1.0
TABASCO
spicy
In recent years, there has been growing interest in us- ing diffusion models as generative methods for molec- ular design (Du et al.
-hot
0.9
PoseBusters Validity
2024
Schneuing et al. 2022
-mild
SemlaFlow
Hoogeboom el al. 2022
V Vignac et al. 2023 Dunn &
A EQGAT-diff
ADIT
Koes 2024 Irwin et al.
2025): Much of the literature
converges on design principles believed be essential for high-quality molecular generation. First, models are typically SE(33--quivariis
symmetry prior
0.7
that serves as strong inductive bias (Hoogeboom ct al. 2022). Second, message-passing graph neural networks (GNNs) are widely used to capture many- hop, context-dependent interactions between atoms (Hogobooo et 2022 Schneuing et al. [2022] Irwin al.
FlowMol
0.6
10M
100M
# Params.
2025 Dunn & Koes 2024 |Schneuing al.), Third, recent work emphasises flow-matching objectives that rely
making them permutation-invarie
Figure 1: PoseBusters on GEOM-Drugs. We show the trade-off between model size and POSEBUSTERS performance for TABASCO and baselines.
"Core contributor.
Work done visitor the University of Cambridge.

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Published in Transactions on Machine Learning Research (02/2026)
coupled optimal transport (OT) (Tong et al. 2023) incorporate heavily structured, domain-infrrmed priors (Dunn & Koes 2024 Irwin et al. 2025 Despite incorporating these inductive biases, current models
continue to struggle with physical plausibility, often failing to produce chemically coherent structures or
accurately recover fundamental features of protein- ligand binding (Buttenschoen et al., 2024 Harris et al. 2023
In parallel, growing body of work explores scaling up simpler model architectures, most notably Transformers VVaswani et al.) 2017 across adjacent domains. A prominent example is AlphaFold3 (Abramson et al. 2024 which achieves strong performance
physical plausibility benchmarks (Buttenschoen et al.) 2024) despite omitting many of the conventional inductive biases, including equivariance. Similarly, recent generative models for protein backbone design have demonstrated competitive results with minimal architectural complexity, provided they are scaled appropriately (Geffner et al. 2025). Simplification model architecture and removal of inductive biases for conformer generation has also proven successful (Wang et al., 2024). In parallel this work, (Joshi et al. 2025) explore using non-cquivariant latent diffusion for generating small molccules.
In this work, aim to distill the core components of Liffusion-based molecular generation and ask: Which concepts necessary to build perfo-ner models? We introduce TABASCO (Transformer-based Atomistic Bondless Scalable Conformer Output), scalable model that achieves state-of- the-att-rrtttt performance on unconditional molecular generation benchmarks. Our contributions are as follows:
(i) State-of-the-art physical quality on GEOM-Drugs and QM9. TABASCO surpasses previous models in POSEBUSTERS validity, and achieves a 10x speed-up during sampling (Sections 4.3 and 4.4
Greatly streamlined approach. Our model omits both explicit bond modelling, equivariant layers and permutational invariance, and instead utilizes standard Transformer, simusoid encodings, and random rotations of data points (Section
(iii) Study of model behaviour without explicit symmetry. Under this new approach, we study the behaviour of the model with respect to permutational invariance and SE(3) equivariance.
(a) We find evidence that breaking permutational invariance with positional encodings significantly improves performance by helping distinguish atoms during carly denoising (Section 4.5)
(b) We find that completely removing random rotations and SE(3) equivariance constraints has negligible impact on performance (Section 4.6p.
(iv) Plysically-constrainnd last-mile guidance. We introduce an separate distance-bounds guidance algorithm that further improves POSEBUSTERS validity without requiring force-field-basee relaxation or additional parameters (Section 5).
2 Background and Related Work
2.1 Flow-Matching Models
Flow-matching (FM) is a generative modelling framework that learns to transport samples from a source distribution (e.g., noise) to a target distribution (e.g., data) by directly estimating the time-dependent velocity
field of a probability flow (Limman et al., 2023 Albergo & L'anden-Eijnden 2022
Given a pair of samples x1) from source and target distributions, one defines a continuous interpolation x, = (1 f)xo fx1, and target velocity 4 X1 Xp- A neural field (x(x,, t) is then trained to match this velocity using the mean squared error:
CFM Ex,(x0.x1) [1 Ut
(1)
Flow-matching enables efficient generation via deterministic integration (c.g., using an ODE solver), and has been shown improve sampling speed and stability over score-based diffusion models Dunnn & Koes 2024
Irwin al. 2025
2

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Linear
Embedding
Cross-Allention Layer
Coordinate
Mult--Head Attention
Transition Leyer
Cross-Amention Layer
Atom Type MLP
Time Fourier Encoding
Transformer
Sinusoid
Encoding
Figure 2: Top: Interpolation between noise and data. Bottom: TABASCO model architecture. æ and denote coordinates and atom types, respectively.
2.2 Generative Models for 3D Molecule Design
Early works used standard continuous diffusion processes coordinate and atomic features, where bond connectivity was determined by chemoinformatics software [Hoogeboom et al. 2022 Schneuing et al. 2022 This process often resulted in low-quality conformers that were not fully-connected or violated atomic valences. MiDi (Vignac et al., 2023) improved on this by applying discrete diffusion to both the atom types as well as generated a full bond matrix end-to-end, which significantly increased stabilty and bond connectivity. EQGAT-diff (Le al., 2023) explored the design space of equivariant diffusion models, creating a custom attention-based equivariant architecture allow for interaction between continuous and discrete features.
Further work introduced more advanced model architectures (Morehead & Cheng 2024 Hua et al. 2024 additional losses (Xu et al. 2024), alternative transport strategies (Song et al. 2023), and geometric latent diffusion (Xu et al. 2023 Joshi et al.) 2025). FlowMol (Dunn & Koes 2024) and SemlaFlow (Irwin et al.,
2025) use flow-matching for generation of coordinates, atom types and bonds. Both methods proposed new architectures and showed great improvements speed versus diffusion based approaches.
3 TABASCO: Fast, Simple, and High-Quality Molecule Generation
Overview and Motivation Our goal in this work is to identify the simplest possible model architecture that can generate physically realistic small molecules at scale. Our motivation stemss from the observation that recent progress in protein structure generation has demonstrated the surprising power of non-equivariant
Transformer architectures when scaled appropriately (Abramson et al. 2024 Geffner et al. 2025 Wang et al, 2024). Based on these results, we began our experiments with a deliberately stripped-down, non-oquivariant Transformer backbone for molecular generation. We also chose exclude explicit bond information from the model. While many existing models treat bonds as distinct modality (Irwin et al., 2025 Dunn &
Koes 2024 Vignac al. 2023), in line with earlier work (Schneuing et al. 2022) hypothesised that if coordinate generation is sufficiently accurate, bond information can be successfully imputed with standard chemoinformatios tools. This perspective allowed us to further simplify the architecture while focusing on improving conformer quality. Physical realism, as measured by POSEBUSTERS validity, was the primary metric guiding design decisions. Modules and heuristics in the approach that did not contribute to this metric were pruned, resulting lean, fast, and extensible model that maintains strong performance without relying on specialised architectural components.
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3.1 Model Architecture
In contrast to most prior work in unconditional molecular generation, we adopl a simplified non-equivariant Transformer architecture (see Figure 2 without self-conditioning, Atom coordinates and types are jointly embodded along with time and sequence encodings and are passed through a stack of Transformer blocks. We add a single cross-attention layer for each domain and process these outputs MLP heads for atom types and coordinates. We report resulting performance and model ablations Section 4.3
3.2 Training Objective
We optimize coordinates with Euclidean conditional flo--macching (CFM) (Tong et al., 2023 Albergo Vanden-Lijnden (022) and atom types with discrete CFM which is paramebrized based on the Discrete Flow Models (DFM) framework (Campbell et al.] 2024 Concretely, consider molecule with N atoms, ground-truth coordinates x1 and atom types where the noise is distributed with ~~ (0, I). Noisy atom types de are obtained by interpolating between atom type probabilities and sampling from a categorical distribution a₃ ~ Cat (t 8(ai) + (1 - t) R), where 8()) creates a one-hot encoding (Campbell et al. 2024). During training the model takes x and a, and learns predict the endpoint of the trajectory. The continuous coordinate objective becomes
Coordinates are partially noised with x xx +
Lmmetri Eegt
N (x1)(x.,
x x1112
(2)
The discrete atom lype objective is the cross-entropy loss
Laiscrete (a) = Eg Σ log(2
(3)
We combine these into multi-objective formulation with weighing factor Adiscrete E (0, (0,1),- as
L a) Limetrie + Adiscrete Laisereto(a))
(4)
During training we sample from Beta(a, 1), where a is a hyperparameter we ablate in Appendix B As ->1 the model's behaviour approaches the identity function, due to the chosen endpoint formulation. Inspired by Le al. (2023); [Salimans & Ho (2022) and ensure the model can still learn precise atom placement even losses approach zero as t 1, weigh the loss with B(t) (xotal ax) based the sampled time 1, with
B(1) = min 100, 1)2
(5)
3.3 Sampling
We generate molecules with TABASCO by simulating system of coupled stochastic differential equations:
dx; = vl (x,, dd + g(() ax) + Pp: R2 (x,, Pt
a,
d2g()),
where Pt describes the probability of each atom type time t. We estimate the velocity with v, X from the models endpoint prediction &, at time i, and the score with 8, 1-t tv.-x: We refer the reader to
I-t
Geffner et al. (2025) and [Campbell et al. (2024) on which we base our coordinate and atom type sampling strategies, for more in-detail discussions this approach. To improve sample quality, we apply logarithmic discretization scheme on [0, with more fino-grained steps near the end of denoising. We also scale the St and the Gaussian noise component dW, by g(), setting it to zero
(see Appendix B
4

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Published in Transactions on Machine Learning Research (02/2026)
Figure Sampled molecules from TABASCO
3.4 Ordering Atoms as Sequences
Transformers operate in a bag-of-tokens fashion unless provided with additional information about the absolute relative positions of those tokens. Unlike text or protein sequences, small molccules lack natural linear ordering that reflects their 3D structure. While formats such as SMILES and InChI offer consistent ways to linearise molecular graphs, the ordering in these representations does not strictly correspond to spatial proximity. However, the SMILES ordering deterministically derived, typically via a depth-first traversal starting from canonical root atom (Weininger et al. 1989). which still imparts some semantic structure onto the linearized sequence. In practice, many neighbouring atoms in the SMILES string also spatially chemically proximate in the molecule. Accordingly, we include sinusoidal positional encodings based on the atom indices in the SMILES sequence. We hypothesisc that this implicit locality helps the model distinguish aloms during denoising when atom coordinales contain limited in information and thus create molecules with higher physical validity. We ablate the effect of the positional encodings in Section 4.5
4 Experiments
4.1
Training Setup
We train TABASCO on GEOM-Drugs (Axelrod & Gomez- Bombarlll 2022), a dataset of 1M high-quality conformers of drug-like molccules. We use the splits from Vignac et al. (2023) and, following Irwin al. (2025), discard molecules with more than 72 heavy atoms from the training dataset, accounting for 1% of the data. During testing, we sample the number of atoms from the distribution of molecule sizes in the test set, which was left unchanged. We separately train and evaluate our model on QM9 (Ramakrishnan et al. 2014), which is an emumeration of all physically plausible molecules with up to nine atoms. Since the molecules in QM9 are small in size and GEOM-Drugs considered to be significantly more challenging datasct, most results are reported for models trained on GEOM-Drugs, except for Table 3
We train three TABASCO models at three sizes: Both TABASCO-mild (3.7M params.) and TABASCO-hot (15M params.) were trained two 80GB A100 GPUs for 36 hours at learning rate of 0.001. TABASCO- spicy (59M params.) was trained on the same resources for 72 hours with learning rate of 0.0005 (sce Appendix D. During training, we augment each batch with 8 random rotations of the same molecules to improve equivariance. We apply an Exponential Moving Averaging (EMA) with decay strength 0.999 to the model weights, which we ablate Scction We compare our models against EQGAT-diff (Le al.
2023), FlowMol (Dunn & Koes 2024), SemlaFlow (Irwin et al. 2025), and ADiT (Joshi al., 2025 A Appendix
(sce
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Table 1: Physical validity results for all models. A molecule is only POSE overall if it passes all subtests. Higher better on all metrics. DDue to computational constraints, we evaluate statistics on GEOM-Druge on a random subset of 20K training molecules.
Model
Overall
Counected
Bond Lengths
Bond Angles
No Clashes
Rings Flat
Double Bonds Flat
Internal Energy
GEOM-Druge"
0.94
1.0
1.0
1.0
1.0
1.0
EQGAT-diff
0.84
0.91
0.94
0.94
0.91
0.94
0.93
0,90
FlowMol
0.84
0.68
0.81
0.81
0.78
0.81
0.50
0.80
SemiaFloo
0.94
0.91
0,94
ADET
0.57
0.96
0.96
0.95
0.94
0.98
0.98
0.95
TABASCO-mild
0.85
0.96
0.94
0.94
0.87
0.96
0.96
0.94
TABASCO-bot
0.98
0.98
0.98
0.98
0.97
TABASCO<spec
0.92
0.97
0.97
0.97
0.92
0.97
0.97
0.97
4.2 Evaluation Metrics
We employ POSEBUSTERS as our main metric for mcasuring conformer quality, as its array of tests are designed to test for physical plausibility (Buttenschoen et al. 2024). A molecule is POSE only if it passes an array of tests, inchuding: all atoms are connected, valid bond lengths, valid bond angles, no steric clashes, flat aromatic rings, flat double bonds, and the internal energy comparable to reference conformers. This stricter evaluation necessary, since in existing generative models for 3D molecule generation, most other metries have been saturated [Irwin et al. 2025
We further evaluate generated molecules on several well established metries: (i) Validity: Whether a molecule can be sanitized with RDKIT, (ii) Novelty: Whether the canonical SMILES of the molecule is not present in the training set, (iii) Diversity: Tanimoto similarity of molecule fingerprints, (iv) Strain Energy (Harris et al. )023): Energy of the molecule compared to low energy conformers, (v) Root Mean Square Deviation (RMSD): The averagod distance between the atoms of two molocules when comparing molocules in our guidance experiment, (vi) Jensen-Shannon Divergence (JSD): We extract several features from generated molecules and reference molecules and test how much the features distribution deviates from the reference distribution. The extracted features are: bond lengths, bond angles, dihedral angles, frequency of a given bond pair or triplet, average bonds per atom type, number of rings per molecule, and bond type (see Appendix
4.3 TABASCO Results Across Metrics
Results on Physical Validity. Our physical validity results are shown in Table ! and generated example molecules are shown in Figure g TABASCO-spicy (59M) and TABASCO-hot (15M), surpass all prior methods in physical plausibility, raising the POSEBUSTERS validity from the previous statc-of-the-art of 0.88 to 0.92 (see Figure D. Interestingly, most of the gain is achieved by the 15M parameter TABASCO-hot variant, with only modest improvements from further scaling to 59M, suggesting diminishing returns beyond this point. This may be explained by the fact that in GEOM-Drugs only 94% of molecules are POSE.BSTTERS-valln Alternatively, also highlight that increasing returns to scale might become more apparent with an larger training budget. Farlier models such as Flo which perform well on traditional metrics (see Table 2 show significantly lower physical validity (0.64), further highlighting the need for domain-aware evaluation such as POSEBUSTERS.
The tests contained in POSEBUStERS also reveal which ways TABASCO improves physical validity of generated molecules compared previous methods: Notably, in contrast to most previous methods, TABASCO does not explicitly model bonds, but improves connectivity, bond length and bond angle validity. This indicates that sufficiently precise coordinate modelling suffice to accurately impute bonds post-hoc. We also observe that ADIT and TABASCO, the only non-oquivariant models, achieve significantly higher validity scores for steric clashes, flat aromatic rings, and flat double bonds compared to the equivariant models.
Results on Traditional Metrics. Table 2 shows a summary of results on traditional generation metrics. We observe that all variants of TABASCO maintain strong molecular diversity and novelty (~ 0.89), indicating
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Table Results on established metrics for GEOM-Drugs. We generate 3K molecules for each method.
Due to computational constraints, we evaluate statistics on GEOM-Drugs on random subset of 20K training molecules.
Method
# Params.
Validity
Novelty
Diversity
Strain Energy /
Time (s)
GEOM-Drugs
1.0
0.0
0.90
EQGAT-diff
12M
0.91
0.94
0.90
360.19
4310.94
FlowMol
4.3M
0.81
0.81
0.91
34.20
362.22
SemlaFlow
22M
0.93
0.93
0.91
18.20
201.22
ADIT
150M
0.98
0.97
0.91
46.36
521.21
TABASCO-mild
3.7M
0.95
0.93
0.89
21.32
5.9
TABASCO-hot
15M
0.98
0.93
0.88
14.16
10.67
TABASCO-spicy
59M
0.97
0.90
0.89
15.07
19.77
TABASCO-spicy w/ guidance
59M
0.97
0.92
0.89
19.23
131.80
that performance improvements do not compromise sampling breadth and generalisation. We note that, inline with previous works, for all evaluated methods Novelty and Diversity are computed over all molecules, including molecules that do not pass physical validity, which may lead to invalid molecules distorting the scores both methods. Table 2 also shows that that the time required to sample from TABASCO at least 10x lower than for any previous method, and up to 100x faster than some prior baselines, offering a practical advantage for large-scale or iterative workflows. Appendix [ we additionally compare the JSD of several features of generated molecules and GEOM-Drugs molecules for TABASCO-hot (15M) and three baselines.
4.4 Performance Comparison on QM9
We further train and evaluate our model on the benchmark dataset QM9 (Ramakrishnan et al. 2014 and compare the performance to previous methods in Table 3 We observe that TABASCO achieves state-of-lhe-at POSEBUSTERS scores, although showing low novelty scores on QM9.
Table 3: Results on the QM9 Datasct.
Method
# Params.
Validity f
Novelty 1 Diversity f
PoseBusters
Strain Energy
FQGfA-diiff
12M
0.99
0.99
0.89
0.94
9.10
FlowMol
4.3M
0.97
0.97
0.92
0.92
17.81
SemlaFlow
22M
0.99
0.99
0.89
0.95
4.69
TABASCO-mill
3.7M
0.98
0.31
0.91
0.98
2.31
TABASCO-ho
15M
0.99
0.32
0.92
0.99
3.32
w/o pos. encodings
15M
1.00
0.34
0.93
0.93
17.10
4.5 Breaking Permutational Invariance
In Figure (right) observe that introducing sequence positional encodings yields higher quality molecules compared to treating atoms in bag-of-words fashion. In Table B we observe that this performance gap is much smaller for models trained QM9 compared to TABASCO explanation for this is that the much smaller number of atoms makes it easier to distinguish atoms and place them with respect. to each other even without positional encodings. Additionally, in Figure we show examples of failure modes we observed repeatedly in models without positional encodings.
GEOM-Drugs (see Figure 4 right). A possible molecule compared to GEOM-Drugs
Disentangling the Effect of Positional Encodings We hypothesize that the difference in generative quality when adding positional encodings may stem from early steps molecule denoising, when the atomic coordinates very noisy and positional encodings can provide signal about the relative positions of

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PoseBustes Validty
PoseBlsters Veldity
0.8
0.8
Rositional Encading Sinusoid
0.5
Rositional Encoding
Sinusaid
Nore
0.
0.0
0.4
0.
Denoising start
Model Size (M parameters)
Figure 4: Left: 15M parameter model with/witheg positional encodings, POSEBUSTERS when starting denoising from different noise levels on test molecules from GEOM-Drugs. Right: Model performance across parameter scales with with/without without positional encodings when trained on GEOM-Drugs.
SMILES Ordering
Random Ordering
Figure 5: Importance of atom ordering in TABASCO. Molecules from SMILES-ordeeed atoms with sinusoidal encodings (left) are coherent and valid, while random ordering (right) produces fragmented, implausible structures, showing SMILES' inductive bias for local structure during carly denoising
atoms in molecule. To test this, sample from two 15M parameter TABASCO-model,, one trained with sinusoid encodings and one without any encodings. We partially noise molecules to different t E [r,1) and denoise these molecules using the models starting from that point. Figure S shows how as T increases, the performance difference of the models decreases and switches near the end of denoising. This suggests that the sampling trajectorics in the model with positional encodings differ from those the model is trained on (sce Appendix B). Furthermore, the higher POSEBUSTERS validity of the positiionllenccodnng--f the end of denoising suggests that in later stages of denoising its sampling trajectory is well aligned with training trajectories. Figure 4 also indicates that the permutationally invariant model is able to create high quality molecules when the final atom positions become apparent from its noisy coordinates. This implies that ionalional encodings may be especially important in helping distinguish atoms at high noise levels when coordinates contain little information.
model Lowards
Table 4: Ablation study on TABASCO models trained on GEOM-Drugh showing impact of training without postional encodings, batch augmentations or random rotations.
Configuration
Validity
Novelty
Diversity f
PoseBusters 1
TABASCO-hot (15M)
0.98
0.93
0.89
0.91
w/o positional encoding
0.93
0.93
0.91
0.70
w/o batch augmentations
0.98
0.94
0.88
0.89
w w/o random rotations
0.98
0.94
0.89
0.90
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10
10-1
10-2
Eequiv
10-3
Configuration
10
Standard
W/o Positional Encoding
W/o Batch Augmentations
10
W/o Rotations
0.0
0.2
0.4
0.6
0.8
1.0
Figure 6: Relative equivariance error across noise levels for TABASCO-hot (15M) when trained without positional encodings, batch augmentations or random rotations.
4.6 Analyzing Equivariant Behaviour
Quantifying the Equivariance Error. We evaluate the quality of TABASCO's equivariance, as this is encoded into the architecture. Similarly to previous work, we mcasure the deviation of the models prediction under random rotations (Karras et al., 2021 Bouchacourt et al., 2021). To control for numerical inaccuracies during sampling, rather than measuring the equivariance of fully denoised molecules, the equivariance of the endpoint predictions of the model by partially noising molecules to different t E [0, 1]. Given noisy coordinates X₃ at time f, random rotation R, and a function that at any timestep predicts the endpoint &1 = f(x;, I), we randomly rotate the input and apply the inverse rotation to the output, i.e. Z(x,, t, It) RT t). {
measure
We estimate the relative equivariance error with
Z(x,,,, R) [| t, Rt)l
Eequiv = Varr
We normalize the endpoint prediction per atom within random rotations of the same molecule account for changes in scale during the sampling process and differing vector magnitudes. In an equivariant model one would have 7(x,, t, R) fx,,,t), t), which would trivially yield Cequiv 0.
Ablating the Equivariance Error. During standard model training approximately enforce equivariance by randomly rotating the input data and further augmenting the training batch with additional rotations of the same samples. We study the equivariant behaviour the model by training four 15M TABASCO-hot models with slight modifications: (i) Standard model training, (ii) We train the model without positional encodings, (iii) We apply independent random rotations to all molecules the batch, but do not augment it with additional random rotations of the same data, (iv) We train the model without any random rolations, ie. the model always the same coordinates for the same molecule. We train the models with the same compute budget as previously allotted: two A100 GPUs over 36 hours.
We compare the observed performance in Table and visualize the equivariance error over time in Figure G The results show how in the standard configuration, the model approximates an equivariant function with an error of less than 1% for most t, and as denoising progressos the model further roduces the equivariance error of its predictions. The positional encoding-free free model approximates equivariance similarly well, however, observe differences of up to order of magnitude compared the standard model at certain noise lcales.

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Simultaneously, randomly rotating data, but omitting intra-batch augmentations with further random rotations, does not worsen the equivariance error, but slightly hurts POSEBUSTERS performance. From this we conclude that intra-batch augmentations do not improve model equivariance, but may improve training, possibly bocause higher-quality gradient steps induced by the random rotations.
Finally, Figure and Table show how completely omitting any random rotations during training leads model with high POSEBUSTERS score that has a significantly higher equivariance error than all other models, often larger than 10%. From this conclude that, while (approximate) equivariance is often desirable property, generative model does not require any notion equivariance to generate physically plausible molecules.
5 Physically Constrained Last-Mile Pose Guidance
Existing 3D molecule generators yield globally sound conformations but struggle with local stereochemical checks such as POSEBUSTERS, Most violations stem from coordinate drifts near the end of the sampling trajectory (t (+++1 1). We therefore frame pose refinement as a last-mile problem and introduce a lightweight, differentiable guidance step that enforces simple physical distance bounds without force-field evaluation or relaxation.
Distance bounds matrix. For every atom pair we pre-compute lower and upper bounds Lij. Uisl over 1-5 bond separations, analogously to how POSEBUSTERS computes bounds on valid bond lengths and angles using RDKr::
Lower bound Lyy: sum of van-der-Wasd radii minus 0.1 A;
Upper bound Unt cumulative covalent bond lengths along the shortest path in the molecular graph.
These numbers match the Universal Force Field (UFF) limits but are looked up from static table. Here no CFF energy computation is performed.
Two-phase sampling with distance-bounds guidance.
1. Free denoising. Run the standard sampler until t 0.99, obtaining noised conformation (X0.99, (o.99).
2. Guided refinement. In each remaining denoising step, convert the endpoint predicted coordinates to an RDKIT conformer and look up the physical bounds on atom pair distances [Lijs Uty] for each distance pair dy ||x1, Xt,ll-- The loss on physical constraints is computed with
(di - Uns)² dij Uij.
Lphys (xt)
Σ i2j
< Laj, (di Lnj) dij otherwise.
0.
We back-propagate through the network and apply one gradient step to the inpuls:
Pcphys
X Xx Ophyy Ix.
If the molecule decoded at t 0.99 is not RDKrT-valid, guidance is applied to the sample.
5.1 Effect of Physically-Constranned Guidance
In the last row of Table we compare TABASCO with guidance all evaluated bascline models. In Table compare physically-constrainee guidance to UFF relaxation of unguided molecules: In all experiments
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Table 5: Effect of distance-bounds guidance on POSEBUSTERS validity and runtime (single A100) on TABASCO-hot (15M) for 1000 molecules. RMSD is evaluated with respect the unguided bascline. UUFF calculations were performed on an M3 MacBook Pro.
Method
PosHBUSTERsT Strain Energy &
Diversity t
RMSD
Runtime +
Baseline
0.91
14.16
0.88
10.67
w/ UFF
0.94
4.74
0.88
0.226
14.21
w/ Constr. UFFY
0.93
11.15
0.88
0.084
23.42
w/ guidance
0.94
19.23
0.89
0.132
75.65
the same molecules are denoised identically up to 0.99. In one experiment we allow for unconstrained relaxation and in another introduce movement constraint of 0.1Á on each atoms original location. We choose Ophys 0.01 in all experiments. Table 5 shows how distance-bounds guidance improves POSEBUSTERS validity, while preserving diversity and slightly increasing strain energy. Although distance-bounds guidance increases sampling time due to sequential bound computation and backpropagation, overall sampling remains faster than in prior approaches. The method also largely preserves atom positions compared to unguided baselines and preserves diversily, since the molecular hepothesis is mostly fixed by t= 0.99 and the model is only guided to create a more physically plausible conformer the same molecule. We highlight that practice, our last-mile guidance and UFF target different objectives physical plausibility vs. energy minimization and that guidance is superior in that it yields better POSEBUSTERS validity while better retaining the model's predicted conformation. In Appendix E we provide a more extensive comparison of model performance across parameter scales with and without guidance. The approach is model-agnostic and applies to any diffussion- or flow-based 3D generator that exposes gradients with respect to alom coordinates. Unlike force-conditioned molecular-mechanic. samplers such as DiffForce (Kulyté et al. 2024h, which require full molecular mechanics gradients at every reverse step, our method uses pre-tabulated distance bounds and needs no energy evaluation or additional learnable parameters.
6 Discussion
Our findings align with recent trends toward simpler architectures: Although SE(3) equivariance often considered essential, our non-equivariant model learns equivariant representations up to small errors and achieves state-of-the thr- performance on physical plausibility benchmarks, suggesting that enforced symmetries may be restrictive for some generation tasks. Our theoretical understanding of the approximate equivariance remains limited, but we are able to verify that empirically the model exhibits very low equivariance error. Our stripped-down architecture is easily extensible and only models coordinates and atom types explicitly and encodes atoms with sequential positional encodings. Conversely, omitting explicitly modelled bonds can limit conditioning when aiming to enforce valences or bond types (Peng et al.) 2024), and encoding molecules with SMIT.ES-based posítional encodings may introduce systemic biases. Simultaneously, our model yields ten-fold spoed improvement compared to previous methods, potentially making practical applications like large-scale virtual screening more feasible in the future. We also observe that only minor performance improvements emerge at scale unlike for previous work other molecular modalities (Abramson et al. 2024 Geffner et al. 2025), which may point to the necessity of larger training budgets or may indicate the model is close to saturating the training dataset PoST of 0.94. Physically-oonstrainee guidance is shown to be effect ive for improving physical plausibility with minimal modifications, however compared to traditional methods like UFF Relaxation, based on chemoinformatics heuristics, remains more expensive and converges to higher strain energies.
We provide more detailed analysis of the models limitations in Appendix
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7 Conclusion
In this work we present TABASCO, a non-oquivariant generative model for 3D small molecule design that exhibits enhanced scalability and performance on physical plausibility compared to baselines. We study the importance of positional encodings for small molecules, investigate the emergent equivariant properties of our model and the effects of scaling the model to large sizes. We hope that our model serves as a compelling example of how minimalist architectures can be effectively applied to molecular design and that our code base acts as extensible tool for integration in drug-discovery workflows, for example through conditioned generation relevant modalities or RL-based property optimization.
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A Comparison to Previous Work
For all compared baselines we sample 1000 molecules with three random seeds on an A100 GPU. We report averages over the three runs.
We evaluated EQGAT-difT using the official codebase GitHut and the checkpoints linked there. We used the example evaluation script, which we edited to save molecules as outputted from reverse sampling, without any post-processing.
EQGAT-diff
FlowMol We used the official implementation for FlowMol and the linked checkpoints on GitHut and sampled molecules using the default script. For both GEOM and QM9 (Ramakrishnan et al. 2014 we benchmarked againts the CTMC-based models.
Semla Flow We evaluated SemlaFlow using the sampling script and model checkpoints from Git We modified the sampling script to save all outpuls from the model, as opposed to only valid molecules.
ADiT We benchmark ADIT by evaluating the molccules provided in the paper's GitHub repository, accessed on June 28th 202 When directly generating molecules from the ADIT model checkpoint we observed worse values on all metrics. A likely explanation for this taht the publicly available checkpoints are from an independent reproduction and do not correspond the reported values in the paper, as stated in the public repository. As result, we opt for the metrics computed from the publicly available molecules.
B Ablations
Time Distribution During Training. Based on success in previous works, we choose the dett-distri for sampling the time during training (Irwin al. 2025 Geffner al., 20255. We investigate the effect of different values for the training time distribution Beta(o, 1) and ablate three values in Table G. We observe significant changes in performance at sampling time when shifting the probability weight assigned to different. times t E [0, 1] during training, and empirically find that Beta(1.8, 1) yields the best results for our purpose.
Stochasticity Ablation. We investigate several choices for g(t) in Eq. G based on the approach in Geffner let al. (002)). Throughout this work, except when explicitly stated otherwise, we set g(t) to zero for t >>> 0.9 allow for precise placement of atoms towards the end of sampling. Table 7 compares the effect of four possible stochasticity functions. We observe that except for g(t)
performance is very similar across all metrics. We trace the contrast between g() O and the other parameterizations to difference in sampling
trajectories and show comparison in Figure 7 In contrast to the trajectory of g(t) = 0 which is consistent
'tttps://github.
hattps://github. www///llec//ggattttoooooooo _iff// MIT License
MIT License 3 Axeerod,, Simon, al. "GEOM, energy-annotated molecular conformations for property prediction and molecular generation."
Sci Data 9, 185 (2022). Available under CCO 1.0.
'nttps://github. http:/ttw..oom/eeerrr///aaaaa--- httpp////gthbbbbcmmaaaacaaooeeee -tt.daidiaaaoooddiiiiioooooooo
MIT License
6 hhtner//wwggnneeeee////iiiiihiid
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Table 6: Ablation of a in the training time distribution Beta(a, 1) on TABASCO-hot (15M) trained on GEOM-Drugs.
a
Validity
Novelty
Diversity t PoseBusters f
1.5
0.96
0.92
0.89
0.84
1.8
0.98
0.93
0.88
0.91
2.0
0.97
0.93
0.88
0.89
Table 7: Effect of four possible g(t) paramcterizations on TABASCO trained on GEOM-Drugs. For all t>> 0.9
we set g(() = 0 and use = 0.01.
g(1)
Validity
Novelty
Diversity 1
PoseBusters f
0 0.96
0.95
0.90
0.83
0.98
0.93
0.88
0.91
0.97
0.93
0.89
0.91
1-f
0.98
0.94
0.89
0.89
w/o positional encodings
0
0.94
0.93
0.91
0.69
0.89
0.87
0.91
0.26
with the training trajectory, the rest of the g(t) functions have very large magnitudes close to = 0, which empirically leads first to explosion and then collapse of the atom vector magnitudes. In the collapsed state atoms are roughly arranged in sequence and slowly grow into the finished molecule as t - (see Figure 7) This sudden rearrangement and growing into the finished molecule appears to yield better final molecules compared to when following the training trajectories more closely. This may also help explain the dip in POSEBUSTERS validity during partial molecule noising of TABASCO with positional encodings in Figure In contrast to this, we observe in Table that this explosion and collapse behaviour leads to much worse molecules when positional encodings are not added the model, possibly because in the collapsed state atom coordinates almost identical and become very hard to distinguish without positional encodings.
Number of Steps at Sampling. We investigate the effect of reducing the number of sampling steps on molecule quality and ablate over several choices in Table We observe that as little as 40 steps are necessary for TABASCO-hot to outperform previous methods on POSEBUSTERS. We further observe that additional steps have no effect on molccular quality.
Table 8: Number of steps at sampling for TABASCO-hot (15M) trained on GEOM-Drugs. We additionally evaluate connectivity, which denotes the fraction of fully connected molecules.
# Steps
Validity f
Novelty 1
Connectivity
PoseBusters'
10
0.99
0.98
0.00
0.00
20
1.00
0.99
0.00
0.00
30
0.98
0.97
0.99
0.81
40
0.98
0.94
0.99
0.91
50
0.99
0.95
1.00
0.91
100
0.98
0.94
1.00
0.91
200
0.98
0.93
1.00
0.89
500
0.98
0.94
1.00
0.91
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Figure Snapshots of the sampling trajectorics for two different molccules sampled from TABASCO-hot (15M) trained GEOM-Drugs. The upper trajectory sampled with g(l) and the lower one with
g(t) 2+0.00
1.0
0.9
PoseBusters Validity
0.8
0.6
0.5
g(t)
g(t) = ामद
0.3
0.01
10
50 100
200
500
White Noise Scale
Figure 8: Comparison of valdiv across noise scales 7 with different g(t). In contrast to all other comparisons we set g(t) = 0 only beyond > 0.95 to further augment the effect of adding noise.
Noise Scaling. We investigate several values for 7 to ablate the effect of noise scaling. In Figure g we compare POSE for different noise scales and different gt) parameterizations. We observe that molecular quality remains high over several noise scales, and then collapses for g(() = Z+E
Training Components. We ablate three training components in Table 9 for the 15M-parameter TABASCO- hot model on GEOM-Drugs. Removing weight-EMA has almost effect: all headline metrics change by < 0.01 and diversity rises slightly. This shows that the model does not rely EMA for chemical geometric correctness. Performance is more sensitive to the coordination between input and output coordinate features. Eliminating the single cross-atiention block lowers validity and novelty by ~0.04 and, critically, drops POSEBUSTERS validity to 0.80. This indicates that coupling atom-type and coordinate information is nccessary to resolve physical validity constraints such as steric clashes and strain at this parameter scale. Positional encodings also prove critical, as investigated in Section 4.5 Without them, raw validity remains high (0.93) but POSEBUSTERS validity collapses to 0.70, revealing widespread geometric artifacts. The model
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still generate chemically plausible graphs, yet struggles to arrange them in physically realistic 3D space (see Figure 5
Table 9: Ablation study of model performance when removing components. Layer counts are adjusted to match model size where needed. Higher values are better.
Method
Validity
Novelty
Diversity
PoseBusters
TABASCO-hot
0.98
0.93
0.88
0.91
w/o EMA
0.98
0.93
0.89
0.91
w/o cross-attention
0.94
0.89
0.89
0.80
w/ w/o positional encoding
0.93
0.93
0.91
0.70
C Bensen-Shanmer Divergence Comparison
Table 10:
Divergence on several extracted molecule features. For each method 3,000 molecules were tested on GEOM-Drugs. Lower is better on all metrics.
Model
Bond Length
Bond Angle
Dibedral
Freq. Bond Pair
Freq, Bond Triplet
Bonds Atom
Num, Rings
Bond Type
SemlaFlow
0.1502
0.0677
0.0664
0.1135
0,0384
EQGAT-diff
0.2334
0.1178
0.0824
0.0440
0.0507
0.0677
0.0124
Foo
0.1897
0.1233
0.1202
0.1350
0.0889
TABASCO-bot
0,3589
0.1538
0,0892
0.061
0.0737
0,0713
0,0808
0,0119
In Table 10 we evaluate the JSD of several molecule features between the generated and GEOM-Drugs molecules for TABASCO-hot (15M) and three baselines. We observe only minor differences in the JSD of features between all tested methods. EQGAT-diff most closely matches the molecule distribution of GEOM-Drugs on the evaluated features, however, improvements in JSD do not appear clcarly to correlate with performance improvements in physical validity (Table 1 or other metrics (Table 2) This may indicate that the JSD estimate is too noisy the distance to the GEOM-Drugs distribution is too large across all features for improvements to give meaningful insights the quality of generated molecules.
D Extended Details on Models
In this section we further elaborate on model architecture (Figure) give concrete values for relevant hyperparameters in Table I1 and describe the unconditional sampling algorithm in detail in Algorithm I Atom coordinates are encoded with bias-free linear layer that scales to the model's hidden size. Discrete atom types are encoded through an embedding layer, where we model Carbon, Nitrogen, Oxygen, Fluorine, Sulfur, Chlorine, Bromine, lodine and a miscellaneous "*' atom, for all elements in the training set not contained within the previous list. We encode the time t E [0, 1] through a Fourier encoding, and each atoms location within the molecules's 's SMILES sequence through a standard sinusoid encoding. We tried concatenating these four vectors and creating combined hidden representation with an MLP mapping from R hidden dim + hhidden dim but observed no difference in practice to simply adding the vector representations,
and thus opted for this approach. Each transformer block applies layer-none to the activations, then PyTorch multi-head attention, another layer-norm and transition layer, where we include residual connections between the first two and second two components. This output is processed by two parallel PyTorr cross-allention layers one for atom types and for coordinates. Each consists of a self atlf-ntttntion block, a multi-head attention block, where the original combined hidden representation is used as key and value, and a food-forward block. Both outputs are subsequently processed through domain-specific MLPs where the output atom coordinate ML.P is also bias-
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Table 11: Model hyperparameters across different model sizes
Hyperparam
TABASCO-mild
TABASCO-hot
TABASCO-spicy
# Params.
3.711.369
14.795.529
59.082.249
Hidden size
128
256
512
# Transformer blocks
16
16
16
# Attn. heads
8
8
8
Train l distribution
Beta (1.8,1)
Beta (1.81)
Adiscrete
0.1
0.1
0.1
Learning rate
0.001
0.001
0.0005
Optimizer
Adam
Adam
Adam
FMA-weight
0.999
0.999
0.999
Batch size
256
256
128
# Rotation Augs.
Effective batch size
2048
2048
1024
# GPUs Training Duration
2
2
2
36h
36h
72h
# Sampling Steps (t) Y
100
100
100
1+00.01
1++0.0T
1++0.01
0.01
0.01
0.01
Table 12: Comparison POSEBUSTERS validity when adding physically-constrrandd guidance. Evaluated on 1000 molecules on a single A100 GPU,
Method
# Params.
Validity
Diversity 1
PoseBusters
Strain Energy
Time (s)
TABASCO-mild
3.7M
0.95
0.93
0.89
0.85
21.32
5.9
w/ guidance
0.96
0.95
0.89
0.91
26.53
60.86
TABASCO-hot w/ guidance
15M
0.98
0.88
14.16
10.67
0.97
0.94
0.89
0.94
19.23
75.66
TABASCO-spicy w/ guidance
59M
0.97
0.90
0.89
0.92
15.07
19.77
0.97
0.93
0.89
0.94
17.01
131.80
E Further Analysis on Physical Guidance
We provide a detailed description our physically constrained guidance procedure in Algorithm and provide full results on all model sizes in Table [2] We observe the largest improvement in POSEBUSTERS for TABASCO-small, and minor improvements in novelty for all model sizes. Simultaneously we consistently observe an increase in strain energy when applying physical guidance.
F Failure Case Analysis
We study common physical validity failure modes of generated molecules using TABASCO-hot (15M). Figure [0 shows the phi coefficient matrix, quantifying pairwise correlation between subtest failures. Table shows the absolute failure rate of cach subtest. Unsurprisingly, bond length and bond angle violations are strongly correlated, since when local geometry is implausible, both properties tend to be affected. These geometric failures also correlate with internal energy violations, likely because distorted bonds and angles lead higher strain. The most frequent failure mode steric clashes (Table however, this does not correlate strongly with other tests. We hypothesize this is because clashing atom pairs are not bonded, so for example bond-specifie tests do not apply to them.
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X Steric Clashes
X Bond Lengths
X Steric Clashes
X Steric Clashes
X Internal Energy
Bond Angles
X Steric Clashes
X Steric Clashes
Steric Cleshes
Steric Clashes
X Steric Clashes
X Steric Clashes
X Steric Clashes
X Steric Clashes
X Steric Clashes
X Steric Clashes
x Steric Clashes
X Steric Clashes
X Steric Clashes
X Steric Clashes
Figure 9: Twenty generated molecules chosen at random that do not satisfy physical plausibility, each labeled with the failed subtest.
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Connected
1.00
0.06
0.10
0.04
-0.00
-0.00
0.19
1.0
Bond Lengths
0.06
1.00
0.56
0.05
-0.00
0.02
0.22
0.5
Bond Angles
0.10
0.56
1.00
0.09
-0.00
0.02
0.34
Φ coefficient
No Clashes
0.04
0.05
0.09
1.00
-0.00
-0.01
0.07
0.0
Rings Flat
-0.00
-0.00
-0.00
-0.00
1.00
-0.00
-0.00
0.5
Double Bonds Flat
-0.00
0.02
0.02
-0.01
-0.00
1.00
-0.00
Internal Energy
0.19
0.22
0.34
0.07 -0.00
-0.00
1.00
-1.0
Connected
Bond Lengths
Bond Angles
No Clashes
Double Bonds Flat
Rings Flat
Internal Energy
Figure 10: Phi coefficient matrix of physical validity subtests.
In Figure g we show a random subset of failing molecules along with the test that failed. We observe that as shown in Table [ steric clashes are by far the most common failure mode. We also note that while several molecules are visibly flawed, in some cases the violation of physical validity is not immediately apparent. This further highlights the need for rigorous physical plausibility tests such POSEBUSTERS.
G Limitations
The approach described in this work retains several limitations. SMILES-lerived positional encodings improve performance but can theoretically introduce systemic biases, that may limit the model when faced with unusual bond patterns or non-standard chemical structurcs. Furthermore, omitting explicit bond modeling creates a leaner model and simpler training objective, but limits control over valences and bond orders when sampling the model. While TABASCO exhibits emergent equivariance, in some areas such molecular dynamics, where even small equivariance errors can prove problematic, this approximate equivariance may still be insufficient.
We also observe a scaling plateau: performance improvements diminish significantly beyond the 15M parameter scale, with only modest gains observed when scaling to 59M parameters. This suggests that further scaling benefits may require increasingly larger training compute budgets. Alternatively, this plateau may be partially by the training dataset imposing glass ceiling on physical validity which almost saturated by the 15M parameter model (see Table D.. Understanding the precise factors underlying this plateau remains open question.
While we demonstrate that TABASCO learns approximately equivariant representations through data augmentation alone, our mechanistic understanding of this emergent equivariance remains limited. We quantify the equivariance error empirically (Section 4.6), but do not provide a thcoretical charactcrization of how the model internally represents rotational symmetry or why certain training configurations yield lower equivariance than others. Further investigation into the learned representations could yield insights for
20

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Published in Transactions on Machine Learning Research (02/2026)
designing principled non-cquivariant architectures.
The validation presented in this work focuses exclusively on unconditional molecular generation. While the architecture is designed to be readily extensible to conditional tasks such as property-guided generation or structure-based drug design, we have not empirically validated performance these settings. The benefits observed for unconditional generation may directly transfer to conditional scenarios, which often require different architectural considerations and evaluation protocols.
The pyysically-constrainee guidance algorithm serves as a proof-of-concept for boosting the physical plausibility of molecules during sampling without requiring any modifications to training data, model architect ure or parameter scale. Still, this approach is based on optimizing chemoinformatics heuristics for high-quality molecules and dramatically increases sampling times.
ure
Furthermore, while useful to quantify physical plausibility of 3D molecules, POSEBUSTERS cannot capture all aspects of molecular quality, and does not quantify additional very relevant metrics of interest: TABASCO does not address improvements in drug-likee likeness of molecules synthetic accessibility.
Algorithm Unconditional Sampling Algorithm
procedure BUCLIDEANSUPP(Xx; &1, At, ()),))
Vi - x()
8, < ((0) -
dW, + (27g() ((0,
x + (v: + S; + At
return X,
end procedure
procedure DISCRETEELLOSSTEP(C... At)
All indexed ops without loops vectorized
(+(i.))= At = (1)
> P1 consists of softmax-normalize model logits
(i(),)(()) - E (1) (i.j))
> Make r zero mean
Pi+ + [a)())= + r((ij)
az(i) + C:
return a, end procedure
procedure SAMPLEMOLECULE(F. (t)) NN(O,)) T)
+ Categorical
86 ( atom types
for i = to do
Al < 4 4-11
(x1 P1) + EudpointPrediction(f (x, a),
SAMPLINGSTEP
EUCILDEANSTEP(XX, &1, At)
DISCRETEFFLOWSTEPP... 14, At)
end for return (x end procedure
21

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Published in Transactions on Machine Learning Research (02/2026)
Algorithm Flow Matching with Physical Guidance
procedure PRYSICALLUUDAACCEFF (x,, at),t, a) &1,01 < Enddpointrrediction// ax),
3: (1) = argmax, Pı (é,
4: bounds < Get ConalyyyccaCCCnstraaantnss,,,, @1)
D Calls RDKr GetBoundsMatrix(
5: for each atom pair (x, x1,) in x; do
This nested loop is vectorized practice
d,j + 1|x,, x 11
if di bounds min then
Can also regress towards the interval centre
8:
C L + (dij boundsijing?
9.
else if d, > bounds;; ij
max then
10: c f + (di bounds max)3
11: end if
12: end for
13:
sign Cx,C)
The sign-op slightly stabilizes updates in practice
14: return X₁
15: end procedure
16: procedure GUIDEDSAMPLING (Xo, ao), ( &guidance)
17: (x, t (Xg, 0o)
18: for = 1 to N do
19:
At <- t -4-1
20: if f; 2 Éguidance then
21:
x PIYSSCALGUDDANCE(FXZZ. (x,
22:
end if
(x, a) < (AN 24: end for
23:
25: return (x, a)
26: end procedure
22


Reviews:
[{"review_id": "t8WP8dwve5", "reviewer": "Reviewer_Fj87", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper presents TABASCO, a simplified Transformer-based model for 3D molecular generation that avoids explicit $SE(3)$-equivariant layers and relies instead on standard attention mechanisms combined with data augmentation. The authors show that such a non-equivariant architecture can achieve competitive physical plausibility while offering a substantial improvement in sampling speed. The work also provides an extensive set of ablation studies that probe the roles of positional encodings, approximate equivariance, stochasticity, and architectural choices. In addition, the paper proposes a distance-bounds guidance strategy that aims to improve molecular geometry without invoking expensive force-field evaluations.\n\nKey Strengths:\nThe model demonstrates very large speed gains that make high-throughput 3D molecule generation more practical. TABASCO achieves strong PoseBusters performance, particularly when combined with physical guidance. The breadth of ablation studies is noteworthy and reflects careful scientific investigation of architectural components. The overall design is easy to implement and avoids the complexity typically associated with equivariant neural networks.\n\nKey Weaknesses:\nThe comparison with the most relevant non-equivariant baseline (ADiT) is not scientifically controlled, since the evaluation relies on pre-generated molecules whose conditions are undocumented. Several claims regarding applicability to structure-based or pharmacophore-conditioned drug design are not supported by any experimental evidence. The model shows limited scaling behavior, as increasing the number of parameters yields only small performance gains. The proposed guidance technique improves metrics but introduces substantial computational overhead and leads to higher strain energies. Some of the mechanistic interpretations, such as the role of positional encodings in scaffold construction or the emergence of approximate equivariance, are largely speculative and not empirically validated.", "claims_and_evidence_yn": "No", "claims_explanation": "1. The paper repeatedly claims that TABASCO is suited to structure- and pharmacophore-based drug design. However, the submission contains no experiments involving conditional generation, receptor-aware design, pharmacophore constraints, docking, affinity evaluation, or any other downstream task. All experiments are limited to unconditional 3D molecule generation. This makes the claim scientifically unsupported.\n\n2. The comparison with the most relevant non-equivariant baseline (ADiT) is not scientifically controlled. ADiT is evaluated using pre-generated samples produced under undocumented sampling conditions, whereas all other baselines are evaluated via re-runs. As a result, the evidence for TABASCO's relative speed and physical plausibility is neither accurate nor convincing.\n\n3. The paper offers several mechanistic interpretations, such as the idea that SMILES positional encodings facilitate early scaffold formation or that approximate equivariance naturally emerges. None of these claims is supported by targeted analyses such as attention visualizations, intermediate-step evaluations, counterfactual orderings, or theoretical arguments. The explanations therefore remain speculative.\n\n4. The narrative suggests that the architecture scales effectively, yet the experiments show that a four-fold increase in parameters yields almost no improvement in PoseBusters scores. No analyses are provided to explain this phenomenon. Consequently, the claim of effective scaling is not supported and is contradicted by the reported results.", "audience_yn": "Yes", "audience_explanation": "While non-equivariant Transformer approaches have been explored in the protein domain, some readers of TMLR may still find the small-molecule results presented in the TABASCO paper relevant and informative.", "requested_changes": "1. Ensure all baseline experiments (e.g., ADiT) are conducted under comparable conditions, and explicitly report limitations and negative results.\n\n2. Either include conditional generation or pharmacophore-guided experiments, or remove unsupported claims from abstract, introduction, and conclusion. \n\n3. Provide attention visualizations, intermediate step analyses, or controlled ablations to demonstrate how positional encodings and approximate $SE(3)$ equivariance affect generation.", "broader_impact_concerns": "None", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "pUg6nYwvUV", "reviewer": "Reviewer_Hc1X", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The authors present TABASCO, a molecule generative model based on non-equivariant Transformer architecture. TABASCO co-generates atomic coordinates as well as atomic types through continuous and discrete flow-matching, respectively. The authors also propose physical constrained for last-mile pose guidance to improve physical validity in generated molecules. Finally, the physically-constrained refinement step is introduced to boost the physical realism of the generation. Experimental results on GEOM-DRUGS demonstrated competitive performance in molecular generation.\n\nI find this work being a strong validation of non-equivariant architectures in 3D molecular generation. It is nice to see a standard Transformer, trained via flow-matching, achieve competitive performance with minimal inductive biases. This simpler approach surpasses curated equivariant methods and offers better inference speed.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The experimental results support the paper's argument that a simple and non-equivariant architectures can be effective for 3D molecular generation while being efficient. TABASCO utilizes a standard Transformer trained via flow-matching without graph-based message-passing or equivariant layers. Despite its relative simplicity, the model surpasses more complex / equivariant baselines on the GEOM-Drugs benchmark, and show efficiency in both sampling speed. Furthermore, the paper also includes ablation studies regarding positional encoding and cross-attention which validates some of the core design choices.", "audience_yn": "Yes", "audience_explanation": "The work is closely related to AI4Science community which builds a generative model for molecular generation. It also follows the recent trend of building architecture without strong inductive bias which is mostly supported by the experimental results. I believe the findings shown in this work will benefit the community in building more powerful generative models to solve scientific problems.", "requested_changes": "1. I would like to see how the proposed method works on conditional generation, e.g., generating molecules conditioned on certain properties / pockets. Conditional generation is closely related to the practical applications of molecular generation which would further showcases the effectiveness of TABASCO.\n2. The model relies on positional encoding of SMILES, but will it introduce ordering bias that misalign with generated 3D geometries? Will it help if other positional encodings like pairwise distances between atoms are applied?\n3. Given the model doesn't force physical constraints, are there any failure cases spotted in inference? And are there certain modes in the failure cases?\n4. In Table 5, it is shown that UFF outperforms distance-bounds guidance on speed and strain energy. What are the advantages of using the distance-bounds guidance instead of UFF?\n5. How is the proposed method extendable to larger scale like biomolecules? Do the authors think there're fundamental obstacles for the proposed method to work on larger molecular systems?\n6. It would also help better compare the performance if standard deviations of main metrics are reported.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "vJeutP1FZq", "reviewer": "Reviewer_6mdq", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This approach relaxes the widely used special assumptions or designs, equivariance, permutation invariance, and graph structure, in molecule encoding models, resulting a much more simplified architecture while achieving some better properties. \n\nThe model is demonstrated to generate molecules with higher physical quality and faster in inference.\n\nThe finding of study that even random rotation is not necessary is also interesting.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The claim of simple architecture is supported by the model design.\n\nThe claimed fast and high quality properties are also evident in the experiment results.", "audience_yn": "Yes", "audience_explanation": "Simpler architectures are very important for bio deep learning models to be democratized and are a recent trend in many different sub-fields in molecule-related tasks.\n\nThe successful relaxation of the assumptions has a potentially high impact.", "requested_changes": "- Critical to securing my recommendation for acceptance:\n\nThe motivation to discard molecules with more than 72 heavy atoms from the training dataset is unclear, as transformer is absolutely capable of handling at least hundreds of tokens.\n\nOther widely adopted metrics like RMSD for unconditional small molecule generation should be reported on the proposed model and compared with baselines. RMSD is reported in the current manuscript, but not compared with baselines. I understand it might not be sota in all senses, which is fine, but reporting them would be beneficial for a comprehensive understanding of the advantages and potential disadvantages of the simplication of assumpations.\n\n- Simply strengthen the work in my view:\n\nthe letters x and a in fig. 2 should be explained in the figure itself or in the caption.\n\nThe intuition toweigh the loss could be discussed.\n\nIn fig. 3, the metrics of showcased molecules could be shown to have a better understanding for readers.\n\nThe definition of Z(xt, t, R) is missing.", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)