UniIF: Unified Molecule Inverse Folding
Authors: Zhangyang Gao
Jue Wang
Cheng Tan
Lirong Wu
Yufei Huang
Siyuan Li
Zhirui Ye
Stan Z. Li
Venue: NeurIPS 2024 poster (2024)
Keywords: molecule learning
protein design
RNA design
Primary area: machine_learning_for_other_sciences_and_fields

Abstract: Molecule inverse folding has been a long-standing challenge in chemistry and biology, with the potential to revolutionize drug discovery and material science. Despite specified models have been proposed for different small- or macro-molecules, few have attempted to unify the learning process, resulting in redundant efforts. Complementary to recent advancements in molecular structure prediction, such as RoseTTAFold All-Atom and AlphaFold3, we propose the unified model UniIF for the inverse folding of all molecules.  We do such unification in two levels: 1) Data-Level: We propose a unified block graph data form for all molecules, including the local frame building and geometric feature initialization. 2) Model-Level: We introduce a geometric block attention network, comprising a geometric interaction, interactive attention and virtual long-term dependency modules, to capture the 3D interactions of all molecules. Through comprehensive evaluations across various tasks such as protein design, RNA design, and material design, we demonstrate that our proposed method surpasses state-of-the-art methods on all tasks. UniIF offers a versatile and effective solution for general molecule inverse folding.

UnilF: Unified Molecule Inverse Folding
Zhangyang Gao 1,2,t, Jue Wang 12,, ,CCeng Tan 122,, Lirong Wu 2 Yufei Huang 2, Siyuan Li Zhirui Ye Stan Z. 2 Zhejiang University? Westlake University
Abstract
Molecule inverse folding has been long-standing challenge in chemistry and biol- ogy, with the potential to revolutionize drug discovery and material science. Despite specified models have been proposed for different small- macro- macro-molecules, few have attempted to unify the learning process, resulting in redundant efforts. Com- plementary recent advancements in molecular structure prediction, such RoseTTAFFld All-Atom and AlphaFold3, we propose the unified model UnilF for the inverse folding of all molecules. We do such unification in levels: 1) Data- Level: We propose a unified block graph data form for all molecules, including the local frame building and geometric feature initialization. 2) Model-Level: We introduce geometric block attention network, comprising a geometric interaction, interactive attention and virtual long-term -erm dependency modules, to capture the 3D interactions of all molecules. Through comprehensive evaluations across various tasks such as protein design, RNA design, and material design, we demonstrate that our proposed method surpasses state-of-the-ard methods on all tasks. UnilF offers a versatile and effective solution for general molecule inverse folding.
Introduction
Molecule inverse folding plays pivotal role in drug and material design, enabling scientists to synthesize novel molecules with the desired structure. Previously, many studies focus either
macromolecules [9 33 21
17
I0.
or small molecules [6] 26 16
28
14 31]
separately, leaving the challenge of inverse folding general molecules. For example, the advanced
small molecule model 6 31 take atoms as basic units; the macromolecule models [ TO consider predefined microstructures (such amino acids and nucleotides) the basic units. Additionally, even for the same molecule, different models employ varying strategies to extract geometric features. Complementary the great success of Rose All-Atom (25) and AlphaFold3 [I] in molecular structure prediction, we propose unified model, UnilF, for the inverse folding of all molecules.
By comparing small- and macro- moleellll,, we identify three challenges toward the unified model: (I) Unit Discrepancy: The macromolecules takes predefined microstructures (amino acids and nu- cleotides) the basic units, while small molecules takes atoms as basic units. (2) Geometric Featur- izer: Different studies employ various strategies for extracting geometric features from structures, such distance, angles and tensor product; there are lack of unified featurization strategy. (3) Sys- tem Size: The mmall-molecules allow the full tention transformer to learn long-term dependen- cies, but the quadratic computing cost limits the mechanism scalling up to macro-molecular sys- tems. Alternatively, previous research use sparse GNN, which suffers from the limited local receptive field that causes over-smoothing and over- squashing 29] In addition, developing unified model working well for all molecules challenging.
20 Amino acids
Nucleotides
118 Atoms
Tokens
Unified Inverse Folding Model
Structure
Protein
RNA
Molecule
Figure Unified molecule inverse folding.
Equul Contribution, "Corresponding Author.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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The unit discrepancy makes it challenge to adapt methods across small- and macro-molecules, which explains the divergence between these two research lines. As a solution, we propose a frame-based block to unify the representation of amino acids, nucleotides, and atoms: group of atoms with varying size treated as a block with fixed size. Each block includes decoupled equivariant basis and invariant features, generalizing the representation of AlphaFold2 and other mmall-molecule methods.
The geometric featurizer is necessary to capture the geometric interactions between blocks. We initialize the equivariant block basis using predefined rules or a learnable GNN layer, and then constructing invariant block features based on these basis. The key operation and dot product to capture the geometric interactions between virtual atoms. We reuse the featurizer in each model layer to interactively learn updated geometric features, where the concept of directed virtual atom is introduced to enhance the pairwise interactions. We show that the unified featurizer works well across protein design, RNA design, and material design.
use local coordinates
We use sparse GNN to address the system size issue, while maintaining the ability to capture long- term dependencies. The transformer-style protein models like AlphaFold and RosettaFold require a substantial amount of GPU memory. Sparse GNNs, on the other hand, are criticized for their tendency over-smooth and over-squash due to their limited local receptive field. To be efficient while preserving the ability to capture long-term dependencies, we introduce global virtual blocks. Each virtual block is connected to all real blocks, serving as an information exchange agent.
We conducted comprehensive experiments across various tasks, including protein design, RNA design, and material design, to demonstrate the effectiveness of UnilF. The results show that UnilF achieves state-of-the-art performance on all the tasks, which is non-trivial and may benefit the machine learning, drug discovery, and material science communities.
2 Related work
Unification. Unified molecular learning has attracted increasing attention in recent years. RoseTdAFold All-Atom (RFAA) (25) and AlphaFold3 [ are two representative models that have achieved remarkable success in protein structure prediction. RoseTTAFold All-Atom uses an atom- bond graph for small molecules and a frame graph for macromolecules. AlphaFold3 uses a bi-level representations, i.e., atom representation and token representation, for all molecules. The token con- cept is requivalent to the block concept in this paper, which means a group of atoms, such as a amino acid or a nucleotide. GET 24 and EPT 20 are two recent models that use block representation for both small and macromolecules and introduce new equivariant transformer backbone. Unlike RFAA (25,, which specifies attom-bont graph for small molecules, our model employs a unified block graph for all molecule types and do not require the atom-bond graph. Our model also differs from Alpha ①1. GET 24 and EPT 20 the that we introduce the vector basis for each block.
Protein Inverse Folding. Recent research use k-NN graph to represent the 3D structure and employ graph neural networks for protein inverse folding. GraphTrans [19] uses the graph attention encoder and autoregressive decoder for protein design. GVP 21 proposes geometric vector perceptrons introduces global graph attention for
to learn from both scalar and vector features. GCA 33
32 is developed for scenarios where partial sequences are known while not reporting results on standard benchmarks. Recently, AlphaDesign [9]. ProteinMPNN 44. ESMIF [7]. LMDesign 40. KW KWDesign (8). VFN [27] achieves dramatic improvements. A benchmark
learning contextual features. In addition, ProteinSolver
is proposed to comprehensively evaluate protein design models.
RNA Inverse Folding. RNA inverse folding is a challenging task due to the complex secondary structure and tertiary structure. Traditional methods 33 include colony optimization and constraint programming, in addition to adaptive walk, simulated annealing and Boltzmann sampling. Recent deep learning method RDesign [34] has achieved promising results and build benchmark for AI researchers to follow-up. RiboDiffusion [8] use diffusion decoder to generate RNA sequences conditioned on the backbone structure embeddings.
Material Design. Deciding which chemical compositions are likely to form compounds is a critical task in material design (28 [4]. An important application is to substitute lattice-site elements or
ionic species within existing compounds that exhibit similar chemical behaviors. Wang et al. (36) successfully employed the elemental substitution method to discover 18,479 stable compounds out
of a pool of 189,981 potential candidates. Recently, Jensen et al. 6 introduced a open dataset for
material design and established benchmark for evaluating deep learning models in this domain.
2

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Method
3.1 Overall Framework
As shown Fig. we propose the unified model for general molecule inverse folding. The key insights include: (I) transforming all molecules into block graphs, where each block represents amino acid, nucleotide, or atom; (2) proposing geometric featurizer to initialize geometric node and edge features; and (3) introducing a new GNN layer with long-term dependencies to learn expressive block representations. Our unified model achieves competitive results across diverse tasks, including protein design, RNA design, and material design.
Protein Design
Prior
Block Fie Attintoon
Block Field Attertion
Block Fledd Altirtion
Busis
Micromolecules
Prodictoo
ANA Design
Basis
Design
Small molecules
Graph Featurtzer
Blook Graap Noural Network
Tiskks
Pipeline
The Overall framework. (1) The model treat all types of molecules as block graphs. For macro- molecules. predefined frames based amino acids and nucleotides; for small molecules, learn the local frame each block by one-layer GNN. (2) geometrie featurizer is used initialize the geometric node feature and edge features, (3) We propose the block graph attention layer, based on which we build the block graph neural network to learn expressive block representations. (4) Finally, we show that the UnilF can achieve competitive results diverse tasks, ranging from protein design, RNA design and material design.
Figure 2:
3.2 Block Graph
We introduce the block graph to represent all types of molecules, where the key insight is to transform irregular set of atoms (varying size) as regular block representation (fixed size).
nitrogen alom
phosphurus atom
carbon atom
alphu carbon atom
D5,-1
alpha
dirrettion
cadbon alom
Rotation vector
chain direction
oxygen soom
chuin direction
onygem
My-s
C5
04
Base
0z
U2A
C42
C12
C3,
Block
CL
Block
03
N2
02,
C2
Paaa
Block
Protein
Figure 3: Blocks different molccules. The basic building blocks include amino acids, nucleotides and atoms.
Atom-based Block Representation. A block B (a,, 21 contains set of atoms (ay,, ) where D4 E R and z, E Rd represent the equivariant coordinate and invariant features, such as the atom type. The common block types include amino acids, nucleotides, and atoms, which are represented as Bfold Brna and Bainal respectively. Formally, we write Bfold {xx Neyyoa and graa
{ heyrr,,, where yoold (N, C1, C2, O} and C5, C4, C3, C2 05, O4, O3, O2) are the sets atoms for amino acids and nu-
yrne = (P, (5,C5, cleotides, respectively. For small molecules, each atom a represents block Bemol {(... 2a)) where < < 118. As the block size [B] varies for different types of blocks, the atom-based blocks representation could not directly applied for unified modeling.

---

Frame-based Block Representation. We introduce frame-based block representation unify the model- ing of all molecules. A block B = (F. f) contains the equivariant frame F and invariant feature vector f E Rdd The local frame F(R, t) contains the axis matrix = [e1, e2, E3, .3. ee] and translation vee- tor t. We set & as the coordinate of the representative atom, i.c., C1 of macromolecules and the atom itself of small molecules. Following AlphaFold2, the special case that R E R3.3 is orthogonal. However, additional experiments show that the model can also work well with non-orthogonal axis matrix. For macro- molecules, the axis matrix R is predefined based amino acids and nucleotides, while for small molecules, we learn the axis matrix R it does not have prior common structure patterns. The frame-based block representation decouples geometric information: (I) the local frame basis describe the equivariant pose; (2) the invariant feature vector could embed the atom type and invariant local structure patterns for different tasks. More importantly, the dimension of the frame-based block representation is fixed,
Used for Unified Modeling
Basis Foatures
Frame-based Block (fixed size)
consider
Atom-based Block (varying size) Figure Unified molecule inverse folding.
which is beneficial for the unified modeling; we build block features in Sec. 3.3
Frame-based Block Graph. Given a molecule MM- = {B, [B,)"=> containing blocks, we build the block graph 2 E) using kNN algorithm.
the block graph, the s-th node represented as B, = [F,, fa), and the edge between (s, t) is represented as B., = fet). The relative frame is defined as Fst F-1 o Ft. Inspired by [22 T0 271, we modify PiFold featurizer to initialize the geometric node feature f. and edge feature fa.t; refer to Sec. 3.3
Relation Other Methods. The frame-based block is generalized data form of AlphaFold2 and other methods. If R is required to be rotation matrix, the frame-based block is equivalent to AlphaFold2's local frame; otherwise, is equivalent to represent the atom as invariant feature h and equivalent vector similar to GVP 21 and DimeNet [12
3.3 Block Graph Featurizer
Learning Local Frame. For small molecules, there is no predefined local frame, and we need to learn the local frame for each atom. Given the the molecule M = {(z,, )
1, we use 1 1-layer of GNN to initialize the atom representation {zx) (zx)==1 a=1 <- BlockGAT( (( 2, [M)) where the inital
IM
local frames are (II, 0). The rotation vector т, of the s-th atom is constructed by message passing:
r. (a))
Σ EKEN, c.P(Z...
Øk Eg
(I)
|
xx|
REN.
Nr is the s-th atom's 's neighbor system. Let the direction (Tx, To r2) represent the rotation axis and angle, compute the quanternion and rotation matrix Rt:
|| and magnitude 0 =
||r,||
2: 5'= sin 0 2
as = [W, 2]
[cos
2. r2 sin
2' ryy sin
1 - 2y²² 2x²
2xy 2ow
2xz + 2yw
(2)
Rg = ey, ex]
2xy + 2zw
- 2x - 22
2yz 2rw
2xz 2yw
2yz + 2rw
2r 2y²
Finally, the local frame of the s-th atom is T, (R,, t.), where t, is the atom coordinate. Experiments show that learning rotation vectors consistently outperforms learning schnid--orthoognallzed axises.
Node Geometric Feature.
The invariant block feature captures the atom type and the local structure:
pos
R [I; t.) = F o x; Equivalent to invariant features, local structure
(3)
fz
MLP(z,,247)
Pooling atom features as block features, embed atom type
B.
ieB.

---

The inverse frame operation I project the equivalent global coordinates to the invariant local coordinate, i.e., foooal = F-1 aloobal Rg (aollobal t. We use MI.P to embed atom type and local coordinates. All atom features the same block are pooled get the block feature fx.
Edge Geometric Feature. We initialize pairwise features following the principle that
Edge features capture the directed 3D interactions.
Instead of using mutually constructed distance and angle features, concatenate the local coordinates of two blocks to fully describe their 3D positions. Given B, and B, with global coordinate matrices as Xg E Rlo.13 and X, Rl the invariant edge features following direction is
fa. T (X.)
where T (RT -R t,) projects equivariant global coordinates to invariant local coordinates.
3.4 Block Graph Attention Module
Lipttte
Catae Edge Alarrion
Get- tetoiireraiion
xL
|+3
f
Consmrction
Konduad Fot Cops
Real+Virtual
Geo-Inter leo-Interactton
Gated Edge Attantion
Figure 5: Block Graph Attention Module. (a) Virtual Block for Long-term Dependencies. (b) Geometric Interaction Extractor for learning pairwise features. (c) Gated Edge Attention for updating node features.
Frame-based SE-(3) Module Design. Given the geometric transformation y = W æz we decom- pose W R,ER, using SVD and explain y = R,ERR [ as:
1. Projecting the local coordinate local using the frame F, 0), i.e., aplocal R25 xc
2. Updating local coordinates via gated attention, i.e., socal Eocooca
3. Translating !ocal as the global coordinate using frame T2,,,, 0), i.c., Rocll
If we parameterize Ey as fo(y), and considers the effects of translation, i.e., T2(Rs, L.), (PeRe, tz). the general principle of designing SE-(3) networks could be:
2 R7¹ (x t.)) = T x Equivalent to invariant
2 fo(2) Invariant update,
use GNN or Transformer
(5)
i R.y + t, = Ti Invariant to equivalent
The well-known AlphaFold actually follows such design, where they parameterize fo the IPA module. In this work, we replace fo with enhanced graph neural network:
f((+1) ((++)) (((+)))((())))) + fo( f Te,,
(6)
where ( and f!( represent the input node and edge features of the 1-th layer. Fig. we show the design of the Block Graph Attention Module, consisting of three components: (1) geometric inter-
action extractor, (2) virtual block for long-term dependencies, and (3) the edge attention mechanism. We show the detailed design of the Block Graph Attention Module in the following sections.

---

Long-term Dependency via Virtual Blocks. The GNN is criticized by local receptive field, yielding the problem of over-smoothing and over-squashing 2 these problems using direct paths between distant nodes, while suffering from the O(n²) computing cost. We introduce n' virtual blocks 3 information agents for a graph. Each virtual block directly connects to all the real blocks, resulting in (2- -2- n²' additional directed edges. As n' < n, we claim that the computing cost is close to original GNN. All the virtual blocks, i.e. Ts+1 (R', , t'), 2 R', t'), ,Tn+x (R', t'), share the same rotation R' and translation t':
29 13. Transformers overcome
= [1, T2,,"",""" ....
All the coordinates of the molecule
x = UAV
SVD
R' UV
Pex, Cy 6z]
= - Center of Mass
The invariant features of virtual blocks are different, as we hope they learn diverse interactions. We encode the index to initialize block features f Embedding(() for E (1,2, ,n').
Geometric Interaction Extractor: We enhance edge features with geometric interactions using the local coordinates virtual inter-atoms and dot products of virtual intra-atoms. Previous works, such as PiFold [TO, introduced virtual atoms the featurizer to capture informative side-chain ge- ometry beyond protein backbones, resulting per- formance gains. VFN [27] extended this idea by allowing GNN layers to update the virtual atoms. However, these efforts are limited to learning vir- tual intra-atoms conditioned on node features. In- stead, we propose virtual inter-atoms conditioned on edge features, allowing the same node to exhibit different virtual states specified by edges. Ad- ditionally, inspired by small molecule modeling, capture angle information. We show the geometric interactions Fig. and formulate as:
Locca Position Interaction
Interaction Figure 6: Geometric Interactions.
the dot product of virtual intra-atomss to
(l) MMP(F(")) R Edge feature ( (T,, 1 1
ht hr MMMPP
Local coordinates of virtual inter-atoms
a, = R. Rehe Geometric dot product of virtual intra-atoms (1)
(8)
9at MLP(2!!) Goti Tst, ast)
f())) + -MLP(F .stt)
where Ts: T o = (R.' R4 R (t,t)) 9st vec Rst) E IR9 is the flatten rotation matrix of Tst and rst = ||t, tell indicates the pairwise distance. All 9st, Fat an ast are invariant features. We highlight the difference to previous researches in color.
Gated Edge Attention. We modify PiFold's GNN to capture the geometric interactions when updating node features. For molecular design tasks, we find that aggregating edge features only leads to consistent performance gains. We understand this phenomenon as the model can pay more attention on learning 3D interactions under such a model design. addition, we use gated mechanism to control how the edge features are injected to node features. The gated edge attention module is:
Wat AttMLP (1 ((
ast ΣMEN, exp Wo.
exp
1. EdgeMLP(f!! (1))
Only conditiond on edge
A =EENN h
(1)
fo (++)) =f!") (MLP
U1)
A
Update node feature via forget gate
where @ is element-wii wise product operation, and o(-) the sigmoid function. We highlight the difference between PiGNN and the proposed module in color.

---

FFN & Edge Updating. Analogous to the transformer model, the FFN is a MLP. The edge updating layer remains the same as PiFold:
fst EdgeML
(10)
The proposed module could be equivalently implemented transformer module using matrix multplication. However, we find that padding proteins to the maximum length would grealy increase the computing cost in both GPU occupancy and runtime; We suggest using GNN without padding.
Regularization. We find that the proposed model fit training data better than PiFold and more likely to suffer from overfitting. To address this issue, we randomly drop out the nodes/edges with a probability of to prevent overfitting, We find that controlling the dropout rate could result in models with different fitting abilities. The best performance is achieved when = 0.05.
Experiments
We show the effectivvness of UnilF via multiple inverse folding tasks and ablation studies. We briefly introduce molecular design tasks as follows:
Protein Design (T1): Designing protein sequences folding into the target structure.
RNA Design (T2): Designing RNA sequences folding into the target structure.
Material Design (T3): Discoverying stable composition from a known material structure.
4.1 Protein Design (TI)
Task Description Protein design aims to design protein sequences that fold into target structures. Given a protein backbone structure X' {X, _ 1 i < n). where is the maximum number points belonging to the i-th residue, n the number of residues and the natural proteins are composed by 20 types of amino acids, the goal is to learn function Fe:
Fo X + S.
(11)
The parameters 0 are learned by minimizing the cross-entropy loss, i.e. ((Fa = Dim1 log s 0). The task is challenging due to the combinatorial search space of amino acids and the complex relationship between sequence and structure.
Settings We evaluate of UnilF the CATH4.3 dataset (30 following prior works [1 8 The dataset is split by the CATH topology classification code, yielding 16,631 training, 1,516 validation, and 1,864 testing samples. To assess generalization, we adopt a time-split strategy, considering the use of pretrained ESM2 models by some baselines, which risk data leakage. The time-split evaluation assigns data before specific date the training set and data after that date the set. For structural time-split evaluation, we use the CASP15 dataset [I]. containing novel crystal structures not seen during training. For sequence time-split evaluation, we use the NovelPro dataset (8) which includes 76 protein sequences released within 30 days before November 23, 2023, with structures predicted by AlphaFold2. UnilF consists of 10 layers of BlockGAT with a hidden dimension of 128. It is trained using the Adam optimizer with learning rate of 1e-3 and batch size of 8 for 50 epochs.
Metrics & Baselines We report the median recovery rate of the top-1 predicted sequences, repre- senting the percentage of correctly predicted residues. The ESM2- free baselines include StructGNNN [19, GraphTrans 19. GCA [33], GVP 2I]] AlphaDesign [9) ProteinMPNN 4. and PiFold [IO].
The ESM2-based baselines include LMDesign [17] and K KWDesign [8]. While we prefer open-source baselines, we also re-implement VFN 27 for a comprehensive comparison.
We provide results under different settings (with and without ESM2) and across diverse datasets (CATH4.3, CASP, NovelPro). Using pure inverse folding model without ESM2, UnilF achieves the best performance on all datasets, demonstrating its effectiveness. Notably, UnilF outperforms the strong baseline PiFold with fewer learnable parameters. time-split evaluations, UnilF surpasses all baselines, including ESM2-based methods, by significant margin. On NovelPro, which features novel sequences, UnilF outperforms L.Design and K KWDesign that use ESM2 for sequence refinement. This indicates UnilF's superior generalizability, crucial for real-world
Conclusion
7

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Model
Rec % (CATH4.3)
Roc % CASP
Rec %
Params
ESM
length
< 100
100 S L. 300
300 g < 500
Full
NovelPro
LMDesign 17
0.47
0.56
0.61
0.56
0.48
0.59
DWDesign
0.51
0.61
0.69
0.60
0.56
0.64
StrucIGNN 19
0.30
0.33
0.40
0.34
0.36
0.40
1.4M
Graph
0.29
0,34
0.39
0.34
0.35
0.40
1.5M
GCA 33
0.32
0.36
0.41
0.36
0.40
0.43
2.1M
X
GVP 21
0.33
0.38
0.45
0.38
0.39
0.42
0.9M
AlphaDesign
9
0.37
0,43
0.47
0,42
0.42
0.46
3.6M
ProtcinMPNN
0.38
0.44
0.52
0.44
0.44
0.52
1.7M
PiFold [10
0.43
0.52
0.59
0.51
0.47
0.57
5,8M
Unill (ours)
0.45
0.54
0.53
0.51
0.66
5.4M
Ablation
VEN
0.45
0.53
0.600
0.52
0.48
0.63
5,4M
-GDP
0.45
0.53
0.61
0.52
0.50
0.65
5.3M
-EAtt
0.44
0.53
0.60
0.52
0.48
0.63
5.7M
VVrame
0.45
0.53
0.61
0.52
0.49
0.64
5.4M
Table I: Protein Design results. The best and suboptimal results are labeled with bold and underlined. "VFN"
means that we replace the geometric interaction operation with VFN's operation (27]. "-GDP" means that we remove the geometric dot product features. "-FAut" means that replace the gated edge attention with
PiGNN's attention module TT. "-VFrame" means that we
the global virtual frames.
applications. Ablation studies show that the proposed geometric featurizer, gated edge attention, and global virtual frame enhance performance. On CATH4.3, the overall improvement slight due to strong baselines, but time-split evaluation highlights UnilF's superiority in generalization.
4.2 RNA Design (T2)
Task Description Similar to protein design, RNA design aims to design RNA sequences that fold into target structures. Specially, previous work [34] use the RNA secondary structure as additional input to guide the design process, since the tertiary structure is limited. In this work, we only use tertiary structures as input for the reason of unification, which is more challenging than the baselines.
Datasets Baselines We conduct experiments RNA on the dataset collected by RDesign [34,, consisting of 2218 RNA tertiary structures, which are divided into training (1774 structures), testing (223 structures), and validation (221 structures) sets based on their structural similarity. Following RDesign's benchmark, baseline methods include SeqRNN, SeqLSTM, StructMLP, StructGNN, and StructGNN, GraphTrans [1). PiFold [10] and RDesign (34). Given the small number of data samples, report the median recovery and its standard deviation for theree independent runs.
Table 2: The recovery of RNA design. The best and suboptimal results are labeled with bold and underlined.
Recovery (%)
Method
Short
Medium
Long
All
SeqRNN (h=128)
26.52-H1.07
24.86:1:0.82
27.31++0.41
26.23-10.87
SeqRNN (h=256)
27.61 +1.85
27.16-10..3
28.71+L0.14
28.24-10.46
SeqL.TTM (h=128)
23.48++1.07
26.32-+0005
26.78+11.12
24.70=10.66
SeqLSTM (h=256)
25.00110.00
26.89110.35
28.55400.13
26.93-10.93
StructMLP
25.72:10.51
25.03:11.39
25.38-11.89
25.35:10.25
StructGNN
27.55:10.94
28.78:10.87
28.23411995
28.23-10.71
GraphTrans 19
26.15+00995
23.78+1.11
23.80+t1.69
24.73++0.93
PiFold 10
24.81+2.01
15.90+1.556
23.55++4.13
1..4
RDesign 34
37.22+1.14
44.89+1.67
43.06+0008
41.53+00.88
UnilF (drop 0.05)
48.21 1.095
49.66 11.28
37.29 10.17
10.37
UnilF (drop 0.0)
42,86 +0.87
48.45 11.04
39.23 #0.09
44,29 +0.29
UnilF (drop 0.1)
45.21
+0.98
51.70 1.26
40.30 10.14
46.00 +0.38
UnilF (drop 0.2)
46.97 1.04
48.11 11.3
42.00 #0.18
47.19 #0.45
As shown in Table UnilF achieves the best performance in all cases. The improve- ment is significant, as previous strong baselines like PiFold only excelled in protein design. To our knowledge, UnilF is the first model to achieve state-of-the-an performance in both protein and RNA design tasks, demonstrating
Conclusion
versatility and effectiveness. Compared to RDesign, which uses additional secondary structure features, UnilF relies solely tertiary structure input and still performs better. UnilF successfully unifies the protein and RNA design processes, paving the way for unified inverse folding model for protein-RNe RNA complexes in future developments.

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4.3 Material Design (T3)
Task Description Discovering stable atom compositions from known material structures is crucial for new material discovery [28, [6] [4]. This task is challenging due to the large composition space and the lack of large-scale data. Thanks to recent benchmark efforts 6 we can evaluate the performance of UnilF on this novel task.
Datasets & Baselines We evaluated UnilF on the CHILI-3K dataset [6], which consists of nanomaterial graphs derived from mono-metal oxides. The dataset includes 53 metallic elements and one non-metallic element (oxygen), comprising 3,180 graphs, 6,959,085 nodes, and 49,624.440 edges. Following the official benchmark, the dataset is randomly split into training (80%), vali- dation (10%), and testing (10%) sets. Baselines include GCN [23
Method
Rec % f
Random
1.6+-0.00
GCN 2
49.6+-0.1
PMIP
46.1
GraphSACE 15
49.1
GAT (35]
46.110.0
PMLP 39, GraphSAGE [5 GAT [35) GraphUNet 77), GIN [38.
GraphUNet
$5.2.17.9
and EdgeCNN (37]. Experiments are repeated three times with dif-
GIN 38
58.7+002
EdgeCNN 37
63.2
ferent seeds, using early stopping with a patience of 50 epochs, and
UnilF (ours)
7.,3+1.2
trained up to 1000 epochs.
frame
54.9112.8
quat
65.2+t..
Conclusion In Table 3] UnilF outperforms all baselines by a large margin. Ablation studies demonstrate the crucial role the learned local frame in enhancing interaction feature extraction. In addition, how to learn the local frame is also important. In the "- quat" ablation, we try to learn the x, y. and axes with Householder orthogonalization directly, but found it less effective, with the recovery rate dropping from 75.3% to 65.2%. This highlights the value of the proposed local frame learning mechanism.
Table 3: CHILI-3K Results.
4.4 Case Study
In Fig. 7 we show the designed protein and RNA sequences. In addition, we use AlphaFold3 I to
re-fold the designed sequences into structures. The ground truth (gray), PiFold (green), and UnilF
(pink) structures alinged and compared. We observe that UnilF improves both the recovery and
RMSD of the designed protein and RÑA, demonstrating its effectiveness in inverse folding tasks.
7COK_B B
4XAN D
2ET8 A
UGUAAAUA
GCCGGGCAGGUUCGGIICCCCCGGGAA
CGGAAACCGGGAAOOCGGCCCAAAAGGGGAAA
RDesign
UGAAULIU
ACCEGGAGAGGGUDAAACCCCCCCCCC
GGGCCCGGEEGGGGGGGGGGGGGCCCGGGAAG
UnilF
UGUAUUUA
OCGUAGGAGUUCGCUUCCCCCCCCC
Roc 375--0.3 RMSD: 607 5.76
Secs15 1775 BMM -
Mecsus7 - 8.7 HMBD:: 3.70
1B0Y
PDB
SAPAMAVADIWAAAIAAXYYOOTTTT HTW.://AAPPPPPPPECCCCCCHHOOOOOOO
PiFold
SEPPNLVVSDDPRAAAALIVII WWW.HANNNNNNNSSASNNNNLAAL WNGLLPELQKHANCANN CAAACCAHAVEDAGGATI WWW.VEEEEAAEEEGGHHEC BOCCKLFFGKLVOO WAKKLYNRNGNCANFTVON
UnilF
WWISSRTTAWNLLLPPPLLCCCAAAA POLLLPEELCCCCCCTTTVVTTTI WWW.TTAAAATTNGGGGGGGC DOCELFPGKLVNNNN POGKLYWVNGNCA/WWWWW
2CU7
n n.7 RMSTT 17
PDB
GSSGSSGYSWWTIEEKKLEEEE TEWTTTIEEEEELLGLLLFGGRRRRROO LEA.LLLLGNGAAAANTTICEEEOOO
WWEKKKKKGLKEETPIIIG TPNOKGG
GSSGSSGSEVRL TAEELELL ALLEKGLAKFGSDD SISSDLLIIIKLLLI MIKKIGGS LKK.VVEEESSYEEO EXEKAKSSKSSETPSSSTG
UnilF
(SSGSSGSSWWTTEEXELFRROGG TETTGRNTTTIIKKIIIGN.VVVWVSSSO SWA.RI/GLSKKKKKKLLLLOOOOOO GIKKKEEENNG
3BE3
PDB
MQDERPGYYRHYKGGHHYLAA WWW.:HLLLLLLLAAAAEEEEWWWWYYEEEET
PiFold
GLDERPGIYGNYEGKYYYWWYE LE
UnilF
MEDFRPGGYYMYTGHYYYVLLLAEEE WWW.EEEWWSLLLEEEEEEEEPEORR AAAFEEGGSSSLLSSVRRRAARRRVVEEEEET
Figure 7: Designed examples. The ground truth (gray), PiFold (green), and UnilF (pink) structures are alinged.
5
Conclusion
We propose the first unified model, dubbled UnilF, for general molecule inverse folding. The key points include unifying the data representation, the featurizer and the model architecture without drop in performance. Extensive experiments show that UnilF surpasses baseline methods on tasks. Ablation studies reveal that the geometric interaction extractor, gated edge attention, and virtual long-term dependency modules contribute to performance gains. We believe that the proposed model can benefit multiple domains, such machine learning, drug design, and material design.

---

Acknowledgements
This work was supported by National Science and Technology Major Project (No. 20222D0115101) National Natural Science Foundation of China Project (No. U21A20427), Project (No. WU2022A009) from the Center of Synthetic Biology and Integrated Bioengineering of Westlake University and Integrated Bioengineering of Westlake University and Project (No. WU2023C019) from the Westlake University Industries of the Future Research Funding.
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Reviews:
[{"review_id": "850CQgcb3M", "reviewer": "Reviewer_r3pY", "summary": "This paper addresses the challenge of inverse folding, i.e. the design of novel molecules or macromolecules with specific desired 3D structure, with the goal of improving real-world drug and material design. The authors point out that this challenge has been addressed independently for different contexts, such as inverse folding for small molecules vs inverse folding for large macromolecules. They argue that this has resulted in redundant efforts. To address this issue, they propose a unified model for the inverse folding of all molecules. They demonstrate the efficacy of their novel unified approach across three benchmark molecular design tasks involving protein, RNA and material design.", "questions": "In the absence of wetlab experiments, please could the authors address this limitation by providing data that demonstrates that improved performance at the specific benchmark tasks in inverse folding cited in this paper results in improved performance at novel molecule design. This data can be extracted from previously published work, with appropriate citation. \n\nThere have been a series of recent works presenting improvements at inverse folding. These works, including this paper, make lofty claims about the impact that such improvements will have on drug and material design. Please could the authors point to any realized impact on drug and material design resulting from improvements at inverse folding as measured by performance on these benchmark tasks. \n\nIf such realized impact is difficult to find, please could the authors identify and address this limitation. \n\nPlease could the authors propose strategies to mitigate the risk that further improvements in performance on benchmark tasks, such as the improvements reported in this paper, will not result in realized impact in drug or material design. \n\nPlease could the authors propose strategies to mitigate the risk that the metric used in this work penalizes the model for the design of novel molecules, thereby undermining the stated goals. \n\nPlease could the authors identify a set of real world applications for inverse folding, describing the problem, how inverse folding will contribute to solving this problem, and why existing inverse folding methods are not good enough to solve each specific real world problem identified (NB inferior performance on benchmark tasks does not demonstrate that existing methods are not good enough).\n\nI note that the number of sequences for CATH 4.3 differs from the referenced papers - please could the authors explain this discrepancy. \n\nFor the time split protein tasks, it would be helpful to establish how similar each test sequence is to those used in training, both in terms of proximity in sequence space and in terms of 3D structure. Please then stratify performance as a function of each distance for each held out test set. It would be helpful to also compute these distances for the CATH topology-based split, to provide a watermark for comparison.", "limitations": "In addition to the questions above, please could the authors describe limitations of their work that may prevent improvements at inverse design as measured by performance on benchmark tasks using a recovery metric from having impact on real-world challenges such as drug and material design.", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The authors make an interesting argument that inverse folding problems in different scientific domains should be addressed by a single approach. They present an elegant approach that unifies approaches across domains. Their results demonstrates improved performance as measured by sequence recovery at benchmark tasks involving protein, RNA and material design. The authors carry out ablation studies that provide some insight into the source of this improved performance.", "weaknesses": "While the unified approach is elegant, it is not clear (and the authors do not assert) that the unification is responsible for any performance improvements. However, it is easy for the reader to be left with the impression that the improved results stem from unification. The authors should make it clear that either (i) unification is satisfying but is not connected to performance improvements, or (ii) unification leads to performance improvements and explain clearly the data supporting this. \n\nThe recovery metric is confusing, since it is not clear that higher recovery actually corresponds to the ability to design novel molecules. Indeed, the recovery metric penalizes the model for novelty, which seems to contradict the stated goal ('enabling scientists to synthesize novel molecules with the desired structure'). This undermines the claim that the approach presented in the paper 'can benefit multiple domains, such as machine learning, drug design, and material design'. Specifically, rediscovering existing macromolecules does not benefit these domains. \n\nMoreover, even if the metric were aligned with the stated goals, it is not clear to this reviewer that existing inverse folding methods are not good enough to solve any real world application of inverse folding such as the stated goals described in the introduction to this paper of improvements in drug and material design. This leads me to wonder what problem this paper is trying to solve.\n\nWhile the paper focuses on maintaining the structure of the target molecules (or material), no attempt is made to ensure that the function is maintained. This does not bode well for applications such as drug design, that require that the designed molecule functions correctly. \n\nThe paper does not include any wetlab experiments, instead relying on performance at benchmark tasks to demonstrate improved performance. The authors argue that this is the only limitation of this paper, and that wetlab experiments are by definition out of scope for an AI paper. This raises the question of whether improvements on the benchmark tasks and associated metrics used in this paper corresponds to progress on the stated goals of the paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> **W1:** It is not clear  that the unification is responsible for any performance improvements.\n>> **Reply** The proposed unification method is responsible for performance improvements by using the block-level representation and modules, such as geometric featurizer, interaction operator, and virtual frame. \n\n> The recovery metric is confusing, since it is not clear that higher recovery actually corresponds to the ability to design novel molecules. \n>> **Reply** One might claim that higher recovery reduces diversity without considering similar designability. However, when methods with comparable designability—measured by self-consistent TM (scTM) scores—are considered, the conclusion changes: higher recovery actually increases diversity at the same scTM score level. This is a important finding of ProteinInvBench [1].\n>> [1] Gao, Zhangyang, et al. \"Proteininvbench: Benchmarking protein inverse folding on diverse tasks, models, and metrics.\" NeurIPS (2024).\n\n\n\n> **W2:** Moreover, even if the metric were aligned with the stated goals, it is not clear to this reviewer that existing inverse folding methods are not good enough to solve any real world application. \n> **W3:** The paper lacks wetlab experiments and relies on benchmark performance to demonstrate improvement. Do these benchmark improvements and metrics align with the stated goals?\n> **Q1:** In  the absence  of wetlab experiments, the authors should provide data from published work showing that better benchmark performance in inverse folding leads to improved novel molecule design, with proper citations.\n> **Q2** Can the authors provide examples of improvements in drug and material design resulting from better performance on inverse folding benchmark tasks? If such examples are hard to find, please address this limitation.\n> **Q3:** Please could the authors propose strategies to mitigate the risk that further improvements in performance on benchmark tasks, such as the improvements reported in this paper, will not result in realized impact in drug or material design.\n>> **R3:** The stated goals can be decomposed into two step: computation and wetlab experiments. \n>> Most AI researchers [3,5,6,7] focus on improving  the sequence recovery, which has inspired further biological research. For instance, the well-known ProteinMPNN[4] is largely based on GraphTrans[5]. AI researchers often lack the equipment and funding for wetlab experiments. If you can provide or recommend research opportunities, we would greatly appreciate it.\n\n>> Improving recovery is crucial for wetlab experiments. ProteinMPNN[5] increased recovery from 39% to over 45%, achieving strong wetlab results. LigandMPNN[2] further improved recovery to over 60%, demonstrating the ability to generate small molecules and DNA-binding proteins with high affinity and specificity.\n\n>>  As suggested, AI researchers should collaborate with bio-labs to validate their algorithms. Additionally, evaluating proposed methods on more metrics, such as scTM and diversity, would help mitigate the risks mentioned.\n\n>> [2] Dauparas, Justas, et al. \"Atomic context-conditioned protein sequence design using LigandMPNN.\" Biorxiv (2023).\n\n>> [3] Wu, Fang, and Stan Z. Li. \"A hierarchical training paradigm for antibody structure-sequence co-design.\" NeurIPS (2024).\n\n>> [4] Dauparas, Justas, et al. \"Robust deep learning–based protein sequence design using ProteinMPNN.\" Science (2022).\n\n>> [5] Ingraham, John, et al. \"Generative models for graph-based protein design.\" NeurIPS (2019).\n\n>> [6] Zheng, Zaixiang, et al. \"Structure-informed language models are protein designers.\"ICML, 2023.\n\n>> [7] Hsu, Chloe, et al. \"Learning inverse folding from millions of predicted structures.\" ICML, 2022.\n\n\n\n\n> **W4:** While the paper focuses on maintaining the structure of the target molecules (or material), no attempt is made to ensure that the function is maintained. \n>> **R4:** We appreciate the suggestion; a relevant paper has been published in Nature[6]. We also aim to extend the algorithm to protein functions, such as solubility, but have not found a well-curated dataset for these functions. If you can recommend a suitable dataset, we would be very grateful.\n\n>> [6] Goverde, Casper A., et al. \"Computational design of soluble and functional membrane protein analogues.\" Nature (2024).\n\n\n> **Q4** Could the authors propose strategies to mitigate the risk that the metric used in this work penalizes the model for the design of novel molecules?\n>> **R4** Reducing the temperature can balance recovery and diversity: lower temperatures (temp) increase diversity while reducing recovery. At the same recovery level on CASP15 dataset, UniIF shows higher diversity compared to the baseline, aligning with findings from ProteinInvBench.\n\n|      | ProteinMPNN |      | PiFold |      | UniIF |      |\n|:----:|:------:|:----:|:------:|:----:|:-----:|:----:|\n| temp |   rec  | diversity |   rec  |  diversity |  rec  |  diversity |\n|  0.0 |  0.449 |  0.0 |  0.473 |  0.0 | 0.514 |  0.0 |\n| 0.05 |  0.436 | 0.22 |  0.463 | 0.24 | 0.505 | 0.14 |\n|  0.1 |  0.404 |  0.1 |  0.425 | 0.41 | 0.475 | 0.30 |\n\n\n\nKindly refer to official comments for more response.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "cjwzs9M2X2", "reviewer": "Reviewer_Aoua", "summary": "Previous approaches on molecule inverse folding (IF), which is crucial for drug and material discovery,  focus separately on either macro-molecules or small molecules, leading to the lack of a unified approach for different molecule types. To this end, the paper proposes UniIF to unify the IF from a data and model perspective. Specifically, by introducing a unified block for processing different molecules and a geometric block to capture their 3D interactions, UniIF shows improvements in various tasks across different molecules.", "questions": "1. Could you provide some discussions or experiments on the effectiveness of the proposed featureizer?\n2. Is it possible to train UniIF with mixed molecule datasets?\n3. For Equation 7, the SVD decomposition could be time-consuming when scaling the input size. Could the author explain the motivation for such a design and why a simple approach, like \"mean,\" does not work?", "limitations": "The proposed UniIF offers a unified framework for learning different molecules, such as protein and RNA. However, it still models different molecules individually, hindering the proposed framework's potential impact.", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The proposed UniIF is novel and effective for unifying IF across different molecules, including protein, RNA, and small molecules.\n- The UniIF can excel in protein, RNA, and material design, which is impressive. Extensive experiments have been conducted to provide justification for the effectiveness of the proposed method. \n- The paper is generally well-written, with clear illustrations and tables.", "weaknesses": "- The ablation experiments in Table 1 (-GDP), which remove the geometric dot product features, show miner drawbacks in proteins with longer sequences. This makes the geometric interaction extractor mainly capture the interaction of the virtual inter-atoms. The idea for capturing long-range dependency or interaction using virtual blocks has been studied in [1,2,3,4]; the authors could consider providing a discussion on how their proposed techniques differ from these referenced works.\n- In addition, the UniIF adopted both node and edge feature extractors as feature augmentation, which is model-agnostic. However, the comparison of all three tasks does not show the potential benefit of introducing such a featureizer. \n- The paper could provide further insight into the benefit of employing a unified framework for protein, RNA, and material design. For example, could the UniIF model different molecules in a unified latent space, allowing the researchers to investigate their underlying interactions?\n\n[1] Neural Message Passing for Quantum Chemistry. In ICML, 2017\n\n[2] An analysis of virtual nodes in graph neural networks for link prediction. In LoG, 2022\n\n[3] On the Connection Between MPNN and Graph Transformer. In ICML, 2023.\n\n[4] Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel. In ICLR, 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> **W1** The ablation experiments in Table 1 (-GDP), which remove the geometric dot product features, show miner drawbacks in proteins with longer sequences. This makes the geometric interaction extractor mainly capture the interaction of the virtual inter-atoms. The idea for capturing long-range dependency or interaction using virtual blocks has been studied in [1,2,3,4]; the authors could consider providing a discussion on how their proposed techniques differ from these referenced works.\n>> **R1** Thanks for your recommandation. Recent works show that virtual node could help the model to learn long-term dependencies. Our work is different in the virtual frame construction and interaction: we need to construct the local frame for each virtual node and also we use geometric operators to consider the communication between virtual nodes. Other GNN-related works do not consider the 3D geometric information.\n\n> **W2** In addition, the UniIF adopted both node and edge feature extractors as feature augmentation, which is model-agnostic. However, the comparison of all three tasks does not show the potential benefit of introducing such a featureizer.\n> **Q1** Could you provide some discussions or experiments on the effectiveness of the proposed featureizer?\n>> **Reply** Previously, PiFold use a model-related featurizer for protein design and achieves good results. However, such a featurizer is complex and computationally expensive. Our contribution is to simplify the featurizer and mask it model-agnostic, while ensuring competitive performance. If one use PiFold's featurizer, the recovery of protein design can further improve about 0.3%. However, such a improvement is model-related and could not eazily extend to RNA data. With out simplified featurizer, RNA recovery improved much.\n\n> **W3** The paper could provide further insight into the benefit of employing a unified framework for protein, RNA, and material design. For example, could the UniIF model different molecules in a unified latent space, allowing the researchers to investigate their underlying interactions?\n> **Q2** Is it possible to train UniIF with mixed molecule datasets?\n>> **Reply** Yes. We have tried to train UniIF in mixed protein and RNA dataset, which achieve slightly lower performance than training on single dataset. We plan to expend the experiments to protein-RNA-molecule complexes to study the underlying interactions as you suggested.\n\n\n> **Q3** For Equation 7, the SVD decomposition could be time-consuming when scaling the input size. Could the author explain the motivation for such a design and why a simple approach, like \"mean,\" does not work?\n>> **Reply** Simple approach, like \"mean\", could not obtain a orthogonal frame vectors. We use SVD to get the rotation matrix to serve as orthogonal vector basis.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WdTrwVGYr0", "reviewer": "Reviewer_kcUc", "summary": "This paper propose a unified framework for inverse folding. Their model is applicable to small molecules, proteins and RNA.\nMore specifically, they propose a unified frame representation for amino acid, atom and nucleotide. They further propose a GNN-based model to learn the structure information. \nResults show that their model can outperform other baselines in protein design, RNA design and material design.\n\n\n---\n\nI changed my score to 5 after the discussion with other reviewers and AC.", "questions": "(1) As illustrated in the previous part, do you train the model on three types of molecules?\n\n(2) What decoder model is used?", "limitations": "The limitations are not discussed.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "(1) The paper is clarified clearly. \n(2) The idea of unifying inverse folding for different types of molecules is interesting and reasonable. \n(3) Experimental results show their model work good in different design tasks.", "weaknesses": "Major points:\n\n1. Some important experimental details are missing. For instance, while the paper proposes a unified inverse folding framework, it's unclear whether the model is trained on a combined dataset from different types of molecules. Specifically, the training sets for RNA design and material design are quite limited. If the model is trained with protein data, it would be beneficial to demonstrate whether incorporating data from other types improves performance.\n\n2. Additionally, if I understand correctly, the paper does not explain the decoder part of the inverse folding model.\n\nMinor points:\n\n1. It would be helpful if the paper included a comparison of perplexity, as related works such as PiFold have used this metric.\n   \n2. The performance on protein design looks comparable with other models to me.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> **W1** Some important experimental details are missing. For instance, while the paper proposes a unified inverse folding framework, it's unclear whether the model is trained on a combined dataset from different types of molecules. Specifically, the training sets for RNA design and material design are quite limited. If the model is trained with protein data, it would be beneficial to demonstrate whether incorporating data from other types improves performance.\n> **Q1** As illustrated in the previous part, do you train the model on three types of molecules?\n>> **Reply** We train the model on different types of molecules. We have tried to train UniIF in mixed protein and RNA dataset, which achieve slightly lower performance than training on single dataset. We have used all the RNA data recoreded in PDB to train the model, which is actually the largest real data currently available. Maybe we can use data augmentation techiniques in the future.\n\n> **W2** Additionally, if I understand correctly, the paper does not explain the decoder part of the inverse folding model.\n> **Q2** What decoder model is used?\n>> **Reply** A linear layer is served as the decoder to predict 20 amini acids, 4 neuclitides, or 128 atom types.\n\n> **W3** It would be helpful if the paper included a comparison of perplexity, as related works such as PiFold have used this metric.\n>> **Reply** We provide the perplesxity as follows:\n\n| model  | short | medium | long | all  |\n|--------|-------|--------|------|------|\n| PiFold | 5.81  | 4.57   | 3.77 | 4.50 |\n| UniIF  | 5.53  | 4.41   | 3.62 | 4.32 |\n\n\n> **W4** The performance on protein design looks comparable with other models to me.\n>> **Reply** As far as we know, the protein design performance has reached a bottleneck stage where any improvement is difficult.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "SPEcDoOIDi", "reviewer": "Reviewer_m3n9", "summary": "This paper proposes representing molecular systems as blocks of atoms and designs a neural network architecture that processes such representations. The method is applied for benchmark inverse folding tasks for protein/RNA. A materials-related task is also explored.", "questions": "- In the experiments, what exactly does with/without ESM mean? How is ESM used? Why can't the proposed method also become \"with ESM\"? Why is it valuable to consider \"without ESM\"? Are models \"with ESM\" more expensive or what?\n- Can the author clarify the materials task and why it is inverse folding? Can the authors compare to materials-focused SOTA methods in the space?", "limitations": "The authors did not write about the limitations of the proposed method.", "rating": 5, "confidence": 1, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- The proposed method seems to perform well on various protein/RNA inverse folding benchmarks that focus on the recovery rate metric. (this reviewer is not experienced in protein/RNA design tasks, so it is hard for this reviewer to put the results in context.)\n\n- The idea of representing clusters of atoms as blocks is an interesting approach and is potentially useful for representing molecular systems across different scales. while such ideas have already been explored in several previous works as the authors noted, there are new elements such as vector basis for blocks.", "weaknesses": "- The protein inverse folding ablation seems to show the various introduced techniques do not influence model performance much. The insights of the models seem obscured from this perspective, especially considering block-based representations and architectures have already been explored in previous works (GET/EPT). The RNA design task does not include ablation, and this reviewer is not convinced by the materials-related task (see below). It is rather unclear what a reader can take away from this work in terms what techniques actually contribute to improved performance.\n\n- If \"unified\" is the key word of the method, perhaps the authors should include experiments where the model is trained on various data modalities and see if that improves performances on individual tasks.\n\n- The materials sections are rather strange in this paper. Inverse folding for proteins/RNAs can be understood as inferring the sequence from a given structure. The materials task is not introduced in great detail. What is being predicted? How is it \"inverse folding?\"\n\n- To follow up on that, the baselines that the proposed model is compared against are not what's considered state-of-the-art in materials representation. For materials, there are popular benchmarks such as Matbench (https://matbench.materialsproject.org/), JARVIS (https://pages.nist.gov/jarvis_leaderboard/), or Open catalyst (https://opencatalystproject.org/index.html), where results of SOTA models are readily available. This reviewer is not convinced by the materials-related results presented as significant details regarding the dataset/goals are missing, and the baselines are not really used for materials.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**W1** The protein inverse folding ablation seems to show the various introduced techniques do not influence model performance much. The insights of the models seem obscured from this perspective, especially considering block-based representations and architectures have already been explored in previous works (GET/EPT). The RNA design task does not include ablation, and this reviewer is not convinced by the materials-related task (see below). It is rather unclear what a reader can take away from this work in terms what techniques actually contribute to improved performance.\n>> **Reply** As protein design is more mature than RNA design, we do ablation on it. However, the protein design performance has reached a bottleneck stage where any improvement is difficult. Things will be more clear on RNA dataset. Here we provide the ablation results on RNA dataset:\n\n| model      | short | medium | long  | all   |\n|------------|-------|--------|-------|-------|\n| w/o VFrame | 44.44 | 49.25  | 37.40 | 45.45 |\n| w/o EAttn  | 47.89 | 49.00  | 36.71 | 48.28 |\n| w/o GDP    | 46.88 | 48.00  | 37.19 | 47.06 |\n| UniIF      | 48.21 | 49.66  | 37.29 | 48.94 |\n\nIn addition, we have evaluated the performance of GET on protein design, which performes very poor.\n\n\n**W2** If \"unified\" is the key word of the method, perhaps the authors should include experiments where the model is trained on various data modalities and see if that improves performances on individual tasks.\n>>**Reply** Such a protocol may improve performance of protein-RNA or protein-molecule interaction prediction. However, for the design task, we observe that training on single dataset lead to better results, as we do not consider the complex interactions.\n\n**W3** The materials sections are rather strange in this paper. Inverse folding for proteins/RNAs can be understood as inferring the sequence from a given structure. The materials task is not introduced in great detail. What is being predicted? How is it \"inverse folding?\"\n**Q2** Can the author clarify the materials task and why it is inverse folding?\n>>**Reply** This is a new task defined in  Chili[1], which gives the material structure and predict the composition of atoms types that fit to this structure.\n\n**W4** To follow up on that, the baselines that the proposed model is compared against are not what's considered state-of-the-art in materials representation. For materials, there are popular benchmarks such as Matbench (https://matbench.materialsproject.org/), JARVIS (https://pages.nist.gov/jarvis_leaderboard/), or Open catalyst (https://opencatalystproject.org/index.html), where results of SOTA models are readily available. This reviewer is not convinced by the materials-related results presented as significant details regarding the dataset/goals are missing, and the baselines are not really used for materials.\n**Q2** Can the authors compare to materials-focused SOTA methods in the space?\n>> **Reply** Thanks for your suggestion. On the binary classification task of \"matbench_mp_is_metal\", we provide the updated results as follows:\n\n>>| model  | mean rocauc | mean f1 |\n>>|--------|-------------|---------|\n>>| UniIF  | 0.9617      | 0.9534  |\n>>| CGCNN  | 0.9520      | 0.9462  |\n>>| ALIGNN | 0.9128      | 0.9015  |\n>>| coGN   | 0.9124      | 0.9012  |\n>>| coNGN  | 0.9089      | 0.8972  |\n>>| SchNet | 0.8907      | 0.8765  |\n\nwhere UniIF outperforms previous baseline methods.\n\n\n**Q1** In the experiments, what exactly does with/without ESM mean? How is ESM used? Why can't the proposed method also become \"with ESM\"? Why is it valuable to consider \"without ESM\"? Are models \"with ESM\" more expensive or what?\n>>**Reply** If ESM is allowed, the model can fintune the pretrained ESM model to refine the designed sequence; Otherwise, the model do not allowed to access the knowledge of ESM. The proposed method can also incooparate with ESM. Without ESM, the algorithm is more efficient, and there is do risk of label leackage to avoid potential concerns.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper considers the problem of inverse folding, which is the design of a chemical (such as a small molecule or a protein) to conform to a given 3D structure. Traditionally, the modelling approach used for this task would depend on whether the object to be designed is a small molecule, protein, or material; here the authors propose the UniIF architecture, which shows competitive results on any of the subdomains of inverse folding.

After mixed initial reviewers, some of the reviewers increased their ratings after author rebuttal, with three in favour of accepting the paper, and one leaning towards rejection. Generally, most reviewers agreed that the paper is interesting, well-written, and relatively novel. They enjoyed the model unification idea, although questioned to what extent it is a useful contribution, given that (as shown by the authors during rebuttal) there seems to be no positive transfer between the domains, and best results are achieved by training UniIF for each task separately. The reviewers also discussed the strength of the empirical results, noting that (a) protein design tasks investigated in this work seem close to saturated, and (b) employed materials design tasks are somewhat uncommon and the results are harder to contextualize due to the choice of baselines. Reviewers continued discussing during the private discussion period, debating whether improvements on top of prior work are clear enough, and whether unification without positive transfer is useful in itself. They noted wetlab results could be a potential way to increase the impact of this work, although they agreed this would not be required for acceptance at NeurIPS. During this phase, two of the positive reviewers decided to lower their scores to borderline accept.

After all of the discussion, the paper ended up with exclusively borderline ratings, albeit not due to lack of opinion on the side of the reviewers, and rather them carefully weighing the pros and cons of this work. On the positive side, it is clear that UniIF is an interesting architecture, and while no advantage from co-training on multiple modalities was observed so far, it could encourage downstream work to investigate this idea further, potentially moving towards a “foundation model” for science. Due to this potential, and generally promising performance, I am willing to lean into the positive aspects of this work and support its acceptance despite borderline ratings. I would encourage the authors to carefully consider the feedback from the reviewers and include relevant discussion in the final version of their paper; in particular, I would recommend highlighting the lack of positive transfer between domains as an avenue for future work.
Decision: Accept (poster)