Unified all-atom molecule generation with neural fields
Authors: Matthieu Kirchmeyer
Pedro O. Pinheiro
Emma Willett
Karolis Martinkus
Joseph Kleinhenz
Emily K. Makowski
Andrew Martin Watkins
Vladimir Gligorijevic
Richard Bonneau
Saeed Saremi
Venue: NeurIPS 2025 poster ()
Keywords: score-based generative model
neural fields
3D molecule
Primary area: machine_learning_for_sciences

Abstract: Generative models for structure-based drug design are often limited to a specific modality, restricting their broader applicability. To address this challenge, we introduce FuncBind, a framework based on computer vision to generate target-conditioned, all-atom molecules across atomic systems. FuncBind uses neural fields to represent molecules as continuous atomic densities and employs score-based generative models with modern architectures adapted from the computer vision literature. This modality-agnostic representation allows a single unified model to be trained on diverse atomic systems, from small to large molecules, and handle variable atom/residue counts, including non-canonical amino acids. FuncBind achieves competitive in silico performance in generating small molecules, macrocyclic peptides, and antibody complementarity-determining region loops, conditioned on target structures. FuncBind also generated in vitro novel antibody binders via de novo redesign of the complementarity-determining region H3 loop of two chosen co-crystal structures. As a final contribution, we introduce a new dataset and benchmark for structure-conditioned macrocyclic peptide generation.

Unified all-atom molecule generation with neural fields
Matthieu Kirchmeyer Joseph Kleinhenz'
Pedro O. Pinheiro Emma Willett!
Karolis Martinkus 1.1
Emily K. Makowski2
Andrew M. Watkins
Vladimir Gligorijevic'
Richard Bonneau'
Saeed Saremi*
Prescient Design, Genentech
Antibody Engineering, Genentech
Abstract
Generative models for structure-based drug design are often limited specific modality, restricting their broader applicability. To address this challenge, we introduce FuncBind, a framework based on computer vision to generate target- conditioned, all-atom molecules across atomic systems. FuncBind uses neural fields to represent molecules as continuous atomic densities and employs score- based generative models with modern architectures adapted from the computer vision literature. This modality-agnostic representation allows a single unified model to be trained on diverse atomic systems, from small to large molecules, and handle variable atom/residue counts, including non-canonical amino acids. FuncBind achieves competitive in silico performance in generating small molecules, macrocyclic peptides, and antibody conditioned target structures. FuncBind also generated in vitro novel antibody binders via de novo redesign of the complemmntarity-ddeeemmm of two chosen co-crystal structures. As final contribution, introduce dataset and benchmark for structure-conditioneed macrocyclic peptide generation".
region loops,
region H3 loop
1 Introduction
central challenge in drug discovery is designing molecules that bind specifically a target protein [1]. This task involves navigating a diverse landscape of molecular modalities. from small organic compounds to large biomolecules, each with unique chemical properties. Structure-based approaches are frequently employed to meet this challenge, utilizing the 3D structure of target site (often an accessible protein region) to generate novel molecules with high affinity. Generative models are emerging as powerful data-driven alternative to established traditional techniques such as virtual screening and physics-based simulations. These newer models can explore vast chemical spaces more effectively to identify molecules with desired binding properties [2].
Most structure-basee generative models specialize on single molecule modality to better account for specific physicochemical properties. Focusing on single molecular modality also simplifies data gathering and augmentation, training, representation choice, and metries used for validation. Generative models of small molecules typically represent molecules as point cloud of atoms [3, 4, 5] or discretized atomic densities [6. 7]. Most protein generative models leverage the fact that proteins are sequences of amino acids to represent them with point clouds of residues, where each residue contains multiple atoms [8], recovering their sequences with, e.g., co-generation [9] or inverse folding [10]. Many protein-centric models also rely on large sequence databases and self-supervised generative models for sequence that can help in scoring and generating/proposing mutations.
"The code is available https://githhi: http::///ggthhhcccce//sseeeeeeee https:/ h/agg/////ggggaa.......
design/funcbind The checkpoints
'Equal contribution, twork done Genentech. Correspondence matthieu. kirrhneyer@@mail.ccomm com.
pedrolopinheiro.com com, saremi., sar.mmissseedddgeeee com
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Nomain-specifie representations limit generalization, as models are not transferable from one modality to another. This narrow focus also limits utillity. as most key applications involve interfaces and catalysis across multiple modalities. We argue that modality-agnostii representations are better suited to a wide range of tasks and can learn physical properties across diverse atomic systems, thus leveraging more training data and more challenging metrics. These representations are more expressive [11] as evidenced by the successes of cross-modality models on structure prediction [12, 13], inverse folding [14] or molecular interaction prediction [15].
Here, we introduce FuncBind, a unified and scalable framework for generating all-atom molecular systems conditioned on target structures. Following Kirchmeyer et al. [16], we represent molecules with neural fields: functions that map 3D coordinates atomic densities. Neural fields are a compact and scalable alternative to voxels, while sharing many common advantages over point cloud based representations: they (i) are compatible with expressive neural network architectures (such as CNNs and transformers), (ii) account for variable number of atoms and residues implicitly, and (iii) can represent molecules across modalities with an all-atom formulation. Using this new representation, we build latent conditional generative model compatible with any score-based approaches. We tested denoising diffusion [17] and walk-jump sampling (WIS) [18]. sampling approach that enjoys fast-mixing and training simplicity when compared to diffusion models, largely because it only relies on one or few noise levels.
We train FuncBind on structures from three drug modalities: small molecules, macrocyclic peptides (MCPs), and antibody complementarity papllmmentartyyddeemmmnng region (CDR) loops in complex with target protein. These modalities encompass a range of chemical matter with challenging constraints, such as cyclic backhones and non-canonical amino acids. FuneBind achieves competitive results on in silico benchmarks, matching or outperforming modality-specifft baselines. On in vitro experiments, we show that FuncBind can produce novel antibody binders by redesigning the CDR H3 loop of two chosen co-crystal structures. We also create a new dataset', containing ~990,000 synthetic MCP/protein complexes derived from 641 RCSB PDB structures [19], particularly relevant for this work, as cyclic peptides exhibit chemistry and function that span small and large molecule modalities.
2 Related work
conket-conditioned small molecule generation. Several approaches have framed structure- based drug design generative modeling problem [20]. These methods commonly represent molecules including both ligands and targets-either either as point clouds of atoms or as voxel grids. Point-cloud approaches represent atoms as points in 3D space, along with their atomic types, and typically use graph neural network architectures. Point-cloud approaches have been used to generate molecules using autoregressive models [21, 22, 3], iterative sampling approaches [23, 24, 25], normalizing flows [26), diffusion models [27, 4, 28), and Bayesian flow networks [5]. Voxel-based approaches map atomic densities to 3D discrete voxel grids and apply computer vision techniques for generation [29, 6, 30, 31J. VoxBind [7] demonstrates that voxel-based representations achieve state-of-the-art results using expressive vision- based networks and score-based generative models. However, raw voxel-based models do not currently scale to larger molecules due to high memory requirements. Neural fields serve as the continuous analogue to discrete voxels, a technique widely adopted in 3D computer vision [32]. When applied to molecular generation, these fields match existing performance levels while demonstrating superior memory and computational efficiency [16].
Antigen-conditioned CDR loop generation. Antibody design is an active research topic and antibody-based treatments represented 26% of 2024 FDA approvals [33] (with related biolog- ics approvals an even higher fraction). A key line of antibody engineering work re-designs the iilemmnnarity-detemmmnnn
regions (CDRs), a subset of the heavy and light chains that totals 48 to 82 residues [34] and represents most of the protein's 's affinity determining variability. A recent approach is to co-design the CDRs sequences and structures using residue cloud representations and cquivariant graph neural networks [35, 36, 37, 38), combining these representations with diffusion models for generation [39, 40]. Other works leverage protein language models [41, 42), or revisit the problem by proposing new representations that incorporate domain knowledge and physics- based constraints [43]. As is the case for most models with demonstrated redesign capabilities, we tackle the task where the pose (docking) of the framework is provided. This assumption is relaxed in [44],
'available at http:: http://wwwggggggooooooc/aattttea

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(x}
Fy
D
Fy
D
Zx
Sglobal
(a)
(b)
Figure/ I: Neural field architectures. (a) Architecture used [16] where a global embedding used input to the neural field decoder. (b) Our proposed neural field architecture, where embeddings are spatially arranged into a feature map grid. The latter allows us to better capture local signal information from input space and is compatible with expressive architectures for denoising.
where the authors finetuned RFDiffusion [45] to generate the positions of backbone CDR atoms, followed by an inverse folding step [10]. Unlike other methods, FuncBind the first approach based neural fields that is also applicable to different data modalities simultaneously.
Pocket-conditioedd macrocyclic peptide generation. Occupying a unique chemical space between small and large molecules, cyclic peptides are rapidly growing class of therapeutics that can access biological targets often challenging for both small molecules and antibodies [46]. De nove generation of target-specific cyclic peptides is therefore highly desirable, in part due to the power and utility of high throughput screens for cyclic and linear peptides and peptoid binders, yet only few works have investigated this. Rettic al. [47] propose approach based on RFDiffusion [45] that successfully designed cyclic peptides conditioned on a target protein structure. Yet this prior method is unable to handle non-canonical amino acids, essential component for both compatibility with industry standard seghtthooughpu screens and for enhancing the therapeutic properties of these peptides. The aware of that handle non-canonical amino acids;
work by Tang al. [48] is the only work this model operates on tokenized smiles and performs target conditioning via classifier guidance with ML-based property predictors that known to generalize poorly out-of-distribution [49].
Method
FuncBind is latent ccore-based generative model that consists of two training steps: first learn a latent representation for each molecule that modulates the parameters neural field decoder (Section 3.1), then train conditional denoiser these latents (Section 3.2.1). The denoiser is used to sample molecules, conditioned on a given target (Section 3.2.2).
3.1 Neural field-based latent representation
We consider a dataset of N molecular complex tuples D {(V, otar , the binder and target, respectively, and is the modality of the binder. In this work, we focus on three modalities: small molecules, macrocyclic peptides, and CDR loops, though the framework accommodates any atomic system. Atoms are represented as continuous Gaussian-like densities in 3D space and molecules as functions mapping coordinates n-dimensional atomic occupancy values, R33 [0, 1 (where is the number of atom types) [50, 51, 52]. See Section for details.
1- where and yaar are
Similar to [16], an encoder embeds a molecule into a latent z, which is used to decode back an atomic density field. Decoding consists in modulating the parameters of shared neural field decoder based on the latent representation. However, instead of representing the latent with global embedding (Figure la) used in [16], we consider spatially arranged feature map (Figure 1b). This approach has been successfully applied in other domains [53, 54, 55] and provides two key advantages: (i) each spatial feature captures local information helping to scale the model larger molecules, (ii) compatible with expressive architectures (e.g. U-Nets [56)) for denoising.
The encoder Es 1, + Rdd, d C' L3, is a 3D CNN parameterized by 1o that maps voxel grid G... generatedddddeee by discretizing v at fixed low resolution (for computational efficiency) set by the integer L,, into a latent space with C channels. For decoding, we use nearest neighbor interpolation as [54]: from the feature map z, we extract position-dependent vectors zn E RRC The embedding 2, constant over 3D patch in coordinate space. The decoder D3 R
R
R parameterized

---

E
G
D3
E
x(0.g)
G
S
{a,c)
2dy | 2z ...
Figure 2: Conditional denoiser training overview. We voxelize separately the binder and the target Utar of given complex and encode them into 2, 2 tt using encoders Ev, Eg,, respectivvlly We train denoiser Eo (gy 2aar, o, c) to remove the noise from conditioned Star, the noise level and the one-hot modality class (e.g. a cyclic peptide). The denoised latent representation is fed into a neural field decoder Dæ: this gives reconstructed field undergoes some additional postprocessing to recover the bonds and residue identities (if applicable); see Section 3.3.
by Φ, then computes the molecular density field at coordinates x E R given local modulation embedding 2e E RC, We use a conditional neural field based on multiplicative filter networks [57, 58] with Gabor filters, natural choice for modeling the sparse atomic density fields [16].
The neural field is trained across modalities with the objective proposed in [16]. A KL-regularrizaion term [59] is added, following common practice in latent generation [60]:
/ [1))(2 e(=)11 dæ + 3 KL (90(21)) v) NN(2;0,a)) (1)
O)
Σ Ez-qg(=)o)) VED
where (0 v) = N(2; (i)g(o p(v) and ((v) are parameterized by Eg and [ is regularization weight. As [16], when optimizing for Equation (1), we upsample coordinates æ close to the center of each atom to focus training non-empty spaces. Since model does not have equivariance built the architecture, we apply data augmentation (translation and rotation).
3.2 Conditional latent score-based generation
We train a conditional denoiser on the neural-field based representations (Section 3.2.1). The denoiser used to sample molecules from the aggregate posterior the VAE encoder [61]. conditioned on given target, with conditional diffusion and walk-jump sampling (Section 3,2.2).
3.2.1 Conditioned denoiser
Our denoiser takes as input noisy latent representation and set of conditioning information, and outputs the "clean" version of the latent representation. In this work, we condition the denoiser on three signals: (i) the target structure yaar (ii) the molecule modality and (iii) the noise level o.
More formally, let (v, earr e) be (binder, target, modality) tuple from the dataset, (z, 2 = (E&(G.) (G.), Egg (Gter) )) their latent representations and 2 + de, ~ N(0, Ia), noisy version of z. The target encoder Eg is 3D CNN with similar architecture as Ey but different parameters v Following the preconditioning pre-processing proposed by [62], our denoiser åg is defined as:
2 tar a, c)
+ U
Us -o22
U. U.2 2 tar log(o),c)
o2
Vo2
where Uo a neural network parameterized by and the embeddings and tar are normalized to unit variance and zero mean per channel, similar to [60]. Figure 2 shows an overview of the model architecture. The spatial structure of the latent space allows us to model Up with 3D U-Nets, standard architecture for generative models in computer vision. In particular, we adapt the network of Karras et al. [62] designed to generated 2D images-to our 3D generation setting. Crucially, similar to recent works [52, 7, 63], we do not use any type of SE(3) equivariance constraints. Instead, we replace these constraints with data augmentation (rotations and translations) during training.
The conditional denoiser is trained by minimizing the following loss at a given noise level o:
L. (0, v/) = E "tr,ntr,,)yer ,c)-D, z=qu(zlo), x~N(0.
||
ae tar at",,a, c) - = =11]]
(2)

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where 2''r_ =
E (G is the encoding of the low-resolution voxel of the target. We apply the reweighting scheme in [62] across noise levels, i.e.:
o + o² eu(ar Lo (0, (0) =(()
L denoiser (0, 3¹) Elo))
where a is sampled along some pre-determined distribution p(ar) (see Section 3.2.2) and u(a) is one-layer MLP trained with the denoiser. This effectively reweights the loss based on the noise level.
3.2.2 Sampling strategies
We experimented with various score-based sampling strategies in the conditional setting, including the SDE formulation of denoising diffusion [64, 65], widely recognized for its state-of-the performance in image generation and walk-jump sampling (WJS), based on a probabilistic formulation [18]. Diffusion models operate over continuous range of noise levels in
of least-squares denoising contrast WJS which considers only one noise level.
These models rely on the Tweedie-Miyssawe formula (TMF) [66, 67], which relates the least-squares denoiser at noise level with the score function at the noise level. Given y = 2 oe, 8 N(0, Ia), the conditional extension of TMF was derived in [7]. In our notation, it takes the form:
Vy ply 2 .a,c) sa(y) 2 Star 0, c) = (Ee(y | o. w)/0²,
(3)
a. c) is the learned conditional score function. where Eo is the minimizer of Equation (2); so 2
For diffusion, we follow [65] and generate samples by numerically integrating the reverse-timg SDE from noise level max Omins approximating the score function with the learned denoiser E 2 0, c) and TME. We adopt the variance exploding formulation and the stochastic SDE sampler from EDM [62]. For WJS, we proceed in Section B.4.2 and report the results on CDR H3 redesign in Section C.1.2.
3.3 Recovering molecules from generated atomic-density fields
To recover the underlying molecular structures from sampled latent codes we employ post- processing pipeline inspired by [16]. The initial phase determines atom coordinates by identifying local optima in the neural field. This is achieved by first rendering the latent code into a 0.25A resolution voxel, then performing peak detection with MaxPooling filters, and finally refining the coordinates through gradient ascent, which takes advantage of the neural field's differentiability. The second phase involves inferring bonds and, when applicable, amino acid identities from the generated point cloud (coordinates and atom types) using OpenBabel software [68]. This yields sdf files for molecules and peptides and pdb files for proteins. A specific approach for identifying non-canonical amino acids, which are not recognized by OpenBabel, is described in Section D.
Experiments
We test model on the following in silico settings, covering the three modalities discussed above: (i) small molecule generation conditioned on protein pocket (Section 4.1); (ii) antibody CDR loops redesign conditioned conditioned on a protein pocket (Section 4.3). We also performed in vitro validation of antibody CDR epitope.
epitope (Section 4.2); and (iii) macrocyclic peptides generation
loops redesign conditioned
For these tasks, the neural field is jointly trained on all three modalities. We train a 5B parameter model across modalities. Samples are generated via conditioning the target structure. Note that our network is significantly larger than alternative models; we have observed improved performance in the unified setting for larger networks. See Section B for additional model details and Figure 3 for qualitative samples.
We compared our unified model against specialized models trained independently for each modality, Overall, performance parity was observed across most metrics, with the key exception being unique- ness, which was significantly higher in the unified model. See Section 1.3 3 for comparison on CDR H3 inpainting. Further research exploring transfer learning across a broader set of modalities represents exciting avenue for future work.
5

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Figure 3: Examples of generated molecules given target structure for different modalities: (top) small molecules against 2rma, (middle) macrocyclic peptides against 5ooc and (bottom) CDR H3 loop against 5tlk. The seed binders are shown the right.
4.1 Small molecule generation
Data. We consider the standard Dooke22c [69] benchmark, with the pre-processing and splitting strategy of [70]. Pockets clustered at sequence identity of < 30% using MMseqs2 and are split into 99,900 train ligand pockets pairs, 100 validation pairs and 100 test pairs.
Baselines. We compare FuncBind to various pocket-conditioned ligand generative models: these include point cloud approaches based on autoregressive models (AR [21] and Pocket2Mol [3]), diffusion (DiftSBDD [27]. TargetDiff [4], DecompDiff [28]), Bayesian Flow networks (MolCraft [5]) and a voxel-based based approach based on walk-jump sampling (VoxBind [7]). FuncBind can seen more scalable generalization of VoxBind and closely matches its performance. All methods but DecompDiff and MolCraft rely OpenBabel [68] to assign bonds from generated atom coordinates.
Metrics. We evaluate performance using similar metrics as previous work [4]. For each method, we sample 100 ligands per pocket. We measure affinity with three metries using AutoDock Vina [71]: VinaScore is the docking score of the generated molecule, VinaMin is the docking score after local energy minimization, VinaDock fully re-docks the generated molecule, including both search and scoring steps. We also compute the drug-likeness, QED [72], and synthesizability. SA [73], score of the generated molecules with RDKit [74]. Diversity is the average Tanimoto distance (in RDKit fingerprints) per pocket across pairs of generated ligands [75]. # atoms is the average number of (heavy) atoms per molecule. We also compute the PoseCheck metrics [76]: Steric clash computes the number of clashes between the generated ligands and their pockets, Strain energy (SE) measures the difference between the internal energy of the generated molecule's ' pose (without pocket) and a relaxed pose (computed using Universal Force Field [77] within RDKit); report the median value.
Results. The results are reported in Table 1. FuncBind competitive with the current state of the art, slightly underperforming on bocking-related metries and strain energy compared to VoxBind and Molcraft and on number clashes compared to VoxBind. This experiment demonstrates FuncBind's ability to generate highly-vrribble small molecules. Next, we demonstrate that it can also handle the more regular structures amino acid-based molecules.
4.2 Antibody CDR redesign
Data. We consider the SabDab dataset [78], which comprises suuibody-protein co-crystal structures and the data splits from DiffAb [39]. This non-i.i.d. split ensures that antibodies similar to those of removed from the training set. The test split
the test set (i.e.more than 50% CDR H3 identity) includes 19 targets, for which we redesign each CDR loop individually. As our baselines, we consider the Chothia numbering scheme [79] for the CDR definition.
Baselines. We compare FuncBind to representative baselines: RAbD [80], a Rosetta-based method and two ML-based models, DiffAb [39] and AbDiffuser [43]. We consider the variation of AbDiffuser

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Table 1: Results on CrossDocked2020 test set. 1/4 denote that higher/lower average (Avg.) or median (Med.) is better. For # atoms, numbers close to Reference are better. Baseline results are from [7, 28]. FuncBind's results are shown with mean/standard deviation obtained over 1,000 bootstraps.
VinuScore 4
VinaMin
VinaDuck
QED
SA
Div.
S.E.
Clash 4
#atorns
Avg.
Med.
Avg.
Med.
Avg.
Med.
Avg.
Avg.
Avg.
Med.
Avg.
Avg.
Reference
-6.36
-6.46
-6.71
-6.49
-7.45
-7.26
.48
.73
103
4.7
22.8
AR
-5.75
-5.64
-6.18
-5.88
-6.75
-6.62
.51
.63
.70
595
4.2
17.6
Pocket2mol
-5.14
-4.70
-6.42
-5.82
-7.15
-6.79
.56
.74
.69
206
5.8
17.7
DifISBDD
-1.94
-4.24
-5.85
-5,94
-7.00
-6.90
.48
.58
.73
1193
15,4
24.0
TargetDiff
-5.47
-6.30
-6.64
-6.83
-7.80
-7.91
.48
.58
.72
1243
10.8
24.2
DicompDiff
-5.67
-6.04
-7.04
-7.09
-8.39
-8.43
.45
.61
.68
N/A
7.1
20.9
MolCraft
-6.59
-7.04
-7.27
-7.26
-7.92
-8.01
.50
.69
.72
195
7.1
22.7
VoxBind
-6.94
-7.11
-7.54
-7.55
-8.30
-8.41
.57
.70
.73
162
5.1
23.4
FuncBind
-5.71
-5.64
-6.34
-6.18
-7.26
-7.28
.50
.65
.70
217
7.4
19.0
(4:000)
with side chain generation better match FuncBind's all-atom setting: AbDiffuser in contrast to other baselines, generates all 6 loops jointly. We also compare to AbX [81] and the reproduction of dyMEAN [38] from [81] for H3 design, where the DiffAb splits were considered.
Metrics. We compute the following metrics, measuring the similarity of the generated designs to the seed: amino acid recovery (AAR). the sequence identity between the seed and the generated CDRs; RMSD, the C. root-mean-square deviation between the seed and generated structure and IMP, the percentage of designs with lower binding energy (AG) than the seed, calculated by Interfacennalyzer in Rosetta [80]. Baselines apply pasetta-basee relaxations prior to computing IMP to improve the energy scores: DiffAb refines the generated structure with OpenMM [82] and AbX uses
[80]. We report metrics for 100 generated samples per target. Note that our model, unlike most baselines, generates samples with diverse sequence lengths; compute these metries we consider samples with the same length as the original seed. We found that uniqueness impacts AAR and RMSD, particularly for non-H3 loop designs which exhibited low uniqueness. This presents a challenge for fair model comparison, as baselines do not report uniqueness. For completeness, we also report the metrics for the unique samples on Table (Section C.I).
Results. The results in Table 2 indicate that our model is state of the art both on amino acid recovery (AAR) and C. RMSD values, outperforming other baselines by 1.5 to 3 times across all loops. This performance can be attributed to our all-atom formulation and neural-field representation, which enable the model to better capture the molecular conformation and conditioning context. AbDiffuser also leverages side-chain information but underperforms in RMSD, highlighting the distinct advantages of our approach. Finally, FuncBind's interface energy improvement (IMP) without backbone minimization is competitive to the IMP of approaches that apply minimization. This showcases the quality and fidelity of the generated structures, as energy very sensitive to wrong atom placement. As the baselines [81, 39], when applying Rosetta's fast-relax backbone minimization on the generated loops, IMP greatly improves as expected, outperforming even the Rosetta RAbD protocol that directly optimizes the energy function. This refinement procedure slightly increases RMSD, as it changes the loop to minimize strain, while FuncBind is trained to mimic patterns in ground-truth truth crystal structures.
Length distributions generated. The above evaluation restricted designs to the seed's length, common prior in many generative models for this task. However, in many settings, we do not know what is a reasonable length. FuneBind is designed sample designs across various lengths, a useful capability for de novo CDR generation. To demonstrate this flexibility, we analyzed histograms of sequence lengths and atom counts for CDR H3s designed for de novo target, comparing them against the original seed's values (see Figure 4). For this specific target, while the generated designs exhibited a range of lengths, their distributions were centered on the seed's 's reference values. Further validation of designs with other lengths is left for future research.
7

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Table 2: CDR inpainting on SAbDab [78] with DiffAb splits [70]. RMSD is in Á and AAR, IMP are in %. indicates additional relaxation optimization with Rosetta.
Method
L1
AAR
RMSD /
IMP
AAR
RMSD
1MP
RAbD
22.9
2.26
43.9
34.3
1.20
46.8
DiffAb
65.8
1.19
53.6
55.7
1.39
45.6
AbDiffuser
76.3
1.58
81.4
1.46
FuncBind
86.9
0.41 / /0.44
35.0
86.4
0.68 / 0.73!
45.0/ 80.4!
H2
L.2
RAbD
25.5
1.64
53.5
26.3
1.77
56.9
DiffAbt
49.3
1.08
29.8
59.3
1.37
50,0
AbDiffuser
65.7
1.45
83.2
1.40
FundBind
78.2
0.52
0.54
31.7/6144
86.2
0.83
0.84
39.5 66.0
H3
L3
RAbD
22.1
2,90
23.3
20.7
1.62
55.6
DiftAb
26.8
3.60
23.6
46.5
1.63
47.3
dyMEAN
29.3
4.80
5.26
AbXt
30.3
3,41
42.9
AbDiffuser
34.1
3.35
73.2
1.59
FundBind
47.5
2.04 2.100
19.4 49.91
80.8
0.68 / 0.731
32.7 /
67.5
1065
12-
0281
23
Figure 4: CDR H3 length (left) and atom count (right) histogram the de-novo 4cni target. Red is the seed H3's reference numbers.
In vitro evaluation. We performed wet-lab validation of H3 loop redesigns based on the co-crystal structure of an antibody bound rigid and flexible epitope*. We selected the H3 loop for its important contribution to the antibody's functional properties. We consider a de novo setting, where interfaces similar to the two complexes, identified using Ab-Ligity [83], were excluded from training.
From an initial pool of 10,000 unique generated H3 designs (all matching the original seed's length), 190 were selected for experimental testing. This selection involved two steps: 1) The top 500 designs were shortlisted based on model confidence, as indicated by their repeated generation counts. Our in silico validation showed that repeats is useful proxy for high amino acid recovery of the seed. 2) These 500 designs were then clustered into 190 groups using weighted K-means based on sequence edit distance, where the weights were defined by the repeat generation count. The design with the highest repeat count (highest confidence) from each of these 190 clusters was chosen for synthesis and characterization. The selected antibody designs were expressed and purified in the wet lab. Binding affinity was then determined using surface plasmon resonance (SPR) measurements. Section C.2 presents some detailed analysis. Func achieves a binding rate of 45% on the rigid epitope and 2% on the flexible epitope, which increases to 4% with relaxed binding threshold.
For legal reasons, do disclose the target's names.

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Table 3: Results on our MCP benchmark. RMSD is in A, Residues- Vina Dock are in %.
TS f
Residues-TS20.51
L-RMSD 4
I-RMSD
TM-Score
Vina dock 1
RFPeptide
0.31
29
12
3.3
0.33
8.8
ArCycDesign 0.34
29
7.6
3.7
0.33
29
FuncBind
0.33
25
2.6
1.8
0.36
41
4.3
Macrocyclic peptide generation
Data. Given the scarcity of established baselines, benchmarks, and available data for macrocyclic peptide (MCP)-protche complexes, we introduce a novel benchmark to facilitate the evaluation of gencrative models for MCPs. To address the data limitation, we have curated dataset of 186,685 MCP-protein complexes using a 'mutate then relax" strategy detailed in Section E. Taking as input an original set of 641 protein-MCP complexes sourced from RCSB PDB [19], this strategy consists in (i) randomly mutating the MCPs at 1 to 8 different sites, using a list of 213 distinct amino acids, (ii) relaxing them using fast-relax, which involves iterative cycles of side-chain packing and all-atom minimization [84] and (iii) selecting the lowest interface scores. The source dataset comprises lengths ranging from 4 to 25 amino acids with an average of 10 (Section E Figure 13a). 78% of the MCPs contain one or more non-canonical amino acids, i.e. any amino acid that is neither L-canonical nor D-canonical. We split the dataset into train, test and validation subsets using clustering approach detailed in Section E that aims at creating non-i.i.d test set consisting of 85 protein pockets.
Baselines. MCPs pose significant challenges for generative models due to their non-canonical amino acids, cyclization, and scarce training data. To our knowledge, no other target-conditioned, structure- based MCP generative models handles non-canonical amino acids, precluding direct comparisons. For reference, we compare nonetheless FuncBind with AfCynDesign [85] and RFPeptide [47], two models generating MCPs exclusively with canonical amino acids and N-to-C cyclization.
Metries. As part of this new benchmark, we define and compute relevant metrics. Tanimoto similarity (TS) assesses the resemblance between the ground-truth seed and the sampled MCP structure. Ligand RMSD (L-RMSD) is the RMSD between sample MCP to seed MCP, and template modeling (TM) score, length independent similarity metric, based on the Kabsch alignment of the backbone atoms (N, O)O TM score was calculated by maximizing the scaling factor. The same backbone logic is applied to compute interface RMSD (I-RMSD), the RMSD in the pocket (which are for the most part slightly lower since the pockets are identical). The sample and the seed were not aligned for I-RMSD since this is based on where the MCPs are in the pocket. Binding affinity was calculated through Autodock Vina [71].
Results. We observe correlation between the generated designs with the MCP seeds. Qualitatively, Section 1 Figure 10 illustrates the close alignment of the backbone between the sampled structures and the seed. Most of the sampled molecules display consistent repeating peptide bonds, linking the C₁ carbon of one a-amino acid to the N₂ nitrogen of the next. Closure bonds (such as disulfide in Figure 10a,b and N to C cyclization in Figure 10c) are also often maintained in the sampled sets.
Metrics are reported in Table 3. The mid-range TS and TM scores reflects strong similarity to the peptide backbone, with variability occurring at the functional groups of the residues. An example of per-residue TS for molecules sampled with the seed mutant (Section E. Table 10a) and the crystal MCP (Section E. Table 10b) shows that the highest TS occurs at the disulfide closure bond. This elevated per-residue similarity results from the preservation of closure bond residues throughout the curated dataset. Furthermore, the per-residue TS is higher across the crystal MCP residues than at the mutated residues of the seed mutant (PRO3A20 and GLU4460). Because all mutants originate from the crystal MCP, the dataset is closely tied to the crystal sequence, and the sampled structures similarly reflect this connection. Low RMSD results show generally good alignment with the seed MCP, with many samples exhibiting RMSDs below IÅ, particularly for I-RMSD. These results are consistent with the TM scores, where 20% of the samples exhibit TM scores greater than 0.5. Finally, Autodock Vina binding affinity reveals that nearly half of the generated samples, both before and after minimization, have better binding affinity in the pocket compared to the seed.

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a. (Seed)
(Sample 11)
(Sample 35)
4508
4500
1506
6536
4758
4538
4718
64
SER
4508
GLN
-1.591
5A
010
4508
GLN -1.338
8A
TVR
4508
GLN
-2.804
SER
4538
HET -1.724
C10
4530
MET -2.185
-0.702
5A 010 4718
SER -1.533
Figure 5: Per-residue energy scores at the same position were calculated using Rosetta's residue energy breakdown for seed and two samples. We analyzed: (a) the seed's serine, alanine (C1O) from sample 11 (Section E.1 Figure 12), (c) tyrosine from sample 35.
(b) 3-hydroxycyclopentyi
Compared the baselines, FuncBind achieves superior similar metries, notably lower L/IRRMSDs. FuncBind also yields the highest proportion of designs with better docking scores. The only underper- formance was in Tanimoto similarity (Residues-TS a larger set of non- non-canonical amino acids and sequence lengths as opposed to these baselines. We encourage future comparisons on this new benchmark, especially for models handling non-canonical amino acids.
in particular), expected FuncBind accesses
Analysis of generated non-canonicals. In Figure 11 (Section E.1), amino acids categorized into known canonical and non-canonical amino acids (seen in the training set), and unknown non- canonical amino acids, which represent newly generated amino acids not previously seen. Fewer than 1% of all categorized amino acids were labeled as "unreasonable", a designation applied when a bond was shorter than 0.8A or when invalid oxygen-oxygen or nitrogen- bonds were present. Some reasonable and novel generated amino acids are presented in Figure 12 (Section 1) Generating novel, chemically plausible amino acids without restrictions from predefined library or initial cyclic backbone allows broader exploration of the binding pocket. This is demonstrated in Figure 5b, where an amino acid absent from our library interacts with pocket residues that neither the seed (Figure 5a) nor chemically similar amino acid at the same position (Figure 5c) engage. The absence of constraints in MCP generation promotes greater sequence diversity and deeper investigation of the binding pocket.
Conclusion
We presented FuncBind, new framework for all-atom, duructur-conditiion de novo molecular design. FuncBind based on a new modality-agnostir representation, that enables a single model to be trained across diverse drug modalities; we focused on small molecules, macrocyclic peptides and antibody CDRs. FuneBind handles variable atom and residue counts and is based on recent ad- vancements in computer vision, replacing equivariance constraints with data augmentation. FuncBind demonstrates competitive in silico performance, matching or outperforming specialized baselines. In vitro, we demonstrate that FuncBind generates binders against de novo targets. It generates novel and chemically plausible molecules, including new non canonical amino acids. Future directions include extending FuncBind to larger biomolecular systems and more data modalities. Furthermore, the scaling behaviour of this model remains an interesting direction for future study, particularly given the absence of overfitting as the denoiser increased in size (we tested up to 5B parameters).
important to note that, like other structure-based methods, FuncBind relies on the availability of an accurate model of the molecular interface to be designed. This can be a limitation, as these models are costly to obtain and their availability often restricted the drug discovery process, particularly for large molecules. Finally, real-world application of generative models in drug design requires addressing a range of properties beyond binding, e.g. synthesizability for small molecules and developability for antibodies, considerations not handled in this work.
Acknowledgements We thank Prescient Design and the following colleagues: Jan Ludwiczak for processing the SabDab dataset. Tamica D' Souza for performing the antibody wetlab validation.
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Max Shen, Namuk Park. Nathan Frey, Sidney Lisanza, Rob Alberstein, Ewa Nowara, Natasa Tagasovska, Chen Cheng. Pan Kessel, Sarah Robinson, Joshua Yao-Ye Yu Lin for insightful discussions and Genentech's s legal team.
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[86] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015, (cit. on p. 24)
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[90] K. Drew, P. D. Renfrew, T. W. Craven, et al. Adding diverse noncanonical backbones to rosetta: enabling peptidomimetic design. PLoS One, 2013, (cit. p. 31)
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Appendices
This supplementary material is organized follows:
1. Section A includes a broader impact statement.
2. Section B includes model and implementation details.
3. Section C provides some additional results for antibody CDR redesign.
Section C.I presents some additional in silico results
Section C.2 presents our in vitro results.
4. Section D explain how we inferred non canonical amino acids without OpenBabel.
5. Section E presents some additional results for macro- cycelic peptide generation
Section E. presents some additional results.
Section E.2 presents how curated the dataset for training FuncBind.
Section E.3 presents the train val test splitting logic.
A Broader impacts
This work introduces FuncBind, novel framework for all-atom, structure-conddttoone molecular generation, whose primary positive impact lies in its potential to accelerate and enhance the discovery of new therapeutics across diverse modalities, from small molecules to complex biologies like peptides and antibodies. While the underlying principles could find applications in other scientific fields like materials science, its deployment drug discovery requires addressing significant challenges, including the validation gap between in silico predictions and experimental testing (in vitro, in vivo, and clinical trials).
B
Model details
B.1
Representation
FuncBind is based on a neural field representation that models an atomic density field, a smooth function taking values between 0 (far away from all atoms) and 1 (at the center of atoms). This field takes the following form [50, 51]:
II (1-cxr exp ( (-(1ie
Wa: 2
Vx E IR3 (z(x)= =1
-93r
(4)
.93r
where a, is the ęth atom of type (among n choices), for total of n, atoms and is the atoms' radius set to 1.0A for all atom types.
We consider small molecule, macrocyclic peptides and proteins. Note that protein-specioo atom types (e.g. C. C3 etc.) are merged into a single element type (e.g. C). This helps transfer learning across modalities.
8 element types C, C,O,N, N, F,C,, Br that cover all major atom types across
Finally, the field is defined over volume of (32A)3. It the continuous version of voxel grid of spatial dimension 128 and resolution of 0.25A, in 8x 1283
B.2 Neural Field
The encoder Eg is a 3D CNN containing 4 residual blocks (number of hidden units 256, 512, 1024, 2048 for each block), where each block contains convolutional layers followed by BatchNorm, ReLU and pooling layers (we use max pooling on the first three blocks). The encoder has 59M parameters. The input to the encoder is a low-resolution grid of spatial grid dimension L = 16 corresponding resolution of 2Å. Before voxelizing the molecules, we first center the atoms around the tightest bounding box encapsulating the molecule, apply random rotation to the atoms
23

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(each Euler angle rotated randomly between [0,2 and random translation between [-1, 1]A then normalize their coordinates the range of [-1,1]] 1].
The decoder D is a conditional Multiplicative Filter Network (MFN) [57, 58) with Gabor filters and 6 FiL--modulated layers, where each fully-connected layer has 2048 hidden units. 'he decoder has 59M parameters.
The auto-encoder is trained with Adam Optimizer [86] with learning rate 10-2, 20-2,, _81 0.9, .2 0.999. We apply a KL regularization weight of À = 10-5 Batch size 32 over B200 GPU; we sample 15000 coordinates per batch.
B.3 Denoiser
The hyperparameters the Karras et al. [62] UNet architecture are as follows: 5B model with blocks with 512 channels, channel multipliers [1,2,3,4], attention resolutions [4, 2]. This model follows the XXL setting of EDM2 [62] and increased the number of channels from 448 to 512. For reference, Protéina [63], the largest protein backbone generative model, comprises 400M parameters and RFDiffusion [8] roughly 100M parameters. As [65], we apply preconditioning to learn the denoiser across noise levels. Moreover, we sample the noise levels from log- log-normal distribution with mean 1.2 and standard deviation 0.8.
The target encoder Eng is 3D CNN which takes input voxel grid of dimension R R24322 of the target protein (resolution 1.0A), considering atom elements C, O, S, and consists of magnitude preserving [62] CNN layer with kernel 3 x 3 x 3 and output channels 64, then a downsampling layer to spatial grid in R6 then magnitude preserving U-Net block [62] with output channels
C = 128 leading voxel of size RCx 16
The parameters are optimized with Adam optimizer [86] with learning rate Oreef = 10 2, B1 0.9, B2 0.95 using an aggregated batch size of 768 over 8 B200 GPUs. We perform early stopping on the validation loss. We use the power function exponential moving average from EDM2 [62] with an EMA length of 5%. Moreover, we adopt the inverse square root decay schedule of [86], also used Oref
in [62] which sets a(t) m/ax(1//ret)
where we set fref = 20040. Finally, the networks are trained by randomly dropping the conditioning information 10% of the time.
B.4 Sampling
To improve uniqueness, we apply different rotations the pocket on each MCMC chain, in a similar fashion to how rotation-basee data augmentation is performed at training time.
B.4.1 Denoising difftusion
We set as follows the sampling parameters of EDM2 [62]]
N 128 steps
mmin 0.01, maax 10
S min
5.0, Smax 7.0
Schum 30.0
S Snoise 1.003
p
Moreover, we apply temperature scaling with 7 = 0.5 on Crossdocked and - 0.33 on MCP- protein complexes.
B.4.2 Walk-Jump sampling
We also implemented a conditional form of the Walk-Jump Sampling (WJS), score-based generative model that is based on probabilistic formulation of least-squares denoising [18]. The framework based on the Tweedie-Miyee Miyasawa formula (TMF) [66, 67], which relates the least-squares denoiser
24

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at noise level a with the score function at the noise level. Given + dE,E ~ N(O, the conditional extension of TMF was derived in [7]. In our notation, takes the form:
Vy log ply 2 .a,c) soly 2 tar G, e) = (Eo(y) | o, 9)/0²,
(5)
where &g the minimizer of Equation (2): so 2 o. c) is the learned conditional score function.
Walk-jump sampling. WJS [18] is composed of two stages: (i) (walk) samples the noisy latent variables conditioned on 2/ar, using Langevin Markov chain Monte Carlo (MCMC) via the learned score function (Equation (5)), (ii) (jump) estimates "clean" 2 by single-step denoising. There is a fundamental trade-off in this sampling strategy: for larger a, sampling from the smoother density becomes easier, but the denoised samples move farther away from the distribution of interest [87].
Multimeasurement walk-jump sampling. The sampling trade-off in WJS is addressed in multi- measurement denoising models [88, 87], in which the problem is framed as sampling from the distribution Po (W1:m) associated with /r.m (May...,y (Way...,3m). where y/ 2 oeg. k E (1....,7)) and Eg (O, Ia) all independent of z. Saremi et al. [87] studied a sequential scheme for sampling from Po (y1:m) and showed that the noise level effectively decreases (as far as the denoiser is concerned) at the rate a/ mm. Furthermore, it was shown that sampling becomes casier upon accumulation of measurements. The general sampling problem is therefore mapped to sequence of sampling noisy data at a fixed noise scale, while the effective noise decreases via accumulation of measurements. We refer to this scheme WJS-m, which involves two hyperparameters: the noise level a, and the number of measurements m. In this construction, we only need to keep track of the empirical mean of noisy samples. In particular, we have (see [87, Eq. 4.9]):
V yo log Po (ym 31:mm1 tar G. c)
So (8 1:m
far
c) + σ² (71.m Vm))
where Fmmm is the empirical mean of the measurements (11, (V1..... ). The used in sampling (y1..... 3mm) iteratively using Langevin MCMC [87, Algorithm 1]. Finally, the denoising "jump" in WJS-m is achieved via (single- )ensureenn)))) TMF using the sufficient statisties I:m at the noise scale a/ /.. It is clear that the vanilla WJS discussed above reduces to WJS-1. Although there is flavor of diffusion in this scheme due to its sequential strategy, WJS-m is arguably more "surgical" in that, by construction, we do not need to learn score functions over a continuum of noise levels, but only finite one identified by m. This is especially appealing for applications where WJS-1 already shows reasonable performance and m is therefore taken to be small. This work contains the first experimental validation of WJS-m in generative modeling applications.
function above is
We report the results for WJS for CDR H3 inpainting in Section C.1.2. The parameters are set to 7.0 and 16. We use underdamped Langevin MCMC from Sachs al. [89] in the BAOAB scheme with K 50 steps, friction 7 = 1.0, discretization step = a/2 (187, Algorithm IJ).
C
CDR redesign
C.1
In silico evaluation
C.1.1
Uniqueness
Table 4 reports our CDR sampling results over unique sequences.
We observe that higher uniqueness usually leads to lower Amino Acid Recovery (AAR). In other words, repeated sequences tend to correlate more with the seed. We use this simple heuristic to select H3 designs for in vitro evaluation (see Section C.2).
C.1.2 Ablation with Walk Jump Sampling
We report the performance of multimeasuremmnt WJS and diffusion. Overall the models are compara- ble with slightly higher uniqueness for diffusion.
C.1.3 Comparison to specialized model
We observe that the unified model has higher uniqueness than the specialized model, with slightly better CDR loop inpainting performance on unique samples. We observe this trend on the other data modalities as well.
25

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Table 4: Impact of uniqueness on CDR inpainting performance. RMSD is in and AAR and Uniqueness are in %. indicates designs with unique sequences.
Method
AART
RMSD
Unique f AART
RMSD
Unique
H1
L1
AbDiffuser
76.3
1.58
81.4
1.46
FuneBind
86.9
0.41
23.5
86.4
0.68
38.9
FuncBind"
75.9
0.45
100
79.3
0.85
100
H2
L2
AbDiffuser 65.7
1.45
83.2
1.40
FuncBind
78.2
0.52
20.6
86.2
0.83
19.4
FuncBind"
59.5
0.57
100
54.4
2.39
100
H3
L3
AhDiffuser
34.1
3.35
73.2
1.59
FuneBind 47.5
2.04
85.5
80.8
0.68
44.1
FuncBind" 44.1
2.16
100
68.9
0.94
100
Table Diffusion vs WJS on H3 loop inpainting. indicates designs with unique sequences.
Method
AAR
RMSD
Unique f
FuncBindair
47.5
2.04
85.5
FuncBindgur
44.1
2.16
100
FuncBindw.s-16
51.0
1.89
73.8
FuncBind
41.4
2.18
100
Table 6: Loop uniqueness comparison between Unified and Specialized models.
Loop
Uniqueness Unified
Uniqueness Specialized
HI
23.5
9.6
H2
20.6
12.6
H3
85.5
69.2
LI
38.9
10.6
L2
19.4
14.0
L3
44.1
22.3
Table 7: H3 loop performance on unique samples.
H3 loop
AAR
RMSD
Unified
0.441
2.16
Specialized
0.406
2.07
26

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C.2 In vitro validation
To experimentally validate FuncBind's capabilities, performed wet-lab validation of H3 loop redesigns based on the co-crystal structure an antibody bound to a rigid and flexible epitope. We selected H3 loop redesign due to H3's important contribution to the antibody's functional properties. This study was conducted
de novo setting: interfaces similar to the two complexes, identified using Ab-Ligity [83], were excluded from training.
From an initial pool of 10,000 unique generated H3 designs (all matching the original seed's length), 190 (i.e. 2 SPR plates) were selected for experimental testing. This selection involved two steps:
1. The top 500 designs repeated generation counts. Our in-silico validation showed that repeats is an useful proxy for high amino acid recovery of the seed.
shortlisted based model confidence, indicated by their
2. These 500 designs were then clustered into 190 groups using weighted K-means based on sequence edit distance, where the weights were defined by the repeat generation count. The design with the highest repeat count (highest confidence) from each of these 190 clusters chosen for synthesis and characterization.
The selected antibody designs were expressed and purified in the wet lab. Binding affinity was then determined using surface plasmon resonance (SPR) measurements.
C.2.1 Rigid epitope
An analysis of Amino Acid Recovery (AAR) and Root Mean Square Deviation (RMSD) for the selected designs are provided in Figure 6:
30
30-
20-
15
10
23
Figure 6: All generated CDR H3 designs on rigid epitope: AAR (left) and RMSD (right) histogram.
FuncBind successfully generated novel antibody binders in this de novo redesign problem; in fact 54% were binders (42% with pKD values). 94% of all 190 submitted designs were successfully expressed and purified. Experimental results confirmed that 42% of all submitted designs were binders, with pKD values in the range [7.55, 1 11.29] (KD E [5.08 x 10-12 12 2.84 x 10- $IM) and average pKD of 9.56 (Kp 2.00 10 10-9M). PM). For comparison, the pKD of the parent antibody is 10.20 (Kp = 2.63 x 10 11 M). 12% were binders with no pKD ("bad" binders). We identified a 5X binder in that set.
Table 8: Average RMSD and AAR for binders and non binders on rigid epitope
Binders
Unassigned
Non-Binders
Global
RMSD
0.50
0.53
1.31
0.88
AAR 55.1
50.8
37.9
46.7
27

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Table Average RMSD and AAR for binders and non binders on flexible epitope
Binders
Unassigned
Non-Binders
Global
RMSD AAR
1.93
2.08
1.94
1.95
40.4
35.6
34.2
34.5
FuncBind
11.0
Seed
50-
30.3%
41.6%
Testee Desioon Fartion (16)
Binding nity (OKD)
300
35
12.1%
10
30
(no μiC)
Figure 7: In vitro validation of FuncBind's designs against rigid epitope; expression (left), binding affinity (center), binding rate (right). Expression rate is 93.68%.
C.3 Flexible epitope
An analysis of Amino Acid Recovery (AAR) and Root Mean Square Deviation (RMSD) for the selected designs are provided in Figure
mequency
PRODEITCE
10
10
Figure 8: All generated CDR H3 designs against a flexible epitope; target: AAR (top left) and RMSD (top right) histogram. Bottom: Logo of generated designs.
Experimental results confirmed that 100% of the designs expressed and 2% of all submitted designs were binders, with pKDs [6.95, 7,44, 8.08, 8.47] (KD E [3.38 x 10-9, 1.13 x 10 10-7]M) and an average pKD of 7.74 (KD = 4.03 x 10 10- For comparison, the pKD the parent antibody 10.58 (KD 6.32 x 10 10-1 (M) 10% were binders with pKD ("bad" binders); around Unassigned binders had a very reasonable SPR curves.
Looking at Table 9, no specific correlation between binders and non binders were found based on RMSD on the limited set of binders we had. Though higher AAR seemed to better (based on binder samples only).
28

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Table 10: Per-residue TS between the (a) seed MCP and (b) crystal MCP of the 20 sampled molecules in the pocket relaxed to wwwe mutant 90.
(a)
CYS
HIS
A20
B67
PHE
CYS
Ivwe mut90
0.63 - 0.24
0.24 + 0.17
0.26 + 0.07
0.14
0.12
0.29 + 0.14
0.53 + 0.21
(b)
CYS
HIS
PRO
GLU
PHE
CYS
Ivwe-CP
0.61 + 0.23
0.24 + 0.12
0.31 + 0.14
0.28 + 0.10
0.33 + 0.08
0.57 + 0.23
FuncBind
10.0
80
nity (OK3)
Fraction
60
20
0%
2.1%
Non-binders
Bindles
Binders
Figure 9: In vitro validation of FuncBind's ' designs against a flexible epitope; expression (left), binding affinity (center), binding rate (right). Expression rate is 100%.
D Identifying non canonical amino acids
Unidentified amino acids were determined by recognizing repeated patterns of peptide backbone atoms around a chiral carbon. All atoms stemming from C., including those in the side chain, were identified and labeled per canonical atom naming conventions. SMILES strings of unidentified amino acids were compared with canonnill amino acid library, assigning residue names when a match was found. If no match was found, the amino acid was labeled as unknown and added to the non-canonical library. In the output PDB file, canonical residue name assigned based on the closest alignment of atom naming patterns (e.g., C, Co2 N₂,) to a known canonical residue.
E
Macro cyclic peptides
E.1 Additional results
We perform the following studies:
Figure 10: qualitative comparison between MCP samples and the seed.
Table 10: per residue tanimoto similarity (TS) for molecules samples with the seed mutant (a) and the crystal MCP (b).
Figure 11 shows the categorization of the generated amino acids.
Figure 12 shows
reasonable and novel generated canonical amino acids.
Figure 5 shows how newly generated amino acid interacts with pocket residues that neither the seed nor a chemically similar amino acid same position engage.
29

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a.
4gw5
mutant 104
b.
1vwe mutant
90
1wb0
mutant
Figure 10: Results of sampling in pocket relaxed around MCP seed (grey) are in (a) blue in 4gw5
mutant 104 pocket, (b) purple for Iwwe mutant 90 pocket, and (c) 1wb mutant 389 pocket
40
Percentage
17.2%
8.7%
2.3%
L-Canonical
D-Canonical
N-methyl
Known NCAA
Unknown NCAA
Figure 11:
Proportion of amino acid types classified L-canonical, D-canonical, N-methylated, other known non-canonical amino acids (as annotated in our library), and unknown non-canonical amino acids in the sampled set.
30

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(3-hydroxy) cyclopentyl-G
(5,5)- dicarboxyl-L
(2me)-
-ffflooill
(3-amino)-F
cyclopentyl-A
H
H
H
H.N
H2N
OH
HO
2H₂
3AF
C1O
2XL
1MC
Figure 12: Examples of unknown or novel NCAAs that appeared in the sampled set but were not present in the initial test set library. Novel NCAAs are labeled with a formal name and an assigned 3-letter AA code.
b.
25
00
20-
Percentage
Count
60
40-
10
14 15
16
19
22
6
7
8
Length
Number Mulations
Figure 13: (a) Count of MCPs with each amino acid length the source MCP-protein dataset. (b) Percentage of the number of mutations in the curated MCP-protein bound dataset.
E,2 Data curation
The MCP-protein pair dataset was curated by randomly mutating the MCPs from the source dataset at 1-8 different sites, using a list of 213 distinct amino acids (Figure 13b). The closure bonds in the source dataset consist of 55% N-to-C (head-to-tail), 28% disulfide (cysteine-cysteine)) and 23% S-acetyl-cysteine. The remaining 4% contain other closure bonds, such as linkers, and were mainly avoided or modified in the curated MCP dataset so that mutations Rosetta. Mutations were avoided in amino acids involved disulfide bonds and S-acetyl-cysteine linkage. The amino acid list used for random mutation of the MCPs included L-canonical, D- canonical, N-methylated, and other non-canonical types-such as alpha-modified, beta-modified, and peptoid amino acids. Many of these non-canonical residues in the Rosetta non-canonical rotamer libraries [90]. Following mutation, the MCPs were relaxed using the fast-relax protocol, which involves iterative cycles of side-chain packing and all- atom minimization [84]. The Rosetta interface energy scores-representie
be easily implemented in
me-ppaaamtterized and available
the binding energy of the protein-peptice complex each position were calculated using the ref _ef__011____11111111111111111_21 _arrt_caaarrtt cart energy function. From each source MCP- MCP-protein structure, over 2,000 mutated and relaxed complexes were generated, and approximately 500 with the lowest Rosetta interface scores were selected for the curated dataset. Therefore, we were able to expand the source dataset to 186,685 total MCP-protein structures in the curated dataset.
E.3 Clustering and Splitting
The pairwise similarity protein sequences from the 641 protein targets the curated dataset was evaluated using the Longest Common Subsequence (LCS) method [91]. Similarity between each
31

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pair of sequences was calculated as the ratio of the length of their LCS to the length of the longer sequence. The target proteins were clustered together under a representative if their similarity score was greater than 0.5. If none of the similarity scores met the 0.5 threshold, new cluster was created with that protein as the representative. 208 distinct protein clusters were used for training, validation, and testing. Clusters containing more than 100 MCP-protein pair structures in total were included in the training set. From the remaining clusters, 100 were randomly assigned to the test set, while the rest were added to the validation set.
32


Reviews:
[{"review_id": "v28fZh158Y", "reviewer": "Reviewer_Bhid", "summary": "This paper introduces FuncBind, a unified generative framework for structure-based drug design that generates diverse target-conditioned molecules—including small molecules, macrocyclic peptides (MCPs), and antibody CDR loops—using a single model. Its core innovation lies in a modality-agnostic neural field representation, which models molecules as continuous atomic density fields rather than modality-specific formats, enabling training across varied molecular systems while handling variable atom counts and non-canonical amino acids. FuncBind employs a conditional latent score-based model with walk-jump sampling (WJS), enhanced by a multi-measurement strategy for efficient generation. The method achieves competitive performance against specialized baselines on established benchmarks for small molecules and antibody design and introduces a new dataset of MCP-protein complexes to address the lack of data for cyclic peptides. Notably, FuncBind generates chemically plausible structures, including novel amino acids, and demonstrates that data augmentation effectively replaces explicit SE(3)-equivariance constraints, offering a scalable alternative for structure-based drug discovery.", "questions": "1. Please provide a critical ablation study where you compare your unified FuncBind model against modality-specific versions. How do the results of these specialist models compare to the unified model on a per-task basis? Does the unified training lead to a performance improvement, degradation, or is it neutral?\n\n2. The introduction of a new, large-scale dataset for macrocyclic peptide generation is a fantastic contribution. How does FuncBind's performance on the MCP task compare to other methods? To establish a proper baseline, please apply at least one other relevant generative model to your new benchmark task.\n\n3. How does FuncBind's sampling speed compare to that of other common paradigms, such as autoregressive models (e.g., Pocket2Mol) or one-shot flow-based models? A quantitative statement would help readers understand the practical trade-offs of your approach.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strength:\n\n1.\tThe paper tackles a significant and challenging problem in computational drug discovery. The goal of creating a single, unified model that can operate across different therapeutic modalities (small molecules, peptides, antibodies) is ambitious and, if successful, would represent a major step forward in the generality and applicability of generative models. \n\n2.\tThe use of neural fields to represent all molecules as continuous atomic densities is an elegant approach. It naturally circumvents the challenges of varying atom/residue counts, different connectivity patterns, and diverse chemical types, providing a robust and flexible foundation for a modality-agnostic framework.\n\n3.\tThe introduction of a new benchmark for Macrocyclic Peptide (MCP) generation is a contribution to the community. MCPs are a crucial therapeutic class that sits between small molecules and biologics, but they are notoriously under-resourced in terms of public datasets and benchmarks for generative modeling. This contribution will be valuable for future research in this area.\n\n4.\tDespite being a generalist model, FuncBind achieves competitive, and in some cases state-of-the-art, performance against specialized baselines (e.g., achieving excellent RMSD in CDR loop redesign). This demonstrates the viability and potential of the proposed architectural framework and unified representation.\n\nWeakness:\nThe paper, despite its strengths, suffers from several critical weaknesses that undermine its core claims and the solidity of its contributions.\n\n1. The central hypothesis is unsubstantiated. The paper's core premise is that a unified model is superior because it can \"learn physical properties across diverse atomic systems.\" This claim of synergistic learning or knowledge transfer is the primary justification for the entire unified approach. However, this hypothesis is not tested or validated. The paper fails to address fundamental questions:\n\n    * What are the specific challenges of training on such diverse modalities simultaneously (e.g., conflicting optimization signals, modality imbalance, different length/size scales)? How does the model design address them?\n \n   * Is there any evidence of positive knowledge transfer? For instance, does training on small molecules and their intricate atomic interactions improve the model's ability to generate physically plausible CDR loops?\nWithout this analysis, the \"unification\" feels more like a simple concatenation of datasets rather than a principled approach. The motivation remains unproven.\n\n2. Lack of essential ablation studies. To validate the central hypothesis, the most crucial and necessary experiment is a direct comparison between the unified model and single-modality versions of the same model. The authors should have compared the performance of:\n    * FuncBind (Unified): Trained on all three modalities, as presented.\n    * FuncBind (Small Molecule only): Trained and evaluated only on CrossDocked2020.\n    * FuncBind (CDR only): Trained and evaluated only on SAbDab.\n    * FuncBind (MCP only): Trained and evaluated only on the new MCP dataset.\n\nThis ablation is fundamental. The current results only show that the FuncBind architecture is capable of handling these tasks, not that the unified training strategy itself provides any advantage. This is a major flaw in the experimental design.\n\n3. Marginal or inconsistent performance gains over specialized baselines. Across Tables 1 and 2, FuncBind's performance gains over existing specialist models are not consistently clear, which weakens the argument for its superiority.\n    * In Table 1 (Small Molecules): On this highly competitive benchmark, FuncBind achieves performance that is on par with, but not superior to, leading models like VoxBind and MolCraft. For instance, its Vina docking scores are slightly worse, and its Clash and Strain Energy metrics are less favorable. By only matching the performance of its direct predecessor (VoxBind), it raises the question: what is the benefit of the added complexity and unification if it does not lead to demonstrably better small molecule generation?\n    * In Table 2 (CDR Redesign): The story is more nuanced but still not an unqualified win. FuncBind shows a remarkable and significant advantage in structural fidelity (RMSD), which is a major achievement. However, its performance on other key metrics like Amino Acid Recovery (AAR) is inconsistent, sometimes outperforming but often lagging behind the baseline AbDiffuser. The authors should try to explain this. \n\n4. Insufficient Evaluation of the New MCP Benchmark: While the creation of the MCP benchmark is a commendable effort, its evaluation is critically incomplete. A \"benchmark\" by definition requires comparative data to be useful. In Table 3, the authors only report the performance of their own model, FuncBind, without a single baseline. This makes the reported metrics (e.g., TS=0.29, L-RMSD=2.19, Vina Dock=42%) completely uninterpretable. Are these numbers good or bad? The authors should have adapted at least one or two relevant protein generative models (e.g., RFDiffusion, which they cite as a key related work for peptide design) to their new task to provide a reference point. As it stands, the value of this contribution is diminished as it fails to serve its purpose as a benchmark.\n\n5. The technical components of FuncBind are largely an integration of existing methods: neural fields for molecular representation are from [16], score-based generation and WJS are from [17, 64]. While applying this combination to the novel problem of multi-modality generation is a contribution, its significance is tied to the justification for doing so. As highlighted in Weaknesses 1 and 2, this justification is missing. The contribution thus appears to be more of an engineering integration rather than a fundamental methodological advance that has been proven necessary or superior.\n\n6. The paper completely omits any discussion of sampling efficiency. Score-based models, especially those requiring many iterative sampling steps like WJS-m, are known to be computationally expensive. For practical drug discovery, where high-throughput virtual screening is paramount, generation speed is a critical metric of utility. A report on the wall-clock time to generate a batch of molecules, or a comparison with faster methods (e.g., one-step flows or autoregressive models), is essential for a comprehensive evaluation of the model's practicality. This omission leaves a gap in understanding the model's real-world viability.", "was_revised": true, "final_justification": "Through their rebuttal, the authors improved the manuscript's quality, demonstrated the effectiveness of their core claim via ablation studies, and refined their proposed benchmark. The main reason hindering further score improvement is that joint training on three modalities primarily enhanced generation diversity, while not significantly outperforming single-modality models in generation quality, thereby diminishing the paper's overall contribution.", "rebuttal": "**Unsubstantiated central hypothesis**. We appreciate the feedback regarding our central hypothesis and the call for an ablation study comparing unified and modality-specific models. This analysis is insightful and we have now included an ablation study to directly address this point. \n\nUnified training is inherently challenging, which explains the prior absence of models spanning the three modalities we consider. Our neural-field-based representation, however, made multi-modality training seamless and straightforward; we achieved this by simply conditioning the denoiser on the modality class and applying uniform rebalancing. While performance on individual saturated benchmarks could be further optimized with modality-specific tuning, our primary goal was to establish a robust unified model, not necessarily to achieve state-of-the-art on metrics known to poorly correlate with true binding.\n\nThe appeal of multi-modality lies in its potential for positive knowledge transfer, enabling better generalization by leveraging more diverse data and eliminating the need for separate models. Given the distinct nature of our modalities, evaluating this transfer is particularly insightful. To empirically address the reviewer's concern, we trained modality-specific models for small molecule, CDR loop, and MCP generation, and the results are compared against our unified model in the revised manuscript.\n\n**Ablation: unified vs. single-modality**. We conducted this ablation using similar compute resources and diffusion as the score-based generation method. The comparison considers uniqueness/diversity and performance metrics across modalities:\n\n* *CDR loop inpainting*: The specialized model shows a drop in sequence design uniqueness, with comparable performance on unique samples. Given uniqueness's importance in library design, the unified model's ability to maintain higher uniqueness is a notable advantage.\n\n| Loop | Uniqueness Unified | Uniqueness Specialized |\n|--|--:|--:|\n|H1|10.1|9.6|\n|H2|15.6|12.6|\n|H3|83.0|69.2|\n|L1|19.8|10.6|\n|L2|11.2|14.0|\n|L3|34.7|22.3|\n\nFor the H3 loop specifically on unique samples:\n| H3 loop | AAR | RMSD |\n|--|--|--|\n| Unified | 0.399 | 1.96 |\n| Specialized | 0.406 | 2.07 |\n\n* *Small Molecules (CrossDocked)*: Metrics remained largely comparable between unified and specialized models, indicating no significant performance degradation from unification.\n\n| Configuration | n_atoms_mean | vina_score_mean | vina_score_median | vina_min_mean | vina_min_median | qed | sa | diversity | clashes_mean | se_median |\n|--|--|--|--|--|--|--|--|--|--|--|\n| Unified | 20.00  | -5.78 | -5.64  | -6.34 | -6.16  | 0.48 | 0.68 | 0.73  | 7.21 | 166 |\n| Specialized | 19.68 | -5.70  | -5.64 | -6.29  | -6.09  | 0.50 | 0.67 | 0.72 | 7.20 | 194 |\n\n* *Macrocyclic Peptides (MCP)*: The unified model demonstrated superior performance in key structural metrics (TM-score, lRMSD, iRMSD) and cyclization fraction, with other metrics remaining similar.\n\n| Configuration | lRMSD | iRMSD | TM_score | TS_full | Ratio_>0.5_TSPR | cyclization_frac |\n|--|--|--|--|--|--|--|\n| Unified | 2.65 | 1.85 | 0.33 | 0.35 | 0.27 | 0.75 |\n| Specialized | 2.90 | 1.98  | 0.30 | 0.36 | 0.27 | 0.73  |\n\nIn summary, the unified model generally shows a positive to neutral effect on performance across modalities. A key benefit is the improved uniqueness, likely due to mitigating overfitting from a larger, more diverse training set. Furthermore, unification greatly simplifies experimental validation by eliminating the need to train separate models per modality. We note that transfer is likely to have a greater impact between more similar molecular classes (e.g., antibodies and proteins); this is an area for future work.\n\n**Performance on benchmarks.**\n* *Small molecules*: We acknowledge that FuncBind's performance on the highly competitive CrossDocked benchmark is on par with, or slightly behind, leading specialized models like VoxBind. However, achieving this parity while being a unified model capable of tackling significantly larger and more complex modalities like macrocyclic peptides and CDR loops—which VoxBind's discrete voxel representation inherently struggles with due to cubic scaling limitations —is a major achievement for generalizability and viability. Our primary goal was to demonstrate unification without significant performance compromise on individual tasks, rather than solely achieving state-of-the-art on (saturated) benchmarks.\n* *CDR redesign*: We appreciate your recognition of FuncBind's \"remarkable and significant advantage in structural fidelity (RMSD)\". This is indeed an unqualified win. Our Amino Acid Recovery (AAR) is competitive, and with recent enhancements using a diffusion model, FuncBind now leads on all AAR metrics as shown in the updated table below. Furthermore, our strong IMP (interface energy improvement) scores in the original manuscript, nearly double that of some baselines without explicit minimization, underscore the quality of our generated structures. \n\n|Loop|FuncBind AAR|FuncBind RMSD|AbDiffuser AAR|AbDiffuser RMSD|DiffAb AAR|DiffAb RMSD|\n|----|-----------|------------|--------------|----------------|---------|------------|\n|H1|88.0|0.36|76.3|1.58|65.8|1.19|\n|H2|79.7|0.44|65.7|1.45|49.3|1.08|\n|H3|44.8|1.82|34.1|3.35|26.8|3.60|\n|L1|91.0|0.58|81.4|1.46|55.7|1.39|\n|L2|91.2|0.82|83.2|1.40|59.3|1.37|\n|L3|82.9|0.60|73.2|1.59|46.5|1.63|\n*Table*: Comparison of Funcbind with diffusion model against baselines.\n\n* For practical validation beyond in silico metrics, we tested our antibody H3 redesign in the wet-lab using Surface Plasmon Resonance (SPR) in a de novo setting. This yielded a 42% binding rate on a target with a rigid epitope and 4% on a target with a flexible epitope. Further details will be provided in future revisions, pending legal clearance.\n\n**MCP baselines**. You correctly highlight the absence of baselines for macrocyclic peptide (MCP) generation, which is precisely why we introduced a new dataset for this modality. MCPs pose significant challenges for generative models due to their non-canonical amino acids, cyclization, and scarce training data. At submission, no other target-conditioned, structure-based MCP generative models handled non-canonical amino acids, precluding direct comparisons.\nTo address this, we now compare FuncBind with AfCycDesign [Rettie et al 25a] and RFPeptide [Rettie et al 25b], two models generating MCPs exclusively with canonical amino acids and N-to-C cyclization. As shown below, FuncBind achieves superior or similar metrics, notably lower average ligand RMSDs (2.47 vs 3.08 and 3.99) and interface RMSDs (2.1 vs 3.3 and 5.2). FuncBind also yields the highest proportion of designs with better docking scores (50% vs 7% and 18%). The only underperformance was in Tanimoto similarity (0.35 vs 0.39), expected as FuncBind accesses a larger set of non-canonical amino acids and different sequence lengths as opposed to these baselines. We encourage future comparisons on this new benchmark, especially for models handling non-canonical amino acids.\n|Model|TM-score|l-RMSD|i-RMSD|VinaScore|TanimotoSimilarity|\n|-----|--------|------|------|----------|------------------|\n|AfCycDesign|0.21|3.08|3.3|7|0.39|\n|RFPeptide|0.22|3.99|5.2|18|0.30|\n|Funcbind|0.33|2.65|1.85|50|0.35|\n\n[Rettie et al 25a] Cyclic peptide structure prediction and design using AlphaFold2\n[Rettie et al 25b] Accurate de novo design of high-affinity protein binding macrocycles using deep learning \n\n**Technical components**. We respectfully disagree that FuncBind is merely an integration of existing methods. Our core contribution is a novel neural field representation specifically for structure-conditioned generation, a previously unexplored area. This enabled us to develop one of the first unified models for generating small molecules, MCPs (with non-canonical AAs), and CDR loops, all conditioned on a target structure, achieving competitive or SoTA results across diverse chemical matter. The unified training, facilitated by this representation, was a direct byproduct of its inherent flexibility.\n\nUnlike the unconditional FuncMol, FuncBind is explicitly structure-conditioned. It introduces significant methodological advancements like replacing FuncMol's vector latent with a spatial latent tensor, which greatly improves performance by enabling U-Net architectures and simplifying structure conditioning. Finally, while FuncBind utilizes established score-based generative models (walk-jump sampling in the initial draft, and diffusion in revision), this is common practice in the field. Our primary aim was not to invent a new sampler, but to propose a new, highly compatible representation for structure-conditioned generation that can seamlessly integrate with any score-based generative framework.\n\n**Sampling efficiency**. We agree that sampling efficiency is a crucial practical metric. FuncBind demonstrates a significant speed advantage, sometimes an order of magnitude faster than many baselines, as shown in the table below (average time in seconds per molecule on A100).\n\n|Method|TargetType|InferenceTime(s/mol)|\n|------|-----------|--------------------|\n|FuncBind-diffusion|SmallMolecule|2.2|\n|FuncBind-WJS-16|SmallMolecule|5.7|\n|VoxBind|SmallMolecule|5.0|\n|Pocket2Mol|SmallMolecule|25.4|\n|TargetDiff|SmallMolecule|34.3|\n|DecompDiff|SmallMolecule|61.9|\n|FuncBind-diffusion|CDR|2.8|\n|FuncBind-WJS-16|CDR|7.3|\n|DiffAb|CDR|8.9|\n|AbDiffuser|CDR|34.4|\n\nUnlike FuncBind, these slower models may face more issues with real-world viability where high-throughput generation is critical. While lower sampling time is useful, we prioritize models with demonstrated high experimental binding rates, a benchmark FuncBind has achieved in CDR loop redesign."}, {"review_id": "Thc1LYQbAX", "reviewer": "Reviewer_n7Ct", "summary": "The paper presents a single, modality-agnostic generative framework that directly produces all-atom, structure-conditioned binders in three dimensions, seamlessly spanning small molecules, macrocyclic peptides, and antibody CDR loops. It does so by representing each atom type as a continuous spatial density field and embedding these fields into a shared latent space via a variational autoencoder; generative sampling is then performed through a novel “walk-and-jump” score-based diffusion process in latent space, conditioned on the target’s binding site and the desired molecular modality. After sampling, high-resolution density grids are converted back into discrete atomic coordinates using gradient-based peak refinement and cheminformatics bond inference. Experiments on CrossDocked2020, SabDab antibody loops, and a new macrocyclic-peptide benchmark show that this unified approach achieves docking scores, amino-acid recovery rates, RMSDs, and binding-affinity improvements that are on par with or exceed those of specialized state-of-the-art methods, all without requiring separate architectures or post-hoc backbone minimization.", "questions": "Which components of your neural-field design are most critical for performance?\n\nHow do bond lengths, angles, and torsion profiles of your generated molecules compare to high-quality crystal structures or established force-field minima?", "limitations": "Yes, with conclusion.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strength \nThe paper’s most compelling contribution is its truly unified treatment of three disparate molecular modalities--small molecules, macrocyclic peptides, and antibody CDR loops--within a single continuous-field framework. By representing each atom type as a smooth spatial density and embedding all-atom structures into a common VAE latent space, the authors avoid the need for separate graph or sequence-based architectures and specialized loss functions for each modality. \n\nMoreover, the neural-field formalism is both elegant and flexible. Decoding via multiplicative Gabor-filtered MLPs supports arbitrary atom counts and fine spatial detail, while score-based diffusion leverages standard 3D U-Net architectures without adding the complexity of equivariant networks. This shows that high-resolution, all-atom accuracy need not rely on explicit rotation equivariance or discrete bond priors, broadening the applicability of diffusion methods to large biomolecules.\n\n## Weakness \n\nThe decoding pipeline’s reliance on density-peak extraction, gradient ascent for atomic refinement, and OpenBabel bond inference introduces multiple heuristic stages that could each introduce errors--missing bonds, unusual torsions, or artefactual geometries. Indeed, the reported slight underperformance on strain-energy metrics versus VoxBind hints that the field representation may struggle to capture fine bond-angle or torsional constraints \n\nThe ablation studies and comparisons are insufficiently thorough. There is no systematic evaluation of how architectural choices, diffusion hyperparamters (number of Langevin steps, noise schedules), or conditioning strategies impact sample quality. Nor is there a head-to-head against recent equivariant diffusion methods on the same benchmarks. Such analyses would strengthen confidence that the unified neural-field approach is indeed the optimal design rather than a convenient simplification.\n\nIn addition to the concerns already noted, the way the empirical results are presented undermines clarity and easy comparison. For example, in the key performance table the authors have failed to highlight or bold the best numbers across methods.", "was_revised": true, "final_justification": "After going through the rebuttal response and other reviewer's review. I will keep suggesting accepting this paper.", "rebuttal": "We thank the reviewer for the thoughtful review and positive assessment of FuncBind's unified, modality-agnostic approach and its innovative use of neural fields. We appreciate your recognition of our framework's ability to span diverse molecular modalities and its competitive performance against specialized baselines. Next, we address the reviewers' concerns.\n\n**Decoding pipeline's reliance on heuristic stages.** We acknowledge that our multi-stage decoding pipeline involves heuristic steps. However, the current pipeline, while not fully differentiable end-to-end for bond inference, was a pragmatic choice to enable unified, all-atom generation across diverse molecular modalities, including variable atom/residue counts and non-canonical amino acids. Our competitive results across small molecules, macrocyclic peptides, and antibody CDR loops demonstrate that FuncBind successfully generates high-quality and chemically plausible structures despite these heuristics. Finally, we also mention that many of the small molecule baselines also rely on post-processing heuristics (eg, OpenBabel to infer bonds).\n\n**Bond lengths, angles and torsions**. While it is not standard to explicitly measure bond lengths, angles, and torsions in the benchmarks we considered, these structural qualities are indirectly evaluated through metrics such as strain energy for small molecules, RMSD and IMP (related to energy) for antibodies. Notably, our diffusion-based variation improved strain energy to a level comparable with VoxBind (166 for FuncBind with diffusion vs 188 for VoxBind), and our original IMP metric remains state-of-the-art by a significant margin. This indicates that our model generates high-quality structures, performing competitively—if not better—than baselines. Furthermore, qualitative assessments reveal consistent peptide and closure bonds in macrocycles. The successful generation of novel, chemically plausible non-canonical amino acids further supports the model’s ability to learn fundamental atomic relationships.\n\n**Ablation studies**. We conducted several ablation studies during FuncBind's development. Eg, preliminary experiments led us to select the EDM2 U-Net architecture for our denoiser (over ADM), adapting its conditioning mechanism for 3D tensors.\n\nBelow, we investigated various sampling configurations. First, we varied the number of sampling steps, as shown in the tables, as requested. For small molecule generation on CrossDocked, reducing sampling steps (e.g., from 256 to 64) leads to increased strain energy, while most other metrics remain stable, and clashes even improve. For antibody CDR loops, more steps generally improve performance, though uniqueness slightly decreases with increased steps.\n\n| Steps | n_atoms_mean | vina_score_mean | vina_score_median | vina_min_mean | vina_min_median | qed | sa | diversity | clashes_mean | se_median |\n |------|--------------|------------------|--------------------|----------------|------------------|-------|-------|-----------|---------------|------------| \n| 64 | 20.15 | -5.87 | -5.72 | -6.36 | -6.17 | 0.48 | 0.67 | 0.73 | 6.87 | 300 | \n| 128 | 20.14 | -5.82 | -5.66 | -6.39 | -6.16 | 0.48 | 0.67 | 0.72 | 7.04 | 237 |\n | 256 | 20.00 | -5.78 | -5.64 | -6.34 | -6.16 | 0.48 | 0.68 | 0.73 | 7.21  | 166 |\nTable: Impact of sampling steps on small molecule generation.\n\n| Step | uniqueness | AAR   | RMSD  |\n|------|------------|-------|--------|\n| 64   | 0.89      | 0.42 | 1.89  |\n| 128  | 0.86      | 0.44 | 1.85  |\n| 256  | 0.83      | 0.45 | 1.82  |\nTable: impact of sampling steps on CDR loop generation.\n\nFinally we perform an ablation on different sampling methods (walk jump sampling, multi measurement walk jump sampling and diffusion). We observe that adding more noise levels generally leads to better metrics, especially w.r.t. Strain energy. \n| Metric             | WJS-1 | WJS-16 | Diffusion |\n|--------------------|-------------|------------------|---------|\n| n_atoms_mean       | 13.06      | 22.9             | 20.0  |\n| vina_score_mean    | -5.09      | -6.35            | -5.78  |\n| vina_score_median  | -4.78      | -6.34            | -5.64  |\n| vina_min_mean      | -5.24      | -6.79            | -6.36  |\n| vina_min_median    | -5.05      | -6.65            | -6.16  |\n| qed                | 0.49       | 0.48             | 0.48   |\n| sa                 | 0.70       | 0.65             | 0.68   |\n| diversity          | 0.80       | 0.73             | 0.73   |\n| clashes_mean       | 4.67        | 7.4              | 7.21   |\n| se_median          | 1240        | 399              | 166     |\nTable: impact of sampling method\n\n**Components of FuncBind critical for performance**. There are two components to performance: 1) a good reconstruction performance and 2) a good denoising performance. As with most papers on latent diffusion, there is a trade-off between these two terms. Based on our experiments, the largest strength of our approach is the use of the latest EDM2 denoiser architecture which can be efficiently reused in this setting (with some adaptation from 2D to 3D data that we made to that network). Moreover, we significantly improved the reconstruction performance of our neural field based auto-encoder by using a spatial latent vector instead of a vector value latent space.\n\n**Equivariant baselines.** The vast majority of our baselines are recent equivariant diffusion models, e.g. DiffSBDD, Pocket2Mol, Molcraft, DecompDiff, TargetDiff on the small molecules and DiffAb, AbDiffuser on the antibodies. \n\n**Empirical results presentation.** We apologize for this oversight. In a revised version, we will bold the best numbers in all performance tables to enhance clarity and ease of comparison."}, {"review_id": "8kL6JqUADK", "reviewer": "Reviewer_d5cJ", "summary": "This paper introduces FuncBind, which improves upon VoxBind, the neural field based molecule generation method of Kirchmeyer et al., adding some improvements to the model, but also scaling to larger problems: macro-cyclic peptides and antibody CDRs.\n\nThe core contributions of this paper are:\n- a demonstration of target-conditional generation\n- demonstrating the robustness of a common core, applicable to different types and sizes of molecules\n- a spatially-structured latent representation\n- a successful application of _multimeasurement_ walk-jump sampling\n- a new benchmark dataset for macrocyclic peptides", "questions": "Expanding on (a) and (b) I would suggest to:\n- run a much more thorough comparison of VoxBind and FuncBind on the CDR and MCP tasks\n- better ablate which improvements of VoxBind lead to FuncBind's increased performance; e.g. the conditioning framework, the multimeasurement (vs not) walk-jump sampling (or even just other sampling methods).\n- expand on computation: I appreciate that the authors try 400M and 700M models, but it's not clear to me how that compares to other models; e.g. what is the runtime? Is the VoxBind baseline also 400M parameters? What if we scaled it? In general, the paper doesn't give a sense of what's a result of scale and more computation vs a result of better design\n- expand on the dataset contribution: similarly, I appreciate that the authors intend to release a new dataset, but it feels strange to not compare FuncBind on this benchmark to any other baseline, even if somewhat trivial. Doing so would help situate that dataset in terms of how common methods perform on it, and what are the challenges still faced.\n\nMinor: I'm not sure that FuncBind being described as \"a new modality-agnostic representation\" is correct, and feels a bit ambiguous. The modality is the vector field. This would feel like introducing a GNN architecture and claiming it to be a new modality-agnostic representation because everything can be a graph. Although I think I understand the intent of the authors, they may want to consider an alternative phrasing.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 1, "originality": 2, "strengths_and_weaknesses": "The paper's main strengths are that it demonstrates that the neural field approach is scalable, and that some clever modifications enable it to ingest more complex, spatial conditioning information. Another positive aspect of the paper is that it introduces a dataset in a domain where very little data exists.\n\nThe paper itself is well written, and I didn't have any problem understanding the method (although I was familiar with VoxBind).\n\nThe paper's main weakness is that it (a) does not appropriately compare itself to the model it is building upon, VoxBind, and that it (b) does not properly decompose the improvements that build FuncBind. This is unfortunate because it prevents us from learning and understanding which parts of the proposed method matter.\n\nIn the end, this paper reads more like a \"we delivered a model\"-paper than a scientific methods paper. What are the hypotheses being tested? Why or when are the proposed changes to VoxBind important? What have we learned from this paper? Other than that it's _possible_ to make performance improve.", "was_revised": true, "final_justification": "There were crucial results that were missing: some important ablations and some important comparisons to other baselines. The results provided in the rebuttal seem to support their claims and fill that gap, and in my opinion were the main missing piece from making the paper go from a \"we made this work\" paper to a \"here's how we can advance the field\" paper.", "rebuttal": "We thank the reviewer for the thoughtful review, particularly for recognizing FuncBind's scalability, its ability to handle complex spatial conditioning, and the significant contribution of our new macrocyclic peptide (MCP) dataset. We are also pleased that you found the paper well-written and the method clear. The reviewer's main concern is about lack of comparison with VoxBind, which we address first, followed by the other remarks. \n\n**Comparison of FuncBind and VoxBind.** Our core contribution introduces neural fields as a novel representation for structure-conditioned molecular generation, an approach previously unexplored in this context. While VoxBind is a conditional model utilizing discrete voxel grids—the discretized counterpart of neural fields—both methods share benefits like compatibility with powerful denoiser architectures and score-based generative frameworks, and ease of structure-based conditioning.\nHowever, the computational and memory costs of voxel representations scale cubically with molecular volume. In practice these models are only applicable to small molecule generation. This is why we did not compare FuncBind vs VoxBind on the CDR/MCP datasets: for the simple reason that VoxBind cannot be trained efficiently in these settings. Our proposed continuous neural field representation directly addresses this fundamental scalability challenge by offering significantly greater memory efficiency. This enabled its application to larger and more complex molecular systems such as MCPs and CDR loops.\n\n**This paper reads more like a \"we delivered a model\"-paper than a scientific methods paper. What are the hypotheses being tested? Why or when are the proposed changes to VoxBind important? What have we learned from this paper?**\nIn our work, we show that continuous neural field representation combined with modern computer vision architectures and efficient score-based sampling can effectively generate diverse all-atom molecular systems conditioned on target structures, without relying on modality-specific representations or explicit equivariance constraints. Through FuncBind, we learned that modality-agnosticism is achievable with a single model spanning disparate molecular modalities (small molecules, macrocyclic peptides, antibody CDR loops), demonstrating the power of unified representation. This is highly non-trivial. Furthermore, our results further validate that explicit SE(3) equivariance constraints are not strictly necessary for high structural accuracy when strong data augmentation is employed, as evidenced by our large improvement of prior SE(3) equivariant baselines on the task of CDR loop redesign. This work also provided the first experimental validation of multi-measurement walk-jump sampling (WJS-m) in generative modeling, showing its effectiveness and efficiency for sampling. \nThese learnings provide generalizable principles for potential future molecular design methodologies, moving beyond merely \"delivering a model.\"\n\n**Ablation studies**\nwe perform some additional ablation studies; first, we ablate the different sampling methods (walk jump sampling, multi measurement walk jump sampling and diffusion). We observe that adding more noise levels (from single measurement walk jump to diffusion) leads to better metrics, especially w.r.t. Strain energy. \n\n| Metric             | WJS-1 | WJS-16 | Diffusion |\n|--------------------|-------------|------------------|---------|\n| n_atoms_mean       | 13.06      | 22.9             | 20.0  |\n| vina_score_mean    | -5.09      | -6.35            | -5.78  |\n| vina_score_median  | -4.78      | -6.34            | -5.64  |\n| vina_min_mean      | -5.24      | -6.79            | -6.36  |\n| vina_min_median    | -5.05      | -6.65            | -6.16  |\n| qed                | 0.49       | 0.48             | 0.48   |\n| sa                 | 0.70       | 0.65             | 0.68   |\n| diversity          | 0.80       | 0.73             | 0.73   |\n| clashes_mean       | 4.67        | 7.4              | 7.21   |\n| se_median          | 1240        | 399              | 166     |\nTable: impact of sampling method\n\nWe also include a study on the impact of the latent space’s dimension; we observe that more latent dimensions helps the model better perform loop inpainting (both higher uniqueness and metrics on unique samples).\n| Config       | Uniqueness | AAR  (unique)  | RMSD (unique)  |\n|----------------------|-------------|--------|-------|\n| code dimension 128   | 83.0   | 0.40 | 1.96  |\n| code dimension 64    | 65.5  | 0.35 | 2.11  |\n\n**Computation.** FuncBind's design, as a latent approach with continuous neural fields, inherently scales more efficiently with model size, enabling it to leverage increased computation more effectively. This contrasts with voxel-based methods like VoxBind, which face significant memory constraints that limit their scalability for larger molecules due. Thus, FuncBind's architectural choices are key to its demonstrated scalability and performance. For context, AbDiffuser reports 169M parameters, RFDiffusion ~100M, and VoxBind ~120M. \n\n**Runtime.** Below is the table of average sampling time per molecule for different methods on small molecules and antibody CDR loops with one A100 GPU. FuncBind achieves preferable runtime than other methods (sometimes by an order of magnitude). We attribute this to the efficient latent representation, to the use of standard architectures (CNN and self-attention layers only) and to the efficiency of score based generative models.\n| Method                        | Target Type                | Inference Time (s/mol) | \n|-------------------------------|------------------------------|-------------------------------|\n| FuncBind-diffusion     | Small Molecule     | 2.2   |\n| FuncBind-WJS-1         | Small Molecule    | 2.1  |\n| FuncBind-WJS-16        | Small Molecule   | 5.7  |\n| VoxBind                       | Small Molecule          | 5.0                                |  \n| Pocket2Mol                  | Small Molecule          | 25.4                             |\n| TargetDiff                     | Small Molecule          | 34.3                              | \n| DecompDiff                  | Small Molecule          | 61.9                             |\n| FuncBind-diffusion       | CDR                          | 2.8                               |  \n| FuncBind-WJS-1           | CDR                          | 2.7                               |  \n| FuncBind-WJS-16         | CDR                          | 7.3                               |  \n| DiffAb                           | CDR                           | 8.9                               |     \n| AbDiffuser                    | CDR                           | 34.4                             |  \n\n**MCP dataset and baselines.** You correctly highlight the absence of baselines for macrocyclic peptide (MCP) generation, which is precisely why we introduced a new dataset and benchmark for this challenging modality. MCPs pose significant challenges for generative models due to their non-canonical amino acids, cyclization, and scarce training data. At submission time, no other target-conditioned, structure-based MCP generative models could handle non-canonical amino acids, making direct comparisons impossible.\nTo address this, we now provide a comparison of FuncBind with AfCycDesign [Rettie et al 25a] and RFPeptide [Rettie et al 25b], two generative models that generate MCPs exclusively with canonical amino acids and N-to-C cyclization. As the table below shows, FuncBind achieves better or similar metrics, particularly with lower average ligand RMSDs (2.47 vs 3.08 and 3.99) and interface RMSDs (2.1 vs 3.3 and 5.2). FuncBind also yields the highest proportion of generated samples with docking scores better than the seed (50% vs 7% and 18%). The only metric where FuncBind samples underperformed was the fraction of residues with a Tanimoto similarity greater than 0.5 (0.38 vs 0.39), which was expected as FuncBind has access to a larger set of non-canonical amino acids and different sequence lengths as opposed to these baselines in its sampling process. We encourage future comparisons of MCP models, especially those handling non-canonical amino acids, against FuncBind on this new benchmark.\n| Model         | TM-score | l-RMSD | i-RMSD | Vina Score | Tanimoto Similarity | \n|---------------|----------|--------|--------|-------------|----------------------|\n| AfCycDesign   | 0.21     | 3.08   | 3.3    | 7           | 0.39                 |\n| RFPeptide     | 0.22     | 3.99   | 5.2    | 18          | 0.30                 | \n| Funcbind      | 0.33     | 2.47   | 2.1    | 50          | 0.38                 | \n\n[Rettie et al 25a] Cyclic peptide structure prediction and design using AlphaFold2\n[Rettie et al 25b] Accurate de novo design of high-affinity protein binding macrocycles using deep learning\n\n**Minor: \"modality-agnostic representation\" phrasing**.  We will revise the current phrasing to clarify that our \"modality-agnostic representation\" refers to the unified approach of representing diverse molecular structures (small molecules, peptides, proteins, etc) as continuous atomic density fields, rather than using distinct, modality-specific representations (e.g., graphs for small molecules, amino sequences or backbone coordinates for proteins). This allows a single model to be trained across these varied chemical entities (and allows us to trivially model non-canonical amino acids)."}, {"review_id": "yWUQfgRiW0", "reviewer": "Reviewer_e5xg", "summary": "This paper presents FuncBind, an all-atom conditional generation model by representing molecular structures as neural fields, which map coordinates to continuous atomic densities. The authors evaluate FuncBind on protein target-conditioned small molecule design in 3D, antibody complementarity-determining region (CDR)  loop generation, and macrocyclic peptide (MCP) generation. FuncBind achieves competitive results on metrics when trained to generate ligands for CrossDocked protein targets, and is state-of-the-art on the SabDab dataset of protein-antibody crystal structures. This work additionally presents a new protein-MCP complex dataset containing ~190,000 synthetic complexes.", "questions": "1. Does one-step sampling during inference yield a speedup compared to diffusion- or flow matching-based methods? This would be an interesting point of comparison that can motivate FuncBind as a faster alternative.\n\n2. How were the 641 complexes chosen from the PDB to build the MCP dataset?\n\n3. Would it be possible to adapt the approach to partial molecular optimization by fixing moieties on a seed known to take part in crucial binding interactions? Fine-grained modifications to known binders is of particular interest in hit-to-lead optimization.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. FuncBind achieves competitive results for both small molecule design with variable functional groups and all-atom canonical and non-canonical amino acid generation, demonstrating its robustness to multiple molecular modalities. \n2. FuncBind achieves state-of-the-art performance on CDR loop generation compared to other methods.\n3. The model implements data augmentations rather than requiring equivariance constraints, which allows it scalability and flexibility to various architectures.\n\n\nWeaknesses:\n1. Although it is explained in reference [16], the authors do not define the terms \"seed\" or \"density field\" despite the concept being central to the decoding process. \n2. The authors claim that the base FuncBind model leads to \"high molecular validity\", but 2D molecular validity metrics are not reported in any of the experiments.\n3. The authors do not compare FuncBind to any baselines for the newly-introduced MCP generation task.\n4. FuncBind appears to be a reasonably straightforward extension of FuncMol [16], and some of its core methodologies (e.g. sampling, loss) do not deviate extensively from those presented by FuncMol.", "was_revised": true, "final_justification": "The paper presents a novel method for generating small molecules, MCP, and CDR loops in a non-equivariant manner. Despite the lack of baselines, the results, particularly in regards to inference-time speed up, are reasonably compelling.", "rebuttal": "We thank the reviewer for the comprehensive review and positive recognition of FuncBind's competitive performance across multiple molecular modalities and its state-of-the-art results in CDR loop generation. We also appreciate the reviewer's acknowledgment of our model's robustness and the benefit of using data augmentation over explicit equivariance constraints. Next, we address reviewers’ weaknesses and questions.\n\n**Undefined Terms (\"seed,\" \"density field\").** Thank you for pointing out the missing definitions. We will clarify these terms in the main paper.\n* Seed: Refers to the original binder present in the co-crystal structure.\n* Density field: A function that maps 3D coordinates to n-dimensional atomic occupancy values, where n is the number of atom types. This continuous generalization of voxels offers advantages over point cloud representations, including compatibility with expressive neural network architectures, implicit handling of variable atom/residue counts, and an all-atom formulation across modalities.\n\n**FuncBind as an extension of FuncMol [16]:**\nWhile FuncBind builds upon the neural field concept introduced in FuncMol, it proposed several significant and non trivial innovations:\n* *Structure-conditioned generative model*. Unlike FuncMol, FuncBind is explicitly designed as a structure-conditioned generative model, a setting with more practical value than unconditional generation especially for drug discovery.\n* *Modality-agnosticism*. FuncMol focused primarily on small molecules and small MCPs. FuncBind is the first framework to unify all-atom generation across three distinct and challenging modalities (small molecules, MCPs, and CDR loops) within a single model, demonstrating broad applicability and leveraging diverse training data.\n* *Spatially arranged feature map*. We introduce a spatially arranged feature map (Figure 1b) in the latent space, a departure from FuncMol's global embedding. This innovation allows FuncBind to better capture local spatial information, scale to larger molecules, and integrate expressive 3D U-Net architectures for denoising.\n* *Multi-measurement walk-jump sampling (WJS-m)*. We provide the first experimental validation of WJS-m in generative modeling applications (Section 3.2.2), demonstrating its effectiveness in improving sampling by effectively decreasing noise levels upon accumulation of measurements, which is a novel contribution in this context. FuncMol was limited to single measurement walk-jump sampling which we observed has lower validity. \n* *Novel Dataset and Benchmark*. The introduction of the MCP dataset and benchmark is a substantial contribution, enabling future research in this understudied area.\n\n**2D Molecular validity metrics.** Our claim of \"high molecular validity\" (Lines 192-193) refers to the successful inference of chemically sensible 3D structures and bonds by OpenBabel, which inherently verifies 2D validity. We observed that increasing the number of noise levels, from walk-jump sampling to multi-measurement walk-jump sampling and ultimately to diffusion (approximating infinite noise levels), directly correlates with a higher fraction of molecules passing the OpenBabel filter. In practice, our method achieved around 50% molecular validity performance on CDRs, meaning half of our generated molecules passed the OpenBabel filter. We note that our fast sampling speed ensures that this validity rate does not become a bottleneck for the utility of our method.\n\n**Baselines for MCP.** You correctly highlight the absence of baselines for macrocyclic peptide (MCP) generation, which is precisely why we introduced a new dataset and benchmark for this challenging modality. MCPs pose significant challenges for generative models due to their non-canonical amino acids, cyclization, and scarce training data. At submission time, no other target-conditioned, structure-based MCP generative models could handle non-canonical amino acids, making direct comparisons impossible.\nTo address this, we now provide a comparison of FuncBind with AfCycDesign [Rettie et al 25a] and RFPeptide [Rettie et al 25b], two very recent generative models that generate MCPs exclusively with canonical amino acids and N-to-C cyclization. As the table below shows, FuncBind achieves better or similar metrics, particularly with lower average ligand RMSDs (2.47 vs 3.08 and 3.99) and interface RMSDs (2.1 vs 3.3 and 5.2). FuncBind also yields the highest proportion of generated samples with docking scores better than the seed (50% vs 7% and 18%). The only metric where FuncBind samples underperformed was the fraction of residues with a Tanimoto similarity greater than 0.5 (0.38 vs 0.39), which was expected as FuncBind has access to a larger set of non-canonical amino acids and sequence lengths unlike the baselines in its sampling process. We encourage future comparisons of MCP models, especially those handling non-canonical amino acids, against FuncBind on this new benchmark.\n| Model         | TM-score | l-RMSD | i-RMSD | Vina Score | Tanimoto Similarity | \n|---------------|----------|--------|--------|-------------|----------------------|\n| AfCycDesign   | 0.21     | 3.08   | 3.3    | 7           | 0.39                 |\n| RFPeptide     | 0.22     | 3.99   | 5.2    | 18          | 0.30                 | \n| Funcbind      | 0.33     | 2.47   | 2.1    | 50          | 0.38                 | \n\n[Rettie et al 25a] Cyclic peptide structure prediction and design using AlphaFold2\n[Rettie et al 25b] Accurate de novo design of high-affinity protein binding macrocycles using deep learning\n\n**Sampling time**. FuncBind is not tied to any specific score-based sampling methods. In fact, we tried three generative models: single measurement and multi measurement walk jump sampling, and diffusion. All these approaches leverage several sampling steps at the different noise levels (in walk jump sampling, these sampling steps are called “walk”). Despite using several sampling steps (500 for single measurement, 1600 for multi measurement and 256 for diffusion), we still achieve the fastest sampling time, owing to our latent formulation.\n| Method                        | Target Type                | Inference Time (s/mol) | \n|-------------------------------|------------------------------|-------------------------------|\n| FuncBind-diffusion     | Small Molecule     | 2.2   |\n| FuncBind-WJS-1         | Small Molecule    | 2.1  |\n| FuncBind-WJS-16        | Small Molecule   | 5.7  |\n| VoxBind                       | Small Molecule          | 5.0                                |  \n| Pocket2Mol                  | Small Molecule          | 25.4                             |\n| TargetDiff                     | Small Molecule          | 34.3                              | \n| DecompDiff                  | Small Molecule          | 61.9                             |\n| FuncBind-diffusion       | CDR                          | 2.8                               |  \n| FuncBind-WJS-1           | CDR                          | 2.7                               |  \n| FuncBind-WJS-16         | CDR                          | 7.3                               |  \n| DiffAb                           | CDR                           | 8.9                               |     \n| AbDiffuser                    | CDR                           | 34.4                             |  \n\n**MCP dataset complex selection**. The 641 MCP-protein complexes are all the complexes we could isolate from the RCSB PDB [19] at the time of data curation.\n\n**Adaptability to partial molecular optimization**. Yes, our approach is adaptable to partial molecular optimization and fixing specific moieties. In fact, the antibody CDR loop generation task (Section 4.2) is a direct instance of this: it involves generating the CDR loops with a fixed antibody framework dock and epitope. Our model can be directly trained for this type of constrained generative task due to the continuous neural field representation and its ability to capture local information. As mentioned by the reviewer, this inherent flexibility makes FuncBind promising for applications like hit-to-lead optimization, where fine-grained modifications to known binders are critical."}]

Author rebuttal:

Meta-review: Summary and Strengths: 1. This paper presents an ambitious and significant advance by creating a unified model that generates diverse therapeutic molecules. 2. Its elegant approach uses neural fields to represent molecules continuously, overcoming key structural challenges.  3. The model achieves competitive, sometimes state-of-the-art, results against specialized alternatives and provides a valuable new benchmark for the under-resourced area of macrocyclic peptides. 

Weaknesses: 1. The paper's central hypothesis lacks support. 2. Critical ablation studies are absent. 3. The performance gains over specialized baselines are marginal or inconsistent. 4. The clarity of the empirical results is undermined by poor presentation and a lack of clear definitions for central concepts.

The paper is recommended for acceptance as the authors have satisfactorily addressed all major reviewer concerns, leading to a consistent consensus for acceptance based on the paper's valuable contributions.
Decision: Accept (poster)