h-MINT: Modeling Pocket-Ligand Binding with Hierarchical Molecular Interaction Network
Authors: Yanru Qu
Yijie Zhang
Wenjuan Tan
Xiangzhe Kong
Xiangxin Zhou
Chaoran Cheng
Mathieu Blanchette
Jiaxuan You
Ge Liu
Venue: ICLR 2026 Poster (2026)
Keywords: Binding Affinity Prediction
BPE
Virtual Screen
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Accurate molecular representations are critical for drug discovery, and a central
challenge lies in capturing the chemical environment of molecular fragments,
as key interactions, such as H-bond and π stacking, which occur only under specific
local conditions. Most existing approaches represent molecules as atom-level
graphs; however, individual atoms cannot express stereochemistry, lone pairs,
conjugation, and other complex features. Fragment-based methods (e.g., principal
subgraph or functional group libraries) fail to preserve essential information such
as chirality, aromatic bond integrity, and ionic states. This work addresses these
limitations from two aspects. (i) OverlapBPE tokenization. We propose a
novel data-driven molecule tokenization method. Unlike existing approaches, our
method allows overlapping fragments, reflecting the inherently fuzzy boundaries
of small-molecule substructures and, together with enriched chemical information
at the token level, thereby preserving a more complete chemical context. (ii) h-
MINT model. We develop a hierarchical molecular interaction network capable
of jointly modeling drug–target interactions at both atom and fragment levels. By
supporting fragment overlaps, the model naturally accommodates the many-to-
many atom–fragment mappings introduced by the OverlapBPE scheme. Extensive
evaluation against state-of-the-art methods shows our method improves binding
affinity prediction by 2-4% Pearson/Spearman correlation on PDBBind and LBA,
enhances virtual screening by 1-3% in key metrics on DUD-E and LIT-PCBA, and
achieves the best overall HTS performance on PubChem assays. Further analysis
demonstrates that our method effectively captures interactive information while
maintaining good generalization.

Published as a conference paper at ICLR 2026
H-MINT: MODELING POCKET-LIGAND BINDING WITH HIERARCHICAL MOLECULAR INTERACTION NETWORK
Yanqu Qu'
Yijie Zhang
Wenjuan Tan 5.6
Xiangzhe Kong
Xiangxin Zhou?
Chaoran Chengl. Mathieu Blanchette Jiaxuan You!
Ge Liu
Depparmenn of Computer Science, UICC
DOO Center for Advanced Bioenergy and Bioproducts Innovation, UIUC
3 School of Computer Science, McGill University
EMILA-Québer Al Institute
5 "Department of Computer Science and Technology, Tsinghua University
Institute for AI Industry Research (AIR), Tsinghua University
Schooo of Artíficial Intelligence, University of Chinese Academy of Sciences (yanruqu2, jiaxuan, geliu j fillinois .edu; yj zaang@maai 1. magi 1ll.... ca
ABSTRACT
Accurate molecular representations are critical for drug discovery, and central challenge lies in capturing the chemical environment of molecular fragments, as key interactions, such H-bond and и stacking, occur only under specific local conditions. Most existing approaches represent molecules as atom-level graphs; hardly express higher-order chemical
however, atom-level representations context (e.g., stereochemistry. lone pairs, conjugation). Fragment- (e.g., principal subgraph, predefined functional groups) fail preserve essential information such as chirality, aromaticity, and ionic states. This work addresses these limitations from two aspects. (i) OverlapBPE tokenization" We propose a novel data-driven molecule tokenization method. Unlike existing approaches, our method allows overlapping fragments, reflecting the inherently fuzzy boundaries of small-molecule substructures and, together with enriched chemical informa- tion at the token level, thereby preserving more complete chemical context. (ii) h-MINT model. OverlapBPE induces many-to-mant atom-fragment mappings, which necessitate a new hierarchical architecture. We therefore develop hier- archical molecular interaction network capable of jointly modeling interactions at both atom and fragment levels. By supporting fragment overlaps, the model naturally accommodates the many-to-many by the OverlapBPE scheme. Extensive evaluation against state-of-the-arr methods shows our method improves binding affinity prediction by 2-4% Pearson/Spearman correlation on PDBBind and LBA, enhances virtual screening by 1-3% in key metrics on DUD-E and LIT-PCBA, and achieves the best overall HTS performance on PubChem assays. Further analysis demonstrates that our method effectively captures interactive information while maintaining good generalization.
methods
fragment mappings introduced
INTRODUCTION
Precise modeling of protein-ligand interactions is pivotal for fundamental tasks, such as binding affinity prediction and virtual screening, in early-stage drug discovery (R Laurie & Jackson| 2006). Beyond pharmaceutical applications, understanding enzyme pocket-ligand interactions is also critical for rational enzyme engineering, where
substrates or cofactors bind to catalytic
"quual contribution.
Corresponding author.
'BPF, (Byte Pair Encoding) (Sennrich al. 2016 is statistical tokemization method that iteratively merges the frequent pairs of symbols corpus to generate compact and efficient subword units.

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Published as a conference paper at ICLR 2026
pockets to regulate activity, stability, and specificity. Such capabiliities are particularly important in bioenergy and bioproduct applications, where engineered enzymes are designed to efficiently convert biomass-derived substrates into sustainable fuels and chemicals. To accurately decipher these interactions, it is essential to build expressive representations to fully capture the chemical environment of the molecules. Most existing methods represent molecules atom-level graphs (Zhou iet al. 2023); however, it is questionable whether they are able to learn essential chemical information (e.g., stereochemistry, lone pairs, conjugation) just from atomic tokens (Wigh et al. 2022). Another line of work employs molecular fragmentation (e.g., Principal Subgraph (Kong et al. (002bb) to preserve local contexts for atoms, in accordance with the intuition that many physicochemical properties occur at the fragment level (Murray & Rees, 2009). Nevertheless, these methods still fail to preserve crucial information like chirality, aromatic bond integrity, and ionic states, which stems from their naive partitioning of molecules into disjoint sets.
This work addresses the above limitations by integrating innovations in both molecular representations and model architectures. (i) We propose novel and frequency-basee molecular tokenization (i.e., fragmentation) algorithm, OverlapBPE, which preserves essential chemical knowledge (e.g. chirality, aromaticity, charges). Specifically, we enrich the atomic representation with their properties (e.g., charges) and further incorporate 3D conformations during an iterative tokenizing process to extract frequently occurring fragments. To maintain the integrity of aromatic systems, we further enable atom overlaps between mined fragments, leading to a many-to-many mapping between atoms and fragments. For example, Naphthalene (elc2ceccc2ccel) can be tokenized into 2 benzene rings (clcccocl) that share 2 aromatic C atoms. While the many-to-many mapping is necessary for aromatic integrity, it also poses an additional challenge on the model architecture, as most existing hierarchical molecular networks only support 1-1 mapping between atoms and fragments. (ii) OverlapBPE induces many-to- mon atom-fragment mappings that existing hierarchical models are not designed to handle. To address this, we develop the hierarchical Molecular Interaction NeTwork (h-MINT), which explicitly supports overlapping atom-fragment structures. h-MINT introduces bilevel attention mechanism that allows bidirectional information flow between atoms and overlapping fragments, and further expands fragment-level relations into atom-level geometric edges. This hierarchical yet equivariant design enables the model to capture multi-scale interaction patterns while maintaining global consistency.
Extensive experiments exhibit the superiority of our method over existing baselines, highlighting 2-4% performance gains on Pearson/Spearman correlation for binding affinity prediction, 1-3% gains on key metrics for virtual screening, and the best overall performance in HTS. Further analysis shows that our tokenization captures important inductive bias, making our model robust to noise and maintaining good generalization under different settings, indicating great potential for real-world application
2 BACKGROUND AND RELATED WORK
F Tragment-Basedr Molecular Tokenization. Fragmentation partitions atom-level molecular graph into coarse units that capture meaningful substructural features. Early work relied on hand-crafted junction-tree rules or predefined fragment libraries (Jin et al. 2018 2020 Yang et al., (02T). To reduce manual bias, subsequent studies adopted unsupervised frequent-subgras fragmentation rules in data-driven manner: the underlying search is Na-hard (Kuramochi & Karypis 2001 Jazayeri & Yangl 2021). but approximate algorithms make it tractable in practice (Inokuchi al. 2000 Yan & Han 2002 Nijssen & Kok| 2004 Geng al. 2023). Byte-pair encoding (BPE), which iteratively merges the most frequent token pairs to build compact vocabulary, has also been adapted 2D molecular data (Li & Fourches 2021| Ucak et al. 2023 Shen & Póczos 2024). Our work is closely related to PS-VAE (Kong et al. 2022b). which proposes a data-driven tokenization that automatically mines and merges the most frequent. maximally-sine (i.e., principal subgraph). However, essential chemical information, such as stereochemistry and conjugation, is usually neglected. In contrast, our proposed OverlapBPE: tackles these challenges by enriching atomic properties, involving 3D stereochemistry, and enabling overlapping between fragments.
mining to construct
molecular fragments
Molecular Interaction Modeling. Recent deep-learning frameworks for biomolecular modeling seek to couple fine-grained molecular representations interacting at multiple spatial resolutions.
"Code & checkpoints: http://giituah.com//Amm00000 4/hmiin
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A.
B.
Figure I: Illustration of the OverlapBPE tokenization process. (i) Starting from the molecule in A, we first extract all basic tokens from the atom graph. (ii) After identifying all basic tokens, we obtain the initial fragments (left) and token graph (right), as shown in B, which contains 4 tokens in 3 types: clccccel (frcq=3778), Cc (freq=3496), and Sc (freq=637). (iii) We then enumerate all adjacent token pairs and identify the highest-frequency composite token from the final vocabulary Pimal. namely Cc cccce1 (freq=2458). (iv) Merging clcceeel and Cc into new token, obtain new fragments and token graph, as shown in C, containing 3 tokens in 2 types: Cc1 ccccel (freq=2458), Sc (freq=637). (v) Continue enumerating adjacent pairs token graph C; no matched token found in vocabulary, the algorithm terminates.
At the finest scale, atom-level graphs are usually processed with E(n -qqiiarrraa or directional message-passeee paaggeg-assigggggooo networks, capturing local physics with high fidelity (Schütt et al. [0117] Satooras
al. 2021 Xu et 2022 Hoogeboom al. 2022 Atz et al., 2021 Zaidi et al. [022] Townshend
et al. 2020). To reason over chemistry that spans several bonds such as aromatic conjugation,
hydrogen-bond lattices and r-stacking, researchers introduce coarser views by pooling atoms into
residues or surfaces for proteins (Jin et al. 2022 Anand & Achim 2022 Somnath et al. 2021
Wang et al.), dual graphs for inverse folding (Gao et al. al.|| (022), and functional-groups mined from small-molecule graphs (Jin et al., 2018 Kong et al. [022bb Geng al., 2023). Colecue modules then embed ligand and receptor in a shared 3-D frame and predict poses or affinities with regression, contrastive, diffusion objectives (Kong et al. 2022a Luo et al. 2022 Stärk al. 2022; Kong et al., 2024] Gao et al. 2023). Although these approaches broaden the receptive field, they often remain confined to a single resolution and are unable enable bidirectional information flow between atoms and their corresponding substructures. Compared with prior hicrarchical GNN approaches, our h-MINT introduces an atom-token overlap mechanism and expands token-level relations into atom-level geometric edges, enabling more flexible cross-scale information flow and yielding fine -na-ggr yet globally consistent interaction modeling.
METHODOLOGY
3.1
OVERLAPBPE TOKENIZATION
We reveal that the failures of existing fragment-based methods stem from the naive partitioning of molecules into disjoint sets. To address these limitations, we propose a new tokenization approach that permits atom overlaps between mined fragments, enabling more expressive, coherent, and chemically meaningful molccular representations.
Atom Graph. An atom graph can be represented property graph GO (V", ES), where Ve = {a,} is a set of atoms, E" C Ve x Va is the set of bonds, and each atom/bond has an associated element/bond type. A tokenization step maps subgraph of Ga with certain atoms and bonds into a Tragment/toker" (V', E') + E 4, where (V", E") = G"[V" 2 x 2 is an induced subgraph, and the set of tokens.
Token Graph. A fragment/token graph G is constructed from an atom graph G through contract- ing subgraphs in G as a single node while preserving connectivity. Each token thus represents an induced subgraph, and two tokens are connected if they share one or more atoms. This construction allows multiple tokens to overlap on shared atoms, forming a non-disjoint cover of the molccular graph. To guarantee not breaking important substructures and support token overlap, a set of basic tokens Busic which includes smallest fragments that are chemically meaningful and should be preserved during partitioning. We include all single atoms, bonds, and rings collected from
first identify
We use the terms token and fragment interchangeably to refer salient patterss mined from the atom graph.
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the training set in Pussi to guarantee the token set is complete, yet not all of the basic tokens will be added to the final vocabulary. In practice, we first replace all rings with tokens, then bonds, and lastly atoms to make sure all atoms and bonds are covered in the token graph G In G each node is basic token, and the tokens are connected through sharing atoms (or disconnected when it is an ion).
G -V=(V',E") T': - Prasic
where / is the set of tokens. E S V x V encodes their adjacency (e.g. two tokens share atoms or connected by bonds), and T maps each fragment to its token type in Prosie. Besides, the following tokenization process follows bottom-up BPE merge fashior which also guarantees that the basic tokens will not be broken.
Frequency-Baae -Based Vocabulary Setup. After we get the token graph G consisting of basic tokens, we iteratively discover new composite tokens and update the vocabulary & comp as follows:
1. For a given token graph G = (V1, E enumerate all adjacent token pairs (fc. E E as composite token candidates: = [Merge(f.; ff) I (fi. fj) E²) where Merge(fs: f;. denotes the operation of combining two neighboring tokens into a larger token. Token frequency is computed over the training corpus.
2. Select the most frequent token f C and add it to the vocabulary: Peomp < Dromp U (f*).
3. Update all token graphs {G') } in the corpus by replacing each occurrence of f with a new hyper node. Note that the original tokens in V will not be removed from G unless all of its adjacent composite candidates have been merged.
4. Repeat steps 1-3 until a stopping criterion (e.g., iteration steps) is met.
5. Filter Pousie and + comp with minimum frequency threshold to make sure a proper vocabulary size Prinal= = {f E Pracic U L comp (frq(f) > t}.
OverlapBPE Tokenization. After obtaining the vocabulary, we can tokenize an atom-bond graph Go following the frequency of tokens in 4 fimll Figure Tillustrates the tokenization process. For more details, please refer Appendix C.1
1. Find all basic tokens Prasic from G and convert G into token graph G For basic tokens in G', we preserve their token identifier if it's in Prial otherwise, we replace it with special identifier (e.g.. <ring>) to make sure not breaking it in the following.
2. For GO, enumerate all adjacent token pairs (fs, ff) E E and find the matching token f* E &tnal with the highest frequency that can be merged from pair(s) (fr.f) E Replace pair(s) with f and update G¹. Note that token ft E N will not be removed from G unless fi and all its adjacent pairs (fi, E E have been merged.
3. Repeat step until no token pairs match tokens in & inal-
Chemical Information Incorporation. Our vocabulary can be easily extended to incorporate domain knowledge. (i) To distinguish Chirality, the algorithm operates on 2D molecular graphs augmented with 3D conformer information. The use of 3D coordinates ensures that stereochemistry is preserved during tokenization. As result, each token is assigned a unique isomeric SMILES string as vocabulary identifier, encoding both atomic connectivity and chirality. For example, L-lactic and R- are represented by C [C@H] (0) C (=0) O and C C[CeCH] (O) C (=0) C, respectively. (ii) Aromatic integrity is ensured by two complementary mechanisms. First, aromatic rings are treated as indivisible basic tokens, which are preserved throughout the bottom-up merge process. Second, the overlapping tokenization strategy allows for progressive merging of neighboring tokens, enabling the discovery of extended conjugated systems while maintaining aromatic consistency. (iii) To properly encode atomic attributes, such as charged and aromatic atoms, we assign explicit identifiers to atoms with formal charges and/or aromatic participation. For instance, [c1-] denotes negatively
Aromatic rings treated as single delocalized systems to preserve conjugation. 'n BPE, tokenization starts from an initial vocabulary of basic tokens (e.g.. characters or word pieces), and iteratively merges the most frequent adjacent pairs. Since merging only combines existing tokens without splitting them, the original basic tokens remain intact.
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A.
B.
Clobal Embeddding
Pragmemi
Fragmenti
Fragment Embedding
Equir
Attendion
Mlayers
Ri-de Figilll sypertoo
Atom Entoddiing
di EquivithN
Grobal
Bidit Equty
Atom
Hararchica Mokcozaar
Representatens
Figure 2: Overall model architecture. (A) Global node, fragments, and atoms in the ligand molecule of input pair. (B) The aggregation of fragment and global embeddings. (C) An encoder layer of h-MINT. Note: Solid lines indicate connection within the same level. Dashed lines indicate connections across different levels.
charged chlorine atom, while [n+] indicates a positively charged aromatic nitrogen. In contrast to standard SMILES, which often omit such details when representing isolated atoms, our token identifiers explicitly preserve these chemically significant properties.
3.2 H-MINT
In this section, introduce the hierarchical Molecular Interaction NeTwork (h-MINT), designed to accommodate the overlapping atom-fragment mappings induced by OverlapBPE. Existing hi- erarchical molecular networks typically rely on disjoint atom-to- tor----m-frgetttttt assignments, limiting their ability to model many-to-many relationships and bidirectional cross-scale interactions. To overcome this restriction, we develop an -quuvarquaaa graph Transformer that (i) adapts its self-attention, feed-forward, and normalization layers to maintain equivariance, and (ii) explicitly preserves atom-token overlap to enable flexible cross-scale information flow. The overall architecture is in Figure 2
3.2.1
MODEL INPUT AND EMBEDDING
The model receives ligand pair as input, including their atoms (V Vi0 tokens (V/ V
and atom-token mapping Tp S V x Vf, p. S Vi x VL Note that: (i) each node /a or V has type (e.g., element type N, token type [Cl-]). (ii) The mapping function T has 2 parts to map atom index to associated token indices Tazs and vice versa Tyza, yet do not operate on atom and token types. Thus, the embeddings can be obtained:
H = Embed p V Tp), Hº Embed V2, V Ti), 7 Embed(VV) (eatter/ean(Embed(( v )), Tyaa) Embed(Pos(VV"),
(2)
where scatter_men Pos(-) simply maps an atom to its position code To collect global information from atoms and tokens, we also augment special <global tokens to all node lists Va Vi, V..V Some example inputs can be found in the Appendix C.T
aggregate atom's associated token types information, and
3.2.2 BUILDING HIERARCHICAL GRAPH
The embedding layer only encodes type and position information, and also uses Tp. Ti to map token type embedding into atom embedding dimensions. In this section, further consider 3D structures and build hierarchical graphs for message passing, as shown in Figure I Before we start building the graph, need to specify that the edges are directed, which means for any token-level edge (fr, ff) or atom-level edge (a,, a,), the former node will receive message from the latter.
We use subscript and for pocket and ligand, superscript a and f to distinguish atom and token (fragment). TTh position code for residue atom is its atom name, e.g., CA + for molecular atom is special token <sm>, We leave the complete position code table in the Appendix C.I

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KNN Token Graph. In the token level, every pair of input contains sequence of pocket tokens and a sequence of ligand tokens, plus 2 augmented global tokens, as follows:
V Ur Jp.o Sp.1. St.: Jt,0, Jt.2.-...
(4)
where we use fo and fi to represent pocket and ligand tokens, and subscription f.g and f, to denote corresponding global token and i-th token. In this step, we construct KNN graph for non-global tokens, where the distance between token f. and f, is the minimum distance between all their atoms,
min dist(as, At), dist Jis fj) a.Efi,aEf,
(5)
For the two global tokens, we connect them with their following tokens to aggregate information within each pocket and ligand, and connect the 2 global tokens to exchange pair information. Therefore get the set of edges EKAN {(fi, VØ UV/ .f E (NN(f))UU(f.rr f.)lfa V' U { (fp..: ft.g): For simplicity, we use KNN(f:) (JI(s. E EENN to denote neighbor- hood of fi.
Token-expanded Atom Graph. The atom graph is constructed through expanding each token- level edge from ENNN into several atom-level edges. More specifically, for token-level edge in fy) E we connect each atom with atoms from the other token. In practice, control the number of atom-level edges, for an atom ag, we connect Q2 E fi with the k-nearest atoms in fj- Through this expansion, we obtain a set of atom-level edges that contain short-range interactions within atoms' neighborhoods, as well as long-range interactions bridged by token-level edges. Finally, EkNN].
get the set of atom-level edges E {(ag, aalaa E fi, a2 knn(f,, ag), (fr, fr) Ef and we use knn(fi, f,) to denote the set of atom-level edges expanded from (ft.f).
3.2.3 BILEVEL MESSAGE PASSING
Given the aforementioned atom embeddings (H); HP] and now introduce our bilevel message passing through equivariant bilevel graph attention.
atom-level edges F
Equivariant Bilevel Graph Attention. For token edge (fr, fJ) (5)+ E EE and their expanded atom edges ((as, at) a. E fv, 0g E knn(f,, we first compute atom-level cross attention based on input atom embeddings H!-1 where is the current layer:
QQ; v/]
LI
(6)
Score : Si. [a,, az} = MLP(Q' K' [a.), RBF(D((as or]), ei.j),
(7)
Attention Weight aii. [as, ar] Softmax,, , (St, (a,, W.),,
where Q'[az], K' [ar] are query and key vectors of a,, a, RBF(D(a, arl) embeds the relative position of a, to ag. e,, is the edge type embedding (ff. fy and Wa is to project the scores into scalars. Through Softmax in Eq. g a, is able to aggregate messages from E knn(ff, a,).
The token-level cross attention is defined through aggregating all atom-level edges expanded from the loken-level edge.
Score: St, knn(fa. fyyl
Σ
Miylas, az).
(9)
(a, (.)m))
Attention Weight: Bıg = (KNN, We),
(10)
where Ws projects the scores into scalars. Basically, St.j aggregates all atom-level edges expanded from (5). Then we have the following message passing and embedding update:
mi.la.]
Σ
as ar) ('
(11)
at Es.nn()
m; m;(a.)
[a.]
Σ
Big (...
(12)
fEEKNN(A)
H' [a.] +- [1_)" ScatterMean(m,(e. TTy)
(13)
We use different edge types distinguish intra- and inter-molecule edges.

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Table 1: Mean and standard deviation of three runs on the PDBBind benchmark. The best
results are marked in bold, and the second best are underlined. Baseline results are borrowed from (Wang et Kong al. 2024). Details in Appendix
Model
RMSE 1
Pearson f
Spearman f
DeepDTA
1,866 0.080
0.472 # 0.022
0,471 = 0.024
Bepler and Berger's
1,985 0.006
0.165 + 0.006
0.152 0,024
TAPE
0.035
0.338 = 0.044
0.286 = 0.124
ProtTrans
0.015
0.438 0.053
0.434 = 0.058
MaSIF
1.484
0,467 # 0.020
0.455 # 0.014
Separate
IEConv
0.016
0.428 0.032
Encoder
Holoprot- Full Surface
1..066
0.509
0.500
Setloprot-Superpixe
1..00
0
ProNet-Amino Acid
0.009
0.526 0.016
ProNet-Backbone
0.0003
0.546
0.550 0.501
ProNet-All All-AAttooo
1,463
0.551 = 0.008
ESM-2 fingerprint
1.537 0.001
0.455
0.433 = 0.009
GVP
0.003
Atom3D-3DCNN
0.021
0.550 + 0.021
0.553 = 0.009
Atom3D-ENN
1,568 0.012
0.389 + 0.024
0.408 0.021
Joint
Atom3D-GNI
1,601 0.048
4
0.533 = 0.033
Encoder
GET
0.007
0.586
0.575
GET-Murcko
1.415 0.010
0.590
0.578 + 0.003
GET-BRICS
1.410 0.008
0.592 0.003
0.579 0.004
GET-PS
1.387 0.015
0.601 + 0.002
0,582 # 0.005
Ours
1,295 0.001
0.640 = 0.002
0.625 = 0.002
where [a[a the value vector of a, at layer-l. Stacking equivariant bilevel attention, equivariant feed-forward layer, and equivariant layer normalization together, we obtain an equivariant graph Transformer layer, as shown in F For more details, please refer to Appendix C.2
4 EXPERIMENTS AND RESULTS
In this section, evaluate our OverlapBPE and h-MINT model in two fundamental drug discovery tasks: binding affinity prediction (Section 4.1 and virtual screening (Section[4. 4.2). We conduct further experiments and case study for OverlapBPE. in incorporating chemical information and representing chirality in Section 4.3
4.1 BINDING AFFINITY PREDICTION
Task Definition. Given the 3D structure of a pocket-ligand pair, the task is to predict the binding affinity, i.e., change in free energy upon binding. The input is complex (p, 1) with 3D structure, and the output is real value y. Performance is assessed with regression metries such as root-mean- -quure error (RMSE) and Pearson/Spearmer correlation against experimental affinities.
Setup. We follow Somnath et al. 202 Wang et al. to conduct experiments on the well-established PDBBind benchmark (Wang al. 2005 and split the 4, 7,90 complexes according sequence identity of the protein using a 30% threshold. We also employ the LBA dataset with its predefined splits from
the Atom3D benchmark (Townshend al. 2020). Both PDBBind and LBA comprise 3,507/466/490
protein-ligand complexes for training/valdaaiion//
with major difference in pre-processing. For
the baseline models, we compare against a variety of approaches (Öztürk et al. 2018 Bepler Berger
2019 Rao et al. [2019: Elnaggar et al. 2022
Gainza et al.] (ermosse et al. 20200 SSomnath
ct al.
202
Wang et al.) Jing et al. 2021
Townshend et al. [2200] Schütt et al. 2017 Gasteiger
et al. 2+iingerprrnt) which incorporates ESM-2 embedding to represent pockets and traditional fingerprints
202 Zhou al. 2023 Gao et al. Feng et al.). We also include another baseline (ESM-
- to represent molecules. Details provided in Appendix [] Among these models, we primarily focus our comparison with GET (Kong et al., (024) and its variants, as currently achieves the best performance and shares similarity with our method. Specifically,

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Table 2:
Mean and standard deviation of three runs on LBA prediction. The baseline results are from Kong et al. (2024). Models with are large pretrained models, results from (Gao et al., Feng a..) The best results are marked in bold and the second best are underlined. Note: To save space, we only report the best baseline settings from atom-level, fragment-level, and bi-level. Details in Appendix
D
Best Repr. Setting
Model
RMSE 1
Pearson 1
Spearman f
SchNet
1.357 : 0.017
0.598 : 0.011
0.592 : 0,015
EGNN
1.358 # 0.000
0.599 # 0.002
0.587 # 0.004
LEFTNet
1.343 = 0.004
0.610 = 0.004
0.598 = 0.003
Atom-level
ET
1.367 # 0.037
0.599 # 0.017
0.584 0.025
UniMo
1.434
0.565
0.540
BigBind
1.340
0.632
0.620
ProFSA*
1.377
0.628
0.620
GemNet
1.393 0.036
1.002
0.553 # 0.026
Equiformer
1.350 0.019
0.004
0.591 = 0.012
Frag-level
DimeNet++
1.388 0.010
0.002
0
MACE
1.372 0.021
0.612
0.592 # 0.010
GET
+.311000008
0.618
0.607 # 0.005
Bi-level
GET-PS
1.312
0.631 0.011
0.642 = 0.011
Ours
1.276 0.011
0.660 0.001
0.661 0.001
include GET-PS, GET-Murcko. and GET-BRICS, which are GET models incorporating the Principal Subgraph tokenization (Kong et al. 2022b), Muris-Murcko scaffolding (Bemis & Murcko 1996). and BRICS tokenization (Wegscheid -eriaahh et al.).
Results. We employ OverlapBPE the ligand molecules while utilizing residues as tokens for pockets, and conduct evaluations on PDBbind and LBA datasets separately. Table [ and report the mean and the standard deviation of the metrics across 3 runs for the PDBbind and LBA datasets. Our model demonstrates significantly better performance over baseline methods in the binding affinity prediction task. Our improvement suggests that our tokenization and modeling may better preserve interaction-relevant chemical features. Significance test and more detailed analysis are in Appendix D
4.2
STRUCTURE-BASED VIRTUAL SCREENING
Task Definition. Given the 3D structure of pocket and a library of ligands {4), the task is to retrieve candidate ligands that are possible bind with p. A Virtual Screening (VS) model learns a score 8; for each pair (p, 4)), and ranks all candidate ligands in descending order of 8;. The evaluation metrics include the area under the ROC curve (AUC), enrichment factor at a given top-k threshold (EF@ k), and BEDROC, which emphasize early-recognition of true binders.
Setup. Evaluation was performed on DUD-E (Mysinger et al. 2012) following preprocessing by Gao et al. (2023) and LIT-PCBA (Tran-Nguyen et al. 2020). DUD-E contains 22,886 active compounds with binding affinities across 102 protein targets (about 224 ligands per target), and for each active ligand provides around 50 decoys that match phssicoclem properties but differ in 2D topology. The dataset is mainly used to test how well docking methods can distinguish true binders from -on-binders. The LIT-PCBA dataset (Tran-Nguyen et al.) 2020) is constructed from dose-response PubChem bioassays mitigate the target and decoy selection biases found in other benchmarks. It comprises 15 protein targets with 7844 experimentally confirmed actives and 407381 inactive, which reflects realistic hit rates in nightthroughput screening. We include other two benchmarks DEKOIS (Vogel et al. 201I). and JACS/Merck (Wang et al.) 2015) Schindler et al. 2020) as used in (Feng et al. (025) in Appendix F.44 We benchmark against classical docking tools
(Hlggen et al.
2004, Trott et al. 2009). early MI scoring functions (Durrant & Mccammon 2011
Ballester et al.
2010 Stepniewska Steptiewska-Dzziuinnke et al. 2017 Zheng et al. 2019: Zhang et al. 2023).
and contrastive learning frameworks DrugCLIP (Gao et al. 2023) and Ligtnity (Feng et al. 2025
h-MINT adapter: We observe that baseline models commonly benefit from pretrained UniMol model improve representation quality. We therefore incorporate h-MINT as a lightweight adapter on top of the UniMol encoder to assess its effectiveness in conjunction with large pretrained models. Since DrugCLIP and LigUnity use different data augmentations, for
PDBBind-only finetuning:
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Table Zero-shot Virtual Screening on DUD-E. Baseline results are from (Gao al. 2023 Feng et al. 2025). Models with are trained with PDBBind data only for fair comparison. Details in Appendix
AUC (%)
BEDROC (%) f
EF
0.5%
1%
5%
Glide-SP
76.70
40.70
19.39
16.18
7.23
Vina
71.60
9.13
7.32
4,44
NN-score
68.30
12.20
4.16
4.02
3.12
RFscore
65.21
12.41
4.90
4.52
2.98
Pafnucy
63.11
16.50
4.24
3.86
3.76
OnionNet
59.71
8.62
2.84
2.20
Planet
71.60
10.23
8.83
5,40
DrugCLIP 81.39
45.96
34,27
29.01
10.18
LigUnity
81.69
46.01
34.44
29.07
10.26
Ours
84.45
47.64
35.06
29.91
10.76
Table 4: Zero-shot Virtual Screening on LIT-PCBA. Bascline results are from Gao et al. (2023);
Feng et al. (2025); Jia et al. (2024). Models with are trained with PDBBind data only. Bold numbers indicate the best performance.
AUC
BEDROC
EF
(%)
(%) f
0.5%
1%
5%
Surflex
51,47
2.50
Glide-SP
53.15
4.00
3.17
3.41
2.01
Planet
57.31
4.64
3.87
2.43
Gnina
60.93
5,40
4.63
DeepDTA
56.27
2.53
1.47
BigBind
60.80
3.82
DrugCLIP
58.15
4.12
4.11
3.08
2.27
LigUnity* 57.61
4.34
4.06
3.03
2.25
Ours 57.77
6.27
7.01
5.20
2.18
fair comparison, we train these models and our model with PDBBind (Wang al. 2005) only, which comprises 16,744 nocket-ligand pairs, with any overlap with the test sets removed. Following the conventions (Feng et al., 20255; Gao et al. 2023), these models are initialized with pretrained checkpoint of UniMol (Zhou et al. 2023).
For detailed implementation, please refer Appendix
Results. We report AUC, BEDROC, and enrichment factors (EF) DUD-E and LIT-PCBA in the zero-shot setting in Table 3 and Table Our model demonstrates strong generalization under the zero-shot setting, highlighting the expressive power of OverlapBPE and the effectiveness of the hierarchical design in h-MINT. Notably, our method consistently outperforms state-of-the-at baselines across all evaluation metrics, underscoring its capability accurately capture fine- -raiggaandd -otein-ligand interaction patterns.
4.3 FURTHER EXPERIMENTS OF OVERLAPBPE
In this section, we provide additional analysis about our tokenization method's 's advantages in two aspects: incorporating chemical information and representing chirality.
Incorporating chemical information. Figure 3 illustrates case study from the LBA dataset. Our
tokenization preserves the integrity of the benzene ring and retains the positive charge of [N+], which is necessary for forming the pi-cation interaction between the ligand and the protein pocket. In contrast, the PS tokenization treats [N] neutral and thus cannot capture this interaction. This difference contributes to more accurate affinity prediction, with our method achieving an error of 0.56 compared 0.67 for the PS tokenization.

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Published as a conference paper at ICLR 2026
Overlao@PEjours)
C.
PS tokemication
Unnchonged group
PS tolien
Avomatic lonunble group
Brolco nigs
Figure 3: OverlapBPE (ours) better preserves aromatic bond integrity, and ionic states. (A) An interaction formed between the ligand and the protein pocket. The ligand contains a positively charged [N+], which forms two и-cation interactions with two aromatic rings in the protein pocket. (B) Representation using our tokenization method. Green colors indicate fragments without charge. Red colors indicate charged fragments. (C) Representation using the PS tokenization method, where green and red indicate fragments above, but the [N+] charge is not preserved, and some rings broken.
Representing chirality. To further validate the effectiveness our overlap tokenization and its capa- bility to represent chirality, we followed MolKGNN (Liu et 2023) and conducted high- high-throughput screening (HTS) experiments on PubChem assays as binary classification task. Specifically, we only use the OverlapBPE tokenization with XGBoost (Chen Guestrin 2016) and do not adopt the h-MINT model for two reasons: (i) the dataset lacks pocket structures, and (ii) HTS places stringent demands on efficiency. We evaluate OverlapBPE on 8 PubChem HTS assays in Table(5) The baselines include strong atom-level GNN models such SchNet (Schütt et al. 2017). SphereNet (Liu et al.) 2022), ChiRo (Adams et al. 202T), KerGNNs (Teng et al.) 2022) and MolKGNN (Liu et al. 2023). For OverlapBPE, investigate two variants: chiral vocabulary that preserves chirality in tokens, and a non-chiral vocabulary that omits stereochemical information. All baselines rely on extensive molecular features and complex model architectures, whereas our method only utilizes tokenized bag-of-words features combined with XGBoost for classification. We report carly-enrichment perfor- mance using logAUC1a.000 fo.ooo.oo..0... 00.001,0.1] in Table 5 and observe that: (i) Our chiral method significantly outperforms non-chiral one, showing the importance of chirality information in HTS tasks; (ii) Our method outperforms all baselines in average ranking, and even exceeds ChiRo and MolKGNN, which designed to represent molecular chirality; (iii) Leveraging XGBoost's lightweight feature, our method completes training and prediction within second.
Table Early-enrichment performance on PubChem HTS assays. Metric is logAUC (0.001.0.1 (higher is better). Ours (chiral) preserves the chirality of tokens and Ours (non-chiral) does not. Bold numbers indicate the best method per dataset. Baseline results are taken from Liu et al. (2023).
PubChem AID
MelKGNN
SchNet
SphereNet
DimeNet++
ChiRo
KerGNN
Ours (chiral)
Ours (non-chiral)
435008
0.255
0.187
0.215
0.203
0.168
0.147
0.221
0.211
0.196
0.208
0.165
0,282
435034
0,227
0.246
0.230
0.235
0.211
0.179
0.281
0.261
2258
0.301
0,240
0,380
0,340
0.251
0.195
0.265
0,246
463087
0.390
0.332
0.399
0.389
0.258
0.150
0.338
0.322
488997
0.303
0.319
0.309
0.315
0.193
0.081
0.384
0.376
0.324
0.264
0,343
485290
0.498
0.333
0.450
0.463
0.295
0.223
0.474
0.341
Rank
3,250
5,250
3.125
3.375
6.375
8.000
2.625
4,000
5 DISCUSSION AND FUTURE WORK
We introduced OverlapBPE and h-MINT, an efficient ceteiizationn-lus-laaatte framework for protein-ligand interactions that achieves superior performance on affinity prediction and virtual screening. Our results demonstrate that explicitly modeling fragment overlaps is not only feasible but necessary for preserving chemically meaningful context, leading to consistent performance gains across diverse tasks. For future work, we plan to validate OverlapBPE on molecular tasks and extend h-MINT docking pose prediction and broader drug design enzyme engineering tasks.
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ACKNOWLEDGMENTS
Research was supported in part by the Molecule Maker Lab Institute: An AT Research Institutes program supported by NSF under Award No. 2505932, and the DOE Center for Advanced Bioenergy and Bioproducts Innovation (U.S. Department of Energy, Office of Science, Biological and Envi- ronmental Research Program under Award Number DE-SCO01 8420). Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the U.S. Department of Energy. Yijie Zhang received support from the Canada First Research Excellence Fund and the Fonds de recherche du Québec awarded to the D2R Initiative at McGill University.
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A ETHICS STATEMENT
Small-molccule modeling plays critical role in drug discovery, with broad potential applications in therapeutic development, virtual screening, and rational design of ligands targeting protein pockets. Advances in representation learning and interaction modeling offer new opportunities to accelerate discovery and improve our understanding of molecular interactions, which may positively impact medicine, biotechnology, and related fields.
At the same time, we recognize that such computational approaches also carry potential risks, particularly regarding misuse in unsafe or unethical drug design. To mitigate these risks, this study is conducted exclusively on publicly available datasets and strictly follows established ethical guidelines. We advocate for the responsible research and application of molecular modeling methods to ensure their development contributes to societal benefit.
B REPRODUCIBILITY STATEMENT
We ensure that the training data, training and inference procedures, and result evaluations are all reproducible. The appendix provides all necessary details and offers comprehensive explanation of each component of this work. The datasets used are publicly available, and the model implementation
based the open-source GET (Kong al. 2024). LigUnity (Feng et al. (025) and MoIKGNN (Liu et al. (023) codebases. The code and models used for evaluation are also publicly accessible and cited the appendix. Furthermore, we describe the training hyperparameters detail in the appendix, thereby ensuring that the entire experimental process is fully reproducible.
C METHOD
C.I OVERLAPBPE
NH
cccson1
NH
c1cson1
nc[nH]
NS(=O)|-OJc1coocee
Figure Comparison of overlap (top) and non-overlap tokenization (bottom).
Overlap vs. Non-overlap Tokenization In Figure we show an example molecule tokenized in different ways. We use different colors to highlight the tokenized fragments. From the top figure, we can see the molecule has 4 tokens, with 3 of the tokens sharing atoms with other tokens (we also highlight the shared atoms and bonds in red). From the bottom figure, we can see that an aromatic bond is broken and forms 2 disjoint tokens. From this figure, can identify the difference between serraa-tookenization and non-overlap-tokeee tokenization. With local chemical environment and better respecting the fuzzy boundaries of substructures in small molecules, we believe our novel OverlapBPE potent to boost small molecular representation, interaction, and even generation tasks.
better preserving the
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Example Input. Here we show some example inputs the encoder, more specifically, the em- bedding layer. The embedding layer takes 3 inputs: atom sequences, atom-level position codes, and token sequences, for pairs of pocket-ligands.
p Vial atg_atom>, N, C, C, O, C, <g sg_atom>, C, C, ...]
(14)
(V))) Pos (VT)) pog_pos>, M A, v, M B, .... <g_pos>, sm, sm, ...]
(15)
V 1/: [<g__rag>, ALA,
<g <g_frag>, clcsenl ...]
(16)
Position Code. Certain types of molecules have conventional position codes to distinguish different atoms with the same element type in the same fragment, e.g., CA and CB in residues. For small molecules, since there are no such conventional position codes, we simply use am as the position code for all atoms in small molecules. In addition, we also add some special position codes for special tokens, e.g. <global>, <mask>, <pad>.
Tokenization Overhead. Compared to the PS tokenizer and GET, the computational overhead of OverlapBPE and h-MINT mainly stems from the repeated calculation of atoms. We performed tokenization using OverlapBPE and PS on the 3,507 molecules in the LBA training set, and the results are shown in T
Table Comparison of tokenization statistics.
Tokenizer
Avg # tokens mol
Avg atoms / token
OverlapBPE
7.95
4.5
Principal Subgraph
3.39
For tokenization, the number of atoms processed by OverlapBPE is 1.32 times that of PS:(7.95 x 4.5) / (8 x 3.39) 1.32. However, since tokenization can be executed offfine and is fully parallelizable, the overhead is negligible in practice. For instance, OverlapBPE only takes minutes process 47.9k molecules using 32 CPUs for virtual screening training data.
C.2 H-MINT
SE-(3) Equivariance. In this section, we provide more details about the model design. At first, h- MINT follows GET's (Kong et al.] (2024) architecture with 2-channel updates: One is the equivariant channel, which mainly encodes and predict the coordinates of molecules following SE-(3) symmetry. The other is the invariant channel, which mainly encodes and predicts embeddings (i.e., H). Thus, in general, our model is an SE-(3) equivariant model. Since we only use the embedding channel in this paper, mainly show the update and message passing for the invariant channel. However, model can also be extended to SE-(3) scenarios like structure prediction and generation.
Difference with GET. We want to emphasize the difference between h-MINT and GET. GET is designed for non-overlap tokenization, while h-MINT is designed for overlap tokenization, and this induces a fundamental difference in model design. For GET, atoms and tokens are 1-1 mapping, making its model design simple and straightforward. For h-MINT, atoms and tokens are in maay-to-mamn mapping. which requires a bidirectional indexing system to convert atom-level embeddings to token-level embeddings and vice versa.
In Section 3.2] we present the embedding layer, graph construction, and Bilevel Graph Attention Layer. For the next, complement other modules, including Bidirectional Equivariant Feed-Forward Network (FFN) and Bidirectional Equivariant Layer Normalization (LN).
Bidirectional Equivariant FFN. For this module, the input contains the atom-level embedding H passed from the former module, and also the bidirectional mapping between atoms and tokens
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Taz5, Tyza- The update is follows:
11's ScatterMean(IH (11', To2s)
(17)
H" ScatterMean ,Tyaa) (Tpa)
(18)
H' H' + MLP(( H² ;
(19)
where H" can be regarded as lkknnennannced atom representation, and D stores the pairwise atom distances.
Bidirectional Equivariant LN. This module involves normalization within each input pair, since we do not use the equivariant channel. Here, we apply simple Layer Normalization to the representa- tions.
H' H' E
μ.,
(20)
Var +E
where a and j are learnable parameters, and El and Varl] are used to calculate the mean and variance of the variable.
D EXPERIMENTS: BINDING AFFINITY PREDICTION
D.1 BENCHMARK DETAILS
We follow the data processing of (Somnath et al. 2021 Wang et al.) to conduct experiments on the PDBBind (v2019) Benehmark. More specifically, we use the split with a sequence identity of 30% to prevent leakage. This filtering results in 4,709 complexes. which are then split into 3,507, 466, and 490 for training, validation, and testing (Somnath al., 202I). We directly borrow the baseline results from (Wang et al. Kong et al. 2024). The main advantage of GET-PS and our model over other baselines is tokenizing small molecules into fragments. We simply remove pairs without small molecules, which results in slightly smaller split (4436 samples) compared to the original identity30 data split (4463 samples).
D.2 BASELINE MODELS
Here briefly introduce the baseline methods. For the PDBBind benchmark, DeepD'TA (Öztürk
ct al., 20188) Bepler and Berger's (Bepler & Berger 2019). TAPE (Rao et al., 2019). ProtTrans
(EInaggar al. 2022), MaSIF Gainza et al. (2020), IEConv (Hermosilla et al. 2020). Holoprot
(Somnath et al. 2021), and ProNet (Wagg et al.) use separate encoders for pockets and ligands. GVP
(Jing et al. 2020). Atom3D (Townshend et al. 2020), and GET (Kong et al.) 2024) instead use joint encoder for pockets and ligands. Inspired by the good performance and trends in joint encoder models, we also adopt a joint encoder architecture. For the LBA dataset, SchNet (Schütt et al. 2018), +imeNet++ (Gasteiger et al.) 2020), GemNet (Gasteiger et al. (02T) are invariant models based on invariant geometric features (Le., distance and angle). EGNN Satorras et al. 2021), TorchMD- (ET) TTōöke & De Fabritiis 2022), ) and LEFTNet (Du et al. 2023) preserve equivariant features and are directly implemented on 3D coordinates. MACE (Batatia et al. 2022) and Equiformer (Liao) & Smidi 2022) utilize harmonic and irreducible representations preserve high-order equivariant features. We also include atom-level pretrained models, UniMol (Zhou et al. 2023), ProFSA (Gao al.) and BigBind (Feng al.). In general, all these models mainly use their invariant channel for affinity prediction, similar to GET (Kong et al. (2244) and our model; thus, we can ignore how these models deal with equivariant features in these experiments. We borrow the baseline results mainly from GET (Kong et al., 2024), which provides complete comparison of all the above models in 3 representation settings: atom-level, fragment-level, and bi-level. To save space, we include each model's 's best representation setting only.
D.3 IMPLEMENTATION DETAILS
We conduct experiments on RTX A6000 GPU. Each model is trained with the Adam optimizer and learning rate decay. Considering the number of tokens and atoms may vary with different input
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complexes, safely and efficiently utilize the GPU memory, we implement dynamic batch to include as many complexes as possible while not exceeding some threshold max_ n_ o_vertex. For graph construction, we use k = 9 for the KNN token graph. which means each token is connected with nearest tokens within a complex. And we use k = 3 for the token-expanded atom graph, which means for atom 0s E ft, for each KNN of the token, fj E (NN( 0s will connect in fj. We set the RBF kernel size to be 32. For baseline models, we follow the official parameter configuration. For our model, we mainly tune the learning rate (Ir [le -3,, Ie 4), final learning rate (ffr E [le - 3, le - -6), and max number of epochs (max_epode 40),
nearest atoms
D.4 ADDITIONAL EXPERIMENT ANALYSIS
Significance of Improvements. Since the benchmarks (PDBBind and LBA) are relatively small, to guarantee fair comparison and consistent results, we report mean and std for 3 runs in Table 1 Besides, we also conduct a significance test on the prediction results of GET, GET-PS and our model. And the results show p-values < 0.005 for these models in both PDBBind and LBA tasks. These evidences conclude that our model performs significantly better than the strong baselines, GET, and GET-PS.
and 2
PDBBind Results Analysis. From Table T we can mainly draw the following conclusions: (i) Joint encoder is generally better than separate encoders. (ii) GET outperforms other baselines by a wide margin due to its hierarchical modeling, and GET-PS performs even better. (iii) Our model consistently outperforms GET and GET-PS by wide margin (4%-5% Pearson Correlation, and 5% Spearman Correlation). We are also surprised by the improvements, and we recognize that the improvements come from new tokenization, OverlapBPE, and new model h-MINT.
LBA Results Analysis. Table 2 compares different models with atom-level, fragment- -eeeel, and bi-level representations. We borrow the bascline results from (Kong et al. 2024), which compares all baselines in all 3 representation settings. In this paper, we only include the baseline results in their best representation settings for saving space. From this table, we can see: (i) In general, li-le representation is better than atom-level or fragment-level. This is reasonable because some molecular interactions happen on atoms (H-bond) and some happen fragments (I stacking). Thus, we believe integrating bi-level information is crucial for modeling molecular interactions. (ii) GET and GET-PS still outperform other baselines by a wide margin (2% Pearson and Spearman Correlation). which demonstrates the effectiveness of their unified representations and model design. (iii) Last, our model outperforms GET and GET-PS even more than their improvements (3% Pearson Correlation, 2% Spearman Correlation). This result again validates the effectiveness of our OverlapBPE tokenization and our new model for many-to-many mapping between atoms and tokens.
Ablation Study of OverlapBPE and h-MINT model. OverlapBPE: tokenization and overlap- compatible hierarchical interaction model, jointly form an integrated framework to tackle the chal- lenge of fuzzy boundaries of meaningful molecular substructures in 3D molecular interaction mod- cling. Meaningful comparison can only be made when they'r 'e used together, because (i) no other network architectures are available for overlapping substructures, and (ii) when non-overlapping tokenization is used, the molecular graphs for h-MINT and GET become identical. As evidence, we provide the following ablation on LBA dataset that adopts non-overlap PS tokenizer for h-MINT in Table
Table 7: Ablation Study of OverlapBPE and h-MINT on LBA.
RMSE 1
Pearson 1
Spearman 1
GET
1.331 + 0.008
0.618 + 0.005
0.607 + 0.005
GET+PS
1.312 # 0.016
0.631 # 0.011
0.642 0.011
h-MINT-PS
1.321 # 0.010 N/A
0.633 # 0.007
0.641 # 0.008
OEe+O
N/A
N/A
Ours (H-MINT++OverapBPE))
1.276 # 0.011
0.660 # 0.001
0.661 * 0.001
As can been seen in this table, GET is not compatible with OverlapBPE. OverlapBPE+hMMNN consistently outperforms models with PS tokenizer, demonstrating clear gain of OverlapBPF.
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When non-overlap PS tokenizer is used, GET+PS and h-MINT- achieve similar performance as expected. The tokenizer and h-MINT architecture are both necessary to handle overlapping fragments and preserve key chemical information (atomic integrity, ionic states, chirality).
E EXPERIMENTS: VIRTUAL SCREENING
E.1 DETAILS ON Loss FUNCTION
According to LigUnity (Feng et al. 2025). we optimise composite objective
C (Cp-st + C(mp) + Lrank + Amse Lonse,
(1)
LiigUnity
which extends the original loss with an additional regression term.
Contrastive retrieval losses. For mini-batch of B pocket embeddings p, and ligand embeddings (/ we define
B Σ log Σラ= exp(r (pi,
exp(T 4))
fp-st
Li-p sym.,
Listwise ranking loss. Given pocket i with Mj ligands sorted by experimental affinity æΙ > ...>> TM.
(x (x)))
Lonk Σ
Ms log M. EiEh exp(T (pi, (pi))a.))
with positional weights LigUnity model, and the PDBBind dataset contains 1 to pocket-ligand pairs only, the listwise ranking loss here was not functional.
log(k++1) However, as we used PDBBind dataset to re-train the
MSE loss. Similar to the regression loss implemented in the LigUnity paper, let âij be the predicted activity for pair (i,j) and dzy the ground truth. With p the positive set, N represents 20 % subsample of negatives, pocket-wise weakest positive amin, and safety margin ö, which was set to 2.0:
(max(0, (0, (Amin,. 03) 5))}²)
Lonse |PI+ INT
Σ (āij aij)) + (R.))P
Σ
(4)
(W))
Throughout this paper, we fix the weights to Amse = 2. Empirically, the extra MSE term accelerates convergence and mitigates the overfit the training dataset.
E.2 IMPLEMENTATION DETAILS
The implementation of virtual screening experiments includes two parts: training the baselines and finetuning our model. All the models were trained on RTX 6000 GPU with 24 GB memory. For the baseline models, retrained LigUnity (Feng et al. 2025) evaluation, we excluded all the samples that exist in any of the test datasets. We also followed their papers' original parameters. To evaluate, averaged the weights of the last 3 model checkpoints. We finetuned our h-MINT model on the same PDB Bind dataset. Namely, the training parameters similar to LigUnity, with learning rate le 4, warmup ratio 0.06, and the maximum number of ligands selected for each pocket was 16. However, we used 32-bit precision for training rather than the original 16-bit, changed the batch size to 96, and set the gradient clip 10. We trained the model for 100 epochs initially, and observed that it converged at around the 25th epoch. Therefore, we trained for 25 epochs and averaged the last 3 checkpoints for evaluation purposes.
the PDBBind dataset. To ensure fair
E.3 EVALUATION METRICS
We assess model performance using the following metrics:
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Table 8: Zero-shot Virtual Screening on DEKOIS.
AUC (%) f
BEDROC
EF
(%) f
0.5%
1%
5%
LigUnity
76.92 81.05
47.20 47.71
18.57 18.085
16.25
8.21
Ours
16.77
8.74
Table 9: Zero-shot Virtual Screening on JACS/Merck.
y
LigUnity
0.173
Ours 0.216
AUC-ROC (Area Under the Receiver Operating Characteristic curve): measures the probability that randomly chosen active compound is ranked higher than a randomly chosen inactive one. In our paper, use AUC to denote it.
BEDROC BBotzmann-Enhance Discrimination of Receiver Operating Character- istic): emphasizes early recognition of actives by applying an exponential weighting to the ROC curve, controlled by tunable parameter. Following previous works, we set the parameter to 80.5.
Enrichment Factor (EF): quantifies the fold-increase in actives found among the top percentile of the ranked library relative to random selection, reported here at 0.5%, 1%, and 5%.
E.4 ADDITIONAL RESULTS ON DEKOIS, JACS/MERCK, AND SIGNIFICANCE TEST
We also provides comparison with LigUnity following (Feng et 2025 in Table and 9 These results validate that h-MINT generalizes effectively across datasets and tasks, including both affinity- ranking and cittullsscrennng benchmarks.
To demonstrate the significance of our results, we conducted statistical significance tests for all benchmarks (DUDE, PCBA, DEKOIS, and FEP). For all comparisons between h-MINT(ours) vs. LigUnity, we obtained p-values 0.005, demonstrating that the improvements are statistically significant and consistent, rather than due to random variation.
E.5 ABLATION OF MSE LOSS OVER LIGUNITY
To isolate the contribution of this loss, we trained LigUnity with additional MSE loss (exact same combined loss ours) under identical settings the PDBBind training set, as shown in Table TO The results on DUDE and LIT-PCBA are shown in the table above, which confirms the following findings: (i). Effect the proposed auxiliary loss. Using our proposed auxiliary loss consistently improves LigUnity across almost all metrics on both datasets. For example, on DUDE, AUC improves from 81.69 to 82.57, and BEDROC from 46.01 47.58. On LIT-PCBA, early-recognition metries show noticeable gains as well. This confirms that additional loss is beneficial and strengthens the model's scoring ability. (ii). Advantage of h-MINT +Mig vs Ours). Our model further improves over LiSE such as BEDROC and EFO.01, which are widely regarded as key metrics for virtual screening. These results confirm that: proposed regression loss is effective, but our h-MINT architecture delivers additional, consistent boosts beyond what the regression loss alone can offer. Thus, the comparison with LigUnity is fair, and the observed improvements come from both components of method.
most metrics. Gains are particularly clear in early-recognition measures
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Table 10: Ablation of MSE Loss over LigUnity
AUC (%) 1
BEDROC (%) 1
EF 1
Dataset
Model
0.5%
1%
5%
LigUnity
81.69
46.01
34.44
29.07
10.26
DUDE
LigUnity+MSE
82.57
47.58
35.83
29.77
10.70
Ours
84.47
47.65
35.06
29.90
10.76
LigUnity
57.61
4.34
4.07
3.04
2.26
LIT-PCBA
LigUnity+MSE
57.68
5.64
6,50
4.22
2.14
Ours
57.77
6.21
7.01
5.20
2.18
F ADDITIONAL ANALYSIS AND EXPERIMENTS
F.1 VOCABULARY ANALYSIS
We provide vocabulary statistics LBA training in Table 11 For all the datasets, extract vocabularies solely from the training set, without introducing additional data.
Table 11: Vocabulary Statistics on LBA training set.
min_free freq
tokens (basic composite / all)
avg token size (basic composite /all)
200
41 / 52 / 93
2.56 / 5.81 4.38
100
80/ 134
2.87 6.53 / 5.05
50
74 137 211
3.11 / 7.44 5.92
20
94 / 200 294
3,28 / 8.11 / 6.56
10
112 / 400 512
3.54 / 9.55 / 8.23
We vary the minimum frequency used by OverlapBPE, yielding vocabularies of sizes [98, 136, 212, 294), Table 12 Performance improves from small vocabularies to around 200, where observe the best aggregate results, and then slightly degrades at 294. Run-to-run variation is small (std <.0006 metrics), indicating stable behavior. These trends suggest a bias-variance trade-off:: overly strict thresholds (very small vocab) underfit by missing informative fragments, whereas overly lax thresholds (very large vocab) admit rare or redundant fragments that increase sparsity and noise. moderate threshold around 200 offers the best balance between coverage and denoising.
Table 12: Vocabulary size ablation on PDBBind. Values are obtained over three runs.
Vocab size
Pearson
Spearman
RMSE
98
0.618 0.005
0.601 + 0.006
1.344 + 0.007
136
0,615 + 0.002
0.605 + 0.002
1.332 + 0.003
212
0.640 0.004
0.626 + 0.005
1.299 + 0.001
294
0.622 0.002
0.617 + 0.003
1.327 + 0.002
F.2 COMPUTATIONAL OVERHEAD
OverlapBPE duplicates certain atoms during tokenization to maintain the continuity and integrity of chemical substructures. Because of these overlapping tokens, the final number of atoms becomes roughly 1.32 times the original. Despite this, both tokenization and graph construction are highly parallelizable and can be performed fully offline in preprocessing. In practice, the overhead is negligible: OverlapBPE only takes 7 minutes to process 47.9 79 molecules using 32 CPUs for virtual screening training data.
During training and inference, the main computational cost comes from the underlying UniMol encoder. h-MINT functions as light-weight adapter, and the runtime difference compared with LigUnity is minimal: training time is x1.12 than LigUnity, and our inference time is x x1.07 than
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LigUnity. Therefore, although h-MINT introduces richer hoom-frgmmnnt representation, the parallel and offline preprocessing ensures that the runtime during the actual virtual screening pipeline remains nearly unchanged.
F.3
NOISE- ROBES-ROBSST ANALYSIS
0.65
0.66
Rearon Spoarnon Corneszinn
0.64
Spearme
0.62
0.92
0.50
GET-pe4mon
GET
GET PS poaisinn
GET-4peaman
0.58
GETspeaman
pearson
0.56
GET PS-P----- opea man
GETPS spearan
ouT pearson
0.36
auru-pennson
0.51
aun-speaman
oun--peammnn
0.54
Noise Scale (A, Trai Set Only)
Noise Scale 18 Train & "est Set)
(a) Structure Noise (Train Set)
(b) Structure Noise (System)
0.66
Sparman Correation
GET-P5 seamon
Spsammnn Comreaton
GE mppSsapaaaaann
0.65
ounopearson
oure-pparman
0:635
0.64
0.925
0.2
Musk Ratio (Train Swt Only!
Mask Ratio (lrain & Text Swt1
(c) Fragment Mask (Train Set)
(d) Fragment Mask (System)
Figure 5: Noise robustness comparison on LBA. We report results from runs. In this section, we analyze the robustness and generalization of GET, GET-PS, and our model under different noise scales on LBA. We consider two noise settings: adding noise only to the training set for simulating scenarios when training data is of low quality or is predicted, and adding noise to both training and test sets for simulating system bias or data resolution. To investigate the effect of noise in different features, we add random noise to input structures, or randomly mask token types with some ratio") The results can be found in Figure 5 In general, we can conclude that: (i) Our model is robust to structure noise even up to 5 Å. (ii) Compared with atom-level 3D structures, token types provide stronger inductive bias for binding affinity prediction, which again highlights the importance of fragment-level representations.
F.4 FRAGMENT EMBEDDING ANALYSIS H-MINT
To evaluate the representation capability of the h-MINT model for molecular fragments, first analyzed the spatial distribution of fragment embeddings using t-SNE, shown in Figure 6 The results showed that all fragments clustered into 6 distinct categories in the latent space.
Statistical analysis of fragments in each cluster, as shown in Figure / revealed that Cluster 5 exhibited significant chemical specificity. This cluster was predominantly enriched with functional groups containing lone electron pairs on N and O atoms, such as C=O, CNC (C)=0, and CNS (=O) (=0). These structures, acting as typical hydrogen bond acceptors, can form stable interactions with polar amino acids (c.g., the hydroxyl group of serine or the guanidinium group of arginine) in protein binding pockets. This ability to capture key chemical features enables h-MINT to more sensitively identify structural determinants affecting binding affinity, leading to superior performance over existing sequence- or graph-based baseline models on the binding affinity prediction and virtual screening tasks.
Since GET only uses atom representations for molecules, only compare GET-PS with ours (c)-(d).
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Clusters
300
200
100
t-SNE 2
-100
200
-300
-200
t:5NE
Figure 6: t-SNE Visualization of Fragment Embeddings.
5
h bond acceptors
2
0
1
2
4
Cluster ID
Figure 7: H-bond acceptors distribution across clusters.
F.5 ADDITIONAL EXPERIMENTS ON MOLECULAR PROPERTY PREDICTION
We include 3 property prediction tasks from MoleculeNet. The baseline follows MoleculeNet directly, which extracts ECFP features and trains XGBoost with grid search. The ECFP features are chosen from 12t-bit, 512-bit, 1024-bit and 2048-bit according to dataset. with bag-of-word features extracted from OverlapBPF: and train the same XGBoost. The results are Table 13 The significant improvements in prediction error confirm that OverlapBPE provides discriminative representations for molecules.
augment the ECFP features
Table 13: Molecular Property Prediction Benchmarks from MuleculeNet.
RMSE
ESOL |
FreeSolv J
Lipo
ECFP
1,5668
3.9498
0.8875
ECFP OverlapBPE
1.2972
3.3409
0.8270
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F.6 EXAMPLE TOKENS AND CHEMICAL INSIGHTS
By construction, OverlapBPE induces a hierarchical organization of fragments: basic tokens corre- spond to chemically primitive units (atoms, bonds, individual rings), while composite tokens capture larger patterns such as fused ring systems and side chains. In this section, we analyze how the learned fragments align with standard functional chemistry.
We provide a list of the top 100 fragments mined from the LBA training set in Fig. 8 and we are able to obtain many chemically and biologically meaningful subunits or motifs. We emphasize that this motif- procedure is fully automatic, based solely on the data distribution, and leverages no prior chemical or biological knowledge. We analyse and categorize the mined fragments into the following chemically or biologically meaningful parts.
Chemical funccional groups. A functional group is specific group of atoms or bonds within molecule that is responsible for its characteristic chemical properties and reactions. Representative functional groups mined include: carboxyl, phosphate, amide (peptide bond), benzyl, secondary amino, tertiary aminoo and quaternary amino (ammonium). These functional groups are small chemical subunits that were often selected by hand in previous functional-group-De tokenization but can be easily recovered with our approach.
Biomolecule subunits. Compared to simple chemical functional groups, biomolecules such as proteins, DNA, RNA, and polysaccharides are significantly larger. Yet, the monomers that constitute these biomolecules exhibit characteristic patterns, such as amino acids, nucleotides, and saccharides. Indeed, we observe a considerable number of biologically meaningful fragments in our codebook: peptide bond, adenine, pyranose, deoxyribose, and, most notably, the whole nucleotide adenosine monophosphate, which consists of an adenine, a ribose, and a phosphate. To the best our knowledge, none of the existing fragmentation approaches has been able to mine such large subunits while preserving their biological significance.
Drug subunits. Remarkably, we also observed that our approach could mine large chemical subunits that are common motifs in small-molecule drugs. These include: adamaniane, 10- carbon tricyclic motif with highly symmetric fused four hexane rings, occurring in some antiviral drugs; *sulfonamide* in the antibacterial drug sulfanilamide; and most notably, the 28-heavy atom *sulfonamide protease inhibitor motif in HIV drugs. Indeed, our approach automatically mined this large subunit, demonstrating its effectiveness at capturing the biochemical roles of many motifs.
G THE USE OF LARGE LANGUAGE MODELS
We employ large language models exclusively for language editing, which is limited to polishing text to improve readability. No language models contributed to the development of research ideas, analysis, models, interpretation of results.
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OH H
CDC 635
CC0C 3071
Oxternerh 2001
NC-2: 2019
BaMMo 1887
DCECC 14M
CNC-D: 1A12
DCI: 3283
Aledo 1on
DETCCOTL
EOOCOC 778
D-C0
OCTEOOOC OCIC00CC1
HO
OH
HO
0400
quicc
2Q.00:
OItY RJ
DOCIOCSOECIA
COOCO
HO
000-0P0 an
s00ap3: 233
ccccccore 218
OEGEE X9
Detrcbrreedd net oOcKIMECC
ccolesceci 107
occoletrecl 124
Deco
cocOCCOcCC 176
DCCCOCCCL 170
optkt TM
EECOOIE 363
H
Keleenel 157
EOOOCCOOOC (41 wottoaaaac
Cetemntt
C-0C:
Figure 8: Top- composite tokens from LAB vocabulary.
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Reviews:
[{"review_id": "UKA3hB1gOp", "reviewer": "Reviewer_vwks", "summary": "This paper proposes OverlapBPE, an overlapping, chemically consistent fragment tokenization that preserves aromatic systems, chirality, and ionic states, and h-MINT, a hierarchical, SE(3)-equivariant interaction network that couples atom–fragment representations via bilevel attention and token-expanded geometric edges. On PDBBind and LBA, h-MINT improves binding-affinity prediction over baselines; on DUD-E/LIT-PCBA it yields better zero-shot virtual screening metrics.", "questions": "1. I would appreciate it if the authors could share results on benchmarks such as JACS/Merck for affinity ranking and DEKOIS for virtual screening. These datasets are commonly used in related works like LigUnity and could help clarify the model’s generalization performance.\n\n2. In Table 3, LigUnity performs substantially better than DrugCLIP on virtual screening metrics. I would be interested in the authors’ view on the role of the ranking loss in virtual screening. The original LigUnity paper reported that this term had little to no effect on screening performance. Have the authors observed different behavior in h-MINT or performed ablations to assess its impact?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The proposed OverlapBPE tokenization effectively preserves crucial chemical information.\n\n2. The h-MINT architecture enables many-to-many atom–fragment interactions via bilevel attention and equivariant message passing.", "weaknesses": "1. The tokenizer and architecture are co-developed, but their effects are not separated. Baselines isolating each component are needed to attribute gains.\n\n2. The many-to-many mapping between atoms and fragments results in more computationally expensive graph construction. The effect of added overhead is acknowledged but not quantitatively discussed. Since the proposed h-MINT model targets the same virtual screening setting as LigUnity and DrugCLIP, a runtime comparison would be highly informative.\n\n3. Virtual screening results are relatively weak, showing only marginal improvements and limited evidence of practical benefit.\n\n4. The comparison with LigUnity seems not entirely fair, since h-MINT adds an extra MSE loss and no ablation is provided.\n\n5. The claim in Section 5 about false-positive fragments is important but underexplored. There is no discussion on how such fragments might affect virtual screening results.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Iu0HjAPdax", "reviewer": "Reviewer_EmZ6", "summary": "The paper proposes an approach for tokenization of molecular graphs by considering overlapping tokens. It starts with frequent basic subgraphs treated as words in  an initial vocabulary. Adjacent subgraphs are merged to  form new tokens,  ovelapping words are allowed in the context. New tokens' frequency is calculated across the database and most frequent new tokens are kept. \nThe tokenized graphs are transformed into new graphs whereas the nodes are subgraphs and edge are overlapping information. The authors use an equivariant  GNN to transform the given subgraphs for predicting binding affinity with target. The experimental results carried out on two standard benchmarks and comparison was done to compare the proposed approaches with baselines regarding equivariant  GNN used for neural potential learning and other variation of all atoms GNN. The proposed approach outperformed the given approaches with a significant margin.", "questions": "Could you please compare your approach to simple baselines: taking ESM-2 as representation of protein, MorganFingerprint , ERP and Avalon as combined representation of ligands, run HPO in lightgbm models to find best set of hyperparameters of lightGBM and report the results of that baseline approaches in your paper?\nIf the experiments show significance results w.r.t to that baseline I am happy to raise my score.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The idea is interesting, the results look significant compared to related baselines.", "weaknesses": "I have a concern on the chosen baselines. It is well known that  representation learning approaches is behind  molecular fingerprint on molecular property prediction tasks. Molecular fingerprint such as Morgan Fingerprints, ERP, Avalon available in the RDKit tools can summary important substructure and functional groups in a small molecule and being better or comparative to other molecular representation learning approaches (see the leaderboard for ADMET drug property prediction by TDC).\n\nThe idea provided in this paper is not new if considering molecular fingerprint where overlappping subgraphs or functional graphs are considered as a fingerprint of the molecules. The new part may concern the preserve of the connectivity between substructure in the graphs but it is unclear whether that information is useful. I would suggest the authors to perform additional experiments whereas fingerprints are used as feature representation of ligands and ESMas feature representation of proteins, then use simple approach such as lightgbm on the top of the provided features to predict binding affinity. That would be a strong confirmation of the significance of the proposed methods compared to existing fingerprint based approaches.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "BZ5ypWUxtt", "reviewer": "Reviewer_gELf", "summary": "This paper introduces a novel framework for modeling protein-ligand interactions, for the purpose of improving performance on drug discovery tasks like binding affinity prediction and virtual screening. The authors identify that existing molecular representation methods, particularly those based on fragmenting molecules into disjoint sets, often fail to preserve crucial chemical information such as aromaticity, chirality, and ionic states.  A new molecular tokenization algorithm is proposed. Unlike prior methods, OverlapBPE allows fragments to overlap, better preserving the local chemical context and molecular substructures. The authors also developed a hierarchical Molecular Interaction Network for interaction prediction. Extensive experiments are conducted on several benchmarks.", "questions": "See Weaknesses", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The idea of OverlapBPE is novel and well-motivated. It effectively addresses a clear limitation in prior work.\n\n2. The experiment is extensive. The authors evaluate their method on four different datasets.\n\n3. The paper is very well-written and easy to follow.", "weaknesses": "1. The OverlapBPE is primarily for molecular representation. How does it perform on molecular tasks? It seems to me that it is more naturel to test OverlapBPE on molecular tasks than the ligand-protein interaction task. \n\n2. The novelty of h-MINT is limited. The core architecture of the h-MINT model is heavily built upon existing equivariant graph transformers. The paper states that h-MINT follows GET's architecture. What are the precise architectural changes made for handling the overlapping tokens? Are the underlying equivariant transformer blocks identical to those in GET?\n\n3. There is no study of the characteristics of the fragment vocabulary, such as the size and statistical information, the hierarchical structure, and the correspondence to common functional groups; thus, although the motivation is good, little chemical insight is presented. \n\n4. What is the training corpus to compute the token frequency? Does it affect the vocabulary?\n\n5. The contributions of the tokenization and the h-mint model are convoluted. There is no ablation to show the effect of the tokenization clearly. \n\n6. In the experiments of VS, DrugCLIP and LigUnity are re-trained with PDBBind. Why do they have such a big difference in performance? The only difference is the ranking loss. The ranking loss in LigUnity needs samples of the same protein and different ligands, which is not common in PDBBind. How many such samples are there in PDBBind? \n\n7. No code available. The reproducibility statement is weak.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "AuCKatdWKR", "reviewer": "Reviewer_e8QX", "summary": "The authors propose a method for biomolecular interaction modelling (protein-ligand binding, specifically). This method has two novel components: 1) OverlapBPE tokenization, which is a novel tokenization technique building upon BPE, where different tokens may overlap each other, capturing more complex molecular substructures, like rings and their functionalizations; 2) h-MINT, a hierarchical SE(3)-equivariant graph neural network built to exploit both the 3D information about protein-ligand pockets and the fragment information derived from OverlapBPE.\n\nThe results show that the method improves with regards to relevant baselines", "questions": "None", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The OverlapBPE tokenization is an elegant way of capturing molecular substructures, while preserving functional units.\n2. The h-MINT, although not particularly novel on its own (as it is a hierarchical GNN), is a reasonable choice for exploiting the information from the protein-ligand binding poses and the fragment information derived from OverlapBPE.\n3. The results show the advantage of the method.\n4. The evaluation describes the deviation between multiple experiments which provides a necessary idea of the significance of the differences with regards to other methods.", "weaknesses": "1. The choice of evaluation benchmarks might lead to undiagnosed overfitting, as the controls for data leakage focus on the protein targets, rather than the molecular ligands. This has been shown to be problematic in some cases. As this problem occurs both on the method and the baselines is not a critical problem, but it is a limitation that should be acknowledged.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)