Drugging the Undruggable: Benchmarking and Modeling Fragment-Based Screening
Authors: Haichuan Tan
Bowen Gao
Jiaxin Li
Yinjun Jia
Wenyu Zhu
Wenxuan Xie
Yihong Liu
Yanwen Huang
Jianhui Wang
Yuanhuan Mo
Ya-Qin Zhang
Wei-Ying Ma
Yanyan Lan
Venue: ICLR 2026 Poster (2026)
Keywords: Drug Discovery
Representation Learning
Virtual Screening
fragment-based drug design
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: A significant portion of disease-relevant proteins remain undruggable due to shallow, flexible, or otherwise ill-defined binding pockets that hinder conventional molecule screening. Fragment-based drug discovery (FBDD) offers a promising alternative, as small, low-complexity fragments can flexibly engage shallow, transient, or cryptic binding pockets that are often inaccessible to conventional drug-like molecules. However, fragment screening remains difficult due to weak binding signals, limited experimental throughput, and a lack of computational tools tailored for this setting. In this work, we introduce FragBench, the first benchmark for fragment-level virtual screening on undruggable targets. We construct a high-quality dataset through multi-agent LLM–human collaboration and interaction-based fragment labeling. To address the core modeling challenge, we propose a novel tri-modal contrastive learning framework FragCLIP that jointly encodes fragments, full molecules, and protein pockets. Our method significantly outperforms baselines like docking software and other ML based methods. Moreover, we demonstrate that retrieved fragments can be effectively expanded or linked into larger compounds with improved predicted binding affinity, supporting their utility as viable starting points for drug design.

Published as a conference paper at ICLR 2026
DRUGGING THE UNDRUGGABLE: BENCHMARKING AND MODELING FRAGMENT- CRAGMENT-BASED SCREENING
Haichuan T2n Bowen Gao Jiaxin Li,, Yinjun Jia', Wenyu Zhu', Wenxuan Xie Yihong Liu?, Yanwen Huang'. Jianhui Wang', Yuanhuan Mo Ya-Qin Zhang', Wei-Ying Ma', Yanyan Lan 1,7f
Institute for AI Industry Research (AIR), Tsinghua University Department of Computer Science and Technology. Tsinghua University
BBejjnng University of Posts and Telecommunications
"Fudan University
Department of Pharmaceutical Science, Peking University
"South China University of Technology
"Beijing Frontier Research Center for Biological Structure, Tsinghua University
ABSTRACT
A significant portion of discase-relevant proteins remain undruggable due to shal- low, flexible, or otherwise ill-defined binding pockets that hinder conventional molecule screening. Fragment- based drug discovery (FBDD) offers a promising alternative, as small, low-complexity fragments can flexibly engage shallow, tran- sient, or cryptic binding pockets that are often inaccessible conventional drug- like molecules. However, fragment screening remains difficult due to weak bind- ing signals, limited experimental throughput, and a lack of computational tools tai- lored for this setting. this work, we introduce FragBench, the first benchmark for fragment-level virtual screening undruggable targets. We construct high- quality dataset through multi-agent LL-M-human collaboration and interaction- based fragment labeling. To address the core modeling challenge, we propose a novel tri-modal contrastive learning framework FragCLIP that jointly encodes fragments, full molecules, and protein pockets. Our method significantly outper- forms baselines like docking software and other ML based methods. Moreover, we demonstrate that retrieved fragments can be effectively expanded or linked into larger compounds with improved predicted binding affinity, supporting their utility as viable starting points for drug design.
INTRODUCTION
A substantial fraction of disease-associaas proteins are considered undruggable targets, such as transcription factors (Bushweller] (2019) and protein-protein interaction (PPI) hubs (Arkin Wells) 2004). These proteins are closely linked to severe diseases, including cancer and neurodegenerative disorders (Bushweller) 2019 Ross & Poirier] (004). therapeutic strategies could be developed for them, the clinical and societal impact would be transformative. However, the lack of well- defined, stable binding pockets makes conventional approaches- smaleessssssall-eeeeeeee -saalllllmeee molecule drug design and high-throughput screening screening-largely ineffective, thus severely limiting progress in drug discovery for these targets.
Fragment- based drug discovery (FBDD) offers unique path forward for undruggable targets. Com- pared to drug-like molecules, fragments are smaller and more flexible in their binding modes, which enables them access shallow transient binding pockets protein surfaces (Erlanson et al. 2016) and to reveal weak but crucial interactions often invisible to conventional screening. AT- though individual fragments bind weakly, they can serve as anchors that can be expanded or linked
"qqual contribution
Correspondence 1anyanyan@ai sainghua..ede edu.cn

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DUB-F
Glide Scrsening
Freg-1 4300
Frag-2 300 pr
Compauns Kl 36 nM
Undruggable pocket
Progments detected by FBbb (PD8 ID: 1956
Mol of frogment linking and oprimization (Po8 ID: IYST)
(Ac-xt, PoB TD: TY5T)
1.2
30
300
04
Var2Te-48 18-028
1e-00 1S-
7.70 1:-0
cottcct
0-33.00000 b-0.28
Trighanch
SEXOES
DuD-6
PCRA
Figure 1: (a) Fragment-based drug discovery on BCL-xL, a shallow-pocket undruggable target where conventional screening fails. Weakly binding fragments identified by NMR were linked to yield compound-1 with markedly improved affinity. (b) Glide (Halgren et al. (004) shows poor enrichment on such targets (EF1 1.8), barely above random. (c) Pocket property distributions of FragBench (green) versus DUD-E (purple), with significance tested by Mann-Whitny U and effect sizes reported as Cliff's 8.
to yield high-affinity, selective molecules (Murray & Rees, 2009). This principle has been validated in classical undruggable targets, highlighting the promise of FBDD this space (Scott al. 2016).
Over 85% of the human proteome remains "undruggable" due to the absence of well-defined pock-
ets suitable for conventional mmlll-moeecule targeting (Spradlin al.) (02I). Fragment-based drug discovery (FBDD) offers promising alternative, exemplified by pipelines like Enamine's V-
SYNTHES, which dock fragment-like synthons and expand them through iterative synthesis (Sady- bekov et al.) 2022). Fig illustrates BCI-xXL, a classical undruggable target, as example of developing a full-molccule ligand through FBDD. However, the initial fragment identification step remains bottleneck. Experimental techniques such as NMR and crystallography reliably detect weak binders but are slow, costly, and limited by factors like solubility or crystal quality (Erlanson et 2016 et al.) [207]. Computational docking, originally designed for full-sized ligands, of- ten underestimates small fragments, leading to high false positive and negative rates (Brenke et al.) 2009 Chen & Pohlhaus 2010). As shown in Figure (b). our experiments confirm these limita- tions doos-nnocigggoo achieves only marginally better fragment ranking than random. These challenges highlight the need for fragment-awat computational models tailored FBDD and undruggable targets.
Despite the rapid advances of machine learning in drug discovery, Al-driven approaches for frag- ment screeming remain largely unexplored. Two major gaps currently hinder progress in this area. First, there lack systematic benchmarks specifically designed for fragment screening on un- druggable protein pockets. This absence limits standardized evaluation and meaningful comparison between methods. Second, existing modeling frameworks fail to capture the tripartite relationship among fragments, drug-like molecules, and protein pockets. Without explicitly modeling both fine- grained fragment-pocke interactions and global molecule-pocket binding principles, is difficult to achieve generalizable fragment retrieval across diverse targets.
To bridge the current gaps in fragment-base drug discovery, we introduce FragBench, the first large-scale benchmark specifically curated for fragment screening undruggable targets. It features high-confidence positive and negative fragment paaneettpoccket pocket pairs derived from structurally challenging protein complexes. Building upon this, we propose FragCLIP, tri-modal contrastive learning
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framework that jointly encodes fragments, molecules, and protein pockets, aligning their represen- tations through dedicated fusion module. This design captures both fine- mne-grained fragment-pocke interactions and scaffold-level molecular context. Our experiments show that FRAGCLIP signif- icantly outperforms classical docking tools and recent learning-basn based models especilly in the challenging cross- cross-target setting highlightnng its potential to advance AI-driven fragment discovery for undruggable proteins.
2 RELATED WORK
Recent advances in deep learning have markedly improved structure-based virtual screening (VS). Models such as EquiBind and DiffDock (Stärk al. 2022, Corso et al.] (002)) enable fast and accurate prediction of protein-ligand binding conformations, while multimodal frameworks like DrugCLIP (Gao et al. 2023) employ contrastive learning to align protein and ligand representa- tions for efficient screening. Meanwhile, standardized benchmarks such DUD-E, LIT-PCBA, and 2r0s (Mysinger et al., 2012, Tran-Nguyen et al., 2020 Francocur et al., 2020) have facilitated robust evaluation of virtual screening models. However, these efforts overwhelmingly fo- cus on drug-like molecules and well-structured binding pockets, overlooking the unique challenges of fragment-base baseed screening- where where small, low-affinity fragments interact with shallow transient sites that are typical of undruggable targets.
Fragment- based drug discovery (FBDD) offers compelling strategy, fragments can access cryp- tic sites and seed ligand development. However, efficient screening remains challenging: experimen- tal methods like NMR and crystallography are accurate but low-throughput, while computational tools such as hotspot mapping and fragment docking often misrank fragments due to scoring biases. Despite progress in virtual screening (VS), no existing benehmark or framework systematically addresses fragment screening on undruggable targets or captures the interplay among fragments, molecules, and pockets.
METHOD
Fragment- drug discovery (FBDD) holds promise for targeting undruggable proteins with shal- low cryptic pockets, where traditional small-molecule screening fails. However, the core task of fragment-level virtual screening remains underdefined and challenging: fragment-pocket binding signals are weak, supervision is scarce, and existing scoring retrieval myehhols-tppccally opti- mized for drug-like figands-fail to generalize to low-mass fragments.
To address this, formalize the task of fragment retrieval in undruggable pockets, and introduce tri-modal modeling approach that leverages information from protein pockets, full drug molecules, and their constituent fragments. Our solution includes (i) FragBench, a new benchmark built with LLM-guided literature mining and interaction- fragment labeling; and (ii) FragCLIP, a con- trastive framework that aligns fragment, molecule, and pocket embeddings via multi-level supervi- sion.
3.1
FRAGBENCH: FRAGMENT-BASE BASED BENCHMARK FOR UNDRUGGABLE TARGETS
3.1.1 TASK DEFINITION
We study fragment-level virtual screening on challenging protein targets. Given a protein pocket p E P typically from an undruggable protein-aan -nd fragment library F {.f1, f2. fv)) the goal is to identify a subset F+ C F of fragments that can form favorable non-covalent interactions with p. Each fragment fa a chemically valid substructure derived from drug-like molecule via synthetically accessible disconnections (c.g., BRICS rules). Compared to conventional ligands, fragments have lower molecular weight, surface area, and fewer functional groups, resulting in weak and localized binding. Yet this simplicity allows them access shallow, flexible, or cryptic sites-preete precisely those found in undruggable pockets.
This setting presents unique challenges: binding signals are subtle, and standard screening methods optimized for full ligands often fail to prioritize fragments. Effective fragment retrieval therefore demands both dedicated benchmarks and tailored models.
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ChEMELL
Soore
EPDB
f Actives
bocking
Prognashatice
Ligard efficency -015
Ligend- pocket par
PLIP
Other Filters
Undruggable
(lydrage bonds, Hy Hydhopphsbre cortecti, i-ttaciing.....
Rule besed Pre curation
Hammnn collabonative Curation
Docking
Fragmentation
DRUGANK
1000
PublMed
Volddtted Undruggetle Torgets
B Teak spesifff Prompss
Find Frompt
Name
E
0000 0 Tral-spacife" trowledge
{
Hamen Export
Drug Information
Restted Papers
Astunated Chasiiicatioon
Uiterftree Retrieval Agest
Eridnce Extraction Agent
Desision Sytthes' Agent
Overview of the data curation pipeline for FragBench. Starting from pairs the Protein Data Bank (PDB), we apply rule-based filters (e.g., site score, ligand efficiency) followed by a collaborative agent-human curation process to select high-quality protein-ligand com- plexes. For challenging targets from ChEMBL, docking and PLIP-bascd interaction analysis are used to annotate fragment-level contacts. Valid fragments are extracted based interaction pat- terns (e.g., hydrogen bonding, hydrophobic contacts). To identify undruggable targets, we further interature-grounnt reasoning pipeline that retrieves UniProt entries, mines PubMed/Druggaan evidence via LLM agents, and synthesizes task-specific knowledge. Human experts verify final de- cisions.
Figure 2:
We construct benchmark comprising undruggable targets, where each pocket p is paired with a fragment set F F+ U F-, containing known binders F+ from experimental protein-ligand complexes and presumed non-binders F-, Given scoring model F - E that ranks fragments for a fixed pocket p, we evaluate its ability to prioritize true binders using early recognition metries: the Enrichment Factor (EF@k) and BEDROC (Truchon & Bayly 2007).
EF@K l measures fold enrichment over random selection: # positives in top-k
HF@k
|P| IF+
h
BEDROC emphasizes early retrieval in the full ranking:
BEDROC = e Σ
-
e
where R -2=1 |F is the total number of fragments and r; is the rank of the i-th positive.
3.1.2 RULE-BASED PRE-CURATION
Although an estimated 85% of the human proteome is considered undruggable, structural data the Protein Data Bank (PDB) (Berman et al. 2000) is heavily skewed toward druggable proteins with well-formed pockets. Moreover, no existing database systematically catalogs undruggable targets. To bridge this gap, developed a rule-based pipeline to extract challenging pocket-ligand pairs from PDB by combining structural heuristics with ligand efficiency filters.
We began with all pigaed complexes in PDB and excluded trivial cases involving covalent ligands, nucleic acid proximity (within 6Å), or small pockets (fewer than 10 residues), yielding 87,425 pairs. We then assessed each pocket using SiteMay (Halgren 2009). retaining those with a site score below i.8iindicatvee of small or poorly enclosed binding sites.
In parallel, computed ligand efficiency (LE) docking score per heavy atom:
S(1)
LE(() = HA(()
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where S(1) is the Glide docking score and HA(() is the heavy atom count. Pairs with LE(() > -0.15 were prioritized, reflecting weak binding normalized by size.
The final candidate set is:
C = (p.1) E DrDB| SiteScore(p) < 0.8 A LE(() > -0.15),
resulting in 1,387 structurally challenging pocket-ligand pairs for expert review.
3.1.3 MULTI-AGENT FRAMEWORK FOR TARGET CURATION
To construct an evidence- grouudded benchmark undruggable targets, we introduce a modular multi-agent framework composed of retrieval, extraction, synthesis, and expert validation compo- nents. Formally, given a protein target t E T (where T is the set of UniProt-annotated human proteins), the system retrieves a corpus D, of relevant documents by querying DrugBank (Knox et al., 2024) and PubMed (White) 2020). This retrieval modeled as mapping R. + Dr where D. ( d2....: ..... dnn includes abstracts and metadata related to l's druggability, clinical development, and structural properties. Each document d, E Dr is then processed by an LLM-based extraction agent & with susc-spe prompting. producing structured tuples (e,, c.) where e; is an evidence type (e.g.. "shallow pocket", "fragment hit", "undruggable domain" and C; is a citation. The agent enforces output consistency via schema-constrained decoding and validation heuristics. A synthesis agent S aggregates extracted tuples {(ei, ci) and metadata features m. from Drug- Bank to compute a provisional classification ý, E (druggable, undruggable, FBDD-reported] with supporting citations. Conflicts ambiguous cases (e.g.. conflicting evidence of druggability and FBDD) are resolved via deterministic resolution rules or flagged for human audit. Finally, a domain expert validation step V Út Y/ confirms corrects each label Gt. producing the final ground- truth annotation Y/ used in our benchmark. This human-in-the-logg step is essential for resolving nuanced biological edge cases, such as disordered proteins with low-confidence fragment data.
Overall, this framework achieves high-throughput and structured curation of undruggable targets, with average of 218, 34, and 25 relevant PubMed documents per target for druggability, undrug- gability, and FBDD evidence respectively. The resulting benchmark provides structured evidence provenance and supports downstream model evaluation under realistic biological constraints.
3.1.4 FRAGMENT CONSTRUCTION
To support fragment-level learning, constructed dataset of fragment-pocket interactions for curated undruggable targets. Active ligands were retrieved from ChEMBL. (Gaulton et al.) 2012) with strict assay-based filtering described in G and each molecule m M was decomposed into
synthetically accessible fragments using the BRICS algorithm (Degen et al. 2008 :
F(m) (f1, fe...f...... fr).
Fragments with 8 < HA(A)) < 24 were retained match common fragment library constraints (e.g., Enamine REAL (Shivanyuk al. (007)). Redundancy was reduced via fingerprint-based
clustering, details are in H
Positive fragment labels were generated by redocking each ligand into its native pocket using Glide, followed by non-covalent interaction detection with PLIP (Salentin et al. 2015). To obtain high- confidence positive examples, we adopted conservative consensus strategy: fragment was labeled as positive only it (i) formed at least two distinct non-covalent interactions with the pocket in given docking pose, and (ii) this multi-interaction pattern reproducibly observed across independent docking replicates. Formally, let A(f) denote the set of atoms belonging to fragment f, and I(a, p) be the number of non-covalent interactions formed by atom with pocket p. A fragment is considered positive if {a E A(f) I(a,p) > 0} 2 and this condition holds consistently across three docking replicates.
We quantitatively assessed the accuracy of this labeling strategy and examined how the number of docking replicates and interaction thresholds affect label reliability; details are provided in Ap- pendix B
Negatives were sampled randomly at a 1:90 cesitive-to-negatiie ratio from a fragment pool. The resulting FragBench dataset spans 54 targets, each associated with average of 84.37 positive and
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7593.33 negative fragments, providing the first standardized benchmark for fragment-level screen- ing against challenging protein pockets. Comprehensive information on 54 targets, including their protein name, Uniprot ID and associated disease indications is presented in Appendix
In Figure I we report a statistical characterization of FragBench pockets, which display reduced size and fewer residues relative to DUD-E targets, indicative of their shallow and flattened topol- ogy. Dscores further underscore the intrinsic challenges these targets pose for rational drug design, highlighting the fundamental differences between FragBench and traditional benchmarks.
3.2
FRAGCLIP: A CONTRASTIVE LEARNING FRAMEWORK FOR FRAGMENT RETRIEVAL
3.2.1 MULTI-GRANULAR CONTRASTIVE ALIGNMENT
The core task of this work is fragment retrieval: given protein pocket, the model must identify fragments likely to bind. Directly learning from fragment-pocket pairs is challenging due to the small size, weak binding affinity, and context-deeendence of fragments. Such training would provide sparse and noisy supervision.
To address this, design multi-encoder framework that jointly models protein pockets, frag- ments, and their parent molecules. The protein encoder fp maps 3D pocket structures into a la- tent space. The fragment encoder fy captures fine-grainet chemical substructures relevant bind- ing. The molecule encoder fm provides scaffold-level context, serving as a structural and chemical bridge to regularize fragment representations and stabilize training.
To align representations across these three molccular granularities, we employ a set of con- trastive objectives: (i) pocket-molecule alignment (Cp.m) preserves scaffold-level semantics, (ii) fragee-f alignment (Cp-t) provides direct supervision for fragment-pon pocket compatibility, and (ili) fragmett alignment (Lm.r) enforces internal consistency between fragments and their source molecules.
Each loss takes the following form:
1 N i=1
Σ log exp(sim(f.(cc..ffo44)/))
La-b
(1)
N
ΣS= (xp(sim(fa(a)))
where (a,b) E {(p, m). (p, (m, f)} and sim(...) denotes cosine similarity. The total loss is: Calign = Lpm + X Ap A2Cm..-
For implementation, adopt the UniMol architecture (Zhou et al. 2023). 3D molecular repre- sentation model with SE(3)-equivariant attention. We use UniMol's pocket encoder for fp. and its molecular encoder for both I' and fy. enabling unified geometrie representations across all modal- ities.
3.2.2 FUSION MECHANISM FOR FRAGMENT IMPORTANCE MODELING
A fundamental challenge in fragment retrieval is that fragment-level signals are inherently noisy (Bon et al. 2022). Many fragments within molecule contribute little to binding, while others may form spurious or centextt interactions. Relying on all fragments equally can there- fore dilute the discriminative cues needed for accurate retrieval, making it difficult for the model to identify which substructures truly drive binding.
To address this, we introduce a fusion mechanism that performs joint selection and filtering of fragment information. Given a molecule embedding fn (m) and associated fragment embeddings {fr(f))}= cross-attention module highlights fragments most relevant to binding while down- weighting less informative ones. The attention output is concatenated with the molecule embedding and passed through multilayer perceptron (MLP) to yield fused representation:
Ztusion
= MLP (m) Atm (m), (f)(t)) (
(3)
This fused embedding is trained to align with the pocket representation via contrastive loss:
1 N
(pp(sim(Sp(P))) 2gusion, )/T)
Liusion
log
(4)
N
Σラ= exp(siimmssppeeeeeeeeeeep 2tusion.))/7

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Controstive, Aligmmntt Acrods Granularities
Information Fusion Learning
Frog eris
Mol erb
ProgeWel contrnattvv
Freg Encoder
Mel Encoder
Fusion
Mel Encoder
Frep Pocket controctive
Pocket Encoder
Mal-Pocket contrantive
controctiv
Pocket Encoder
Fused emb
Virtual Screening for Malecule and Fregments
Moi Progmentation
Fusion
Cosine Sivilarity
Packet
Costre Similarity
Frog
Library
Frag
Top Molecules &
Top Frogments
Overview of the FRAGCLIP framework. (a) Contrastive Alignment Across Granular- ities: three encoders model pockets, fragments, and molecules, aligned via multi-view contrastive losses to capture both fine-grained fragment-pocket interactions and scaffold-level semantics. (b) Information Fusion Learning: molecule and fragment embeddings are fused cross-attention, enhancing fragment representation with contextual molecular information and enabling contrastive alignment with pockets. (c) Fragment and Molecule Screening: during inference, pockets are used retrieve both top-scoring molecules and fragments via cosine similarity in the shared embedding space.
Figure 3:
By emphasizing informative fragments and suppressing noise, the fusion module refines fragment embeddings more discriminative signals for retrieval, highlighting linding-relevant substruc- tures and strengthening fragment-level representation learning.
4 EXPERIMENTS
4.1 EXPERIMENT SETTINGS
Tasks and Datasets
Our primary task is fragment retrieval on FragBench, a benchmark curated undruggable targets with shallow or cryptic pockets. In addition to FragBench, also construct fragment-level trieval benchmarks for several classical virtual-screening datasets, including DUD-E, LIT-PCBA, and DEKOIS 2.0. For each dataset, we repeat docking using Glide and label fragment-pocket interactions with PLIP, designating fragments that form at least two non-covalent interactions con- sistently positives. Detailed dataset statistics and construction procedures are provided in Ap- pendix
For model training we use PDBbind. To prevent information leakage from test targets, we remove from the training pool all protein-ligany complexes whose sequence identity to any target in Frag- Bench, DUD-E, LIT-PCBA, or DEKOIS 2.0 exceeds 90%. After filtering, the training and validation sets contain 14,223 and 744 protein-ligand pairs respectively. We further analyze the impact of dif- ferent levels of sequence- sequence-homologe homology filtering on fragment retrieval performance, and the complete results provided in the Appendix.
Evaluation Metrics We report standard virtual screening metrics, focusing on early recognition evaluate models using AUC, Enrichment Factor top-k (EF@ k),
performance. Specifically, and BEDROC. All results are averaged across targets.
Baselines. We compare FragCLIP with both classical docking/scoring methods and recent Hamiing-bssed rescoring models. Glide-SP (Yang et al. (202T) and AutoDock Vina (Eberhardi et al.] 2021) serve standard docking baselines. We also include the widely-used machine learning scor-

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ing function RF-Score (Ballester & Mitchell 2010), and two high-performing structure- based scor- ing methods that explicitly model 3D geometry and residue-atom interactions, RTMScore (Shen
et al. 2022) and EquíScore (Cao al. 2023). Among learning-basee baaned baselines, DrugCLIP (Gao
et al. 2023 and LigUnity (Feng et al. 2025 align pocket-ligand pairs via contrastive representation
learning for retrieval and screening.
4.2 RESULTS ON FRAGBENCH (UNDRUGGABLE TARGETS)
We evaluate our method on the FragBench dataset, comparing against classical docking tools (Vina. Glide), machine learning models, and a recent contrastive learning baseline. As shown Table I classical docking-basee based approaches completely fail in the fragment-level virtual screening setting, with Vina producing no meaningful ranking results and Glide achieving only marginal enrichment (EF@0.5% of 1.86 and BEDROC of 0.03). DrugCLIP improves performance slightly (EF@0.5 = -.. Our proposed method, FragCLIP, achieves the highest performance across all metrics, with BEDROC of 0.12, and EF@ 0.5% of 6.85. These results underscore the importance of framework: by explicitly modeling the fragment-pocket interaction in contrastive and fragment- centrie manner, FragCLIP substantially outperforms both docking and prior learning-based methods. This demonstrates the effectiveness of our design for fragment-leves recognition.
Table 1: Performance comparison on FragBench. Results are averaged over targets.
Method
AUROC
BEDROC
EF@0.5%
EF@ 1%
EF@2%
EF@5%
Vina
0,476
0,025
1.665
1,419
1.208
1.113
Glide
0.597
0.034
1.862b.513
1.825 768
1.821o 422
1.712a.285
RFscore
0.457
0.025
1.665
1.419
1.469
1.113
RTM Scoret
0,571
0.094
1.896
1.997
1.940
1.824
EquiScore!
0.581
0.105
4.039
3.331
2.638
2.049
LigUnity
0.505
0.089
4.262
3,562
2.933
2.087
DrugCLIP (90%)
0.597
0.080 0003
4.110p.05e 056
3,203p.121
2.660
2.067
FragCLIP (90%)
0.593p.018
0.115
6.853..588
5.7970.258
4.510p.168
3.000p.161
"Subscripts denote standard deviations across three independent runs. *Evaluated using the original checkpoint without homology filtering the test set.
4.3 PERFORMANCE ON OTHER FRAGMENT BENCIMARRS
To rigorously evaluate FragCLIP across varying levels of difficulty, we conducted experiments on
three fragment-version benchmarks: DUD-E, Dekois, and LIT-PCBA. For the construction of these datasets and detailed statistics, please refer to Appendix While DUD-E and Dekois represent standard benchmarks with rell-characterized targets, LIT-PCBA poses challenging dataset.
Remarkably, FragCLIP consistently achieves the best Enrichment Factor (EF) scores across all three datasets, demonstrating its superior capability in early recognition regardless of the benchmark dif- ficulty. On the standard DUD-E and Dekois datasets, FragCLIP dominates with EF@0.5% scores of 20.317 and 17.963, respectively, substantially outperforming baselines like LigUnity and Drug- CLIP. Even on the challenging LIT-PCBA dataset, FragCLIP still secures the highest enrichment performance across all thresholds (e.g., 3.437 EF@0.5% vs. 2.939 for RTMScoree. This consis- tent superiority in metrics highlights FragCI.IP's robustness and practical value in prioritizing active fragments for virtual screening tasks.
4.4 FRAGMENT-AIDER MOLECULE RETRIEVAL VIA FUSION
We explored whether informative fragment-level signals could enhance molecule-level retrieval, results shown in Table Our findings suggest that incorporating fragment supervision during train- ing improves the quality of molecular representations. Specifically, by introducing fragment-level contrastive learning but performing retrieval solely using the molecule encoder, we observed an improvement EF1 % from 31.87 to 33.56.
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Table 2: Performance comparison DUD-E, Dekois, and LIT-PCBA benchmarks (fragment sion). Results are averaged over all targets.
Method
AUROC
BEDROC EF@0.5%
EF@ 1%
EF@2%
EF@
DUD-E (fragment version)
Vina Glide
0,521
0,062
4.805
3.897
3.155
2.323
p.621p.009
0.087p.010
7.5351.458
5.7951.014
4.153
2.807
RTMScore
0,454
0.007
1.818
1.607
1.448
1.353
EquiScore
0,658
0.137
4.442
3.569
3,217
2.726
LigUnity?
0.616p.129
O) 1.194p..10
19.493
14.049
9.078
4.891
DrugCLIP (90%)
0.642
0.136p.o0 0003
12.013g.se 326
9.333o 220
6,843 137
4.320
FLagCLIP (90%)
0.761p.015
0.227 0.007
20.317 1.020
16.012a.700
11.307
6,883p,136
Dekois (fragment version)
Vina
0.546
0.053
3.619
3.022
2.770
2.330
Glide
0.630p.o10
0.065p.012
4.6941.964
4.0981.18
3.297
2.5970.360
RFscore
0,530
0,038
2.790
2.276
1.952
1.544
RTMScore
0.506
0.069
2.153
1.819
1.569
1.274
EquiScoret
0.658
0.138
3.904
3.718
3.376
2.706
DrugCIIP (90%)
0.640p.o1z
0. 0.1
8.607a.170
7.5230.145
5.937o.08n
4.027o.175
FLagCLIP (90%)
0.750p.013
0.213 0.00
17.9630.761
14.710p.41t
10.907
6.773p.163
LIT-PCBA (Iragment version)
Vina
0,492
0.025
1.483
1.250
1.261
1.324
Glide
0.546
0.018
0.528
0.939
1.038
0.927
RFscore
0.456
0.020
1,366
1.269
0.876
0.865
RTMScore
0.567
0.095
2.939
2.818
2.111
1.698
EquiScoret
0.597
0.086
2.446
1.981
1.643
1.470
DrugCLIP (90%)
0,560p.on 023
0.032o.oe 003
1.8230.344
1.550
1.8170.219 2.5170.324
1.6730.225
FragCLIP (90%)
0.575p.029
0.050p.004
3.437
2.857
2.280
*Subscripts denote standard deviations three independent
*Evaluated using original checkpoint/setiins without homology filtering on the test set where applica-
ble.
Frog score: 0,2680
Active malecule for AKTI
Mal scare: -0.0699
Frognentetian
Frog score: 0.4931
Ronk%: 43,31%
Ensemble score 0,2901
Ronk%: 8.36%
Figure 4: Case of fragment-level interactions boost molecule ranking. Two informative fragments with strong pocket interactions (FragScores: 0.2680, 0.4931) were identified from active AKTI ligand poorly scored by the molecule encoder (MolScore: -0.0659, Rank%: 43.31%). Incorporating these fragments via ensemble raised the final score (Ensemble ranking to 8.36%, illustrating the value of fragment-level signals enhancing molecule retrieval.
0.2901) and improved the
Building this, we further investigated how fragment information could be integrated at infer- ence time. We combined fragment-level scores with molecule-level scores through both fusion and ensembling strategies to obtain a more fine-grained assessment. particular, we implemented multi-granularity ensemble where the final score is computed as:
Score = MolScore + FragScore + B FusionScore
with hyperparameters a = 3 = 0.8. This approach yielded a gain in performance, achieving an EF1 % of 37. 27.23 demonstrating the utility of learned fragment representations in complementing

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Undruggable (BCL-2 PDB Ib: 60OL)
Fragments retrieved by FregCLIP (frag score: 0.731, 0.619)
Linked Molecule (glide score-111966
Figure 5: Fragment linking for BCL-2 using FragCLIP and DiffLinker. Left: The undruggable binding pocket of BCI-2, characterized by smooth and shallow surface. Middle: Two high-scoring fragments retrieved by FragCLII, each occupying distinct subpockets and serving anchors. Right: A linked molecule generated by DiftLinker, maintaining favorable interactions across the pocket and achieving strong predicted binding affinity.
molecular signals. Figure@highlight representative case where a molecule initially ranked poorly by the molecule encoder significantly re-ranked due strong fragment-level evidence.
Table 3: Molecule-level virtual screening performance the DUD-E dataset.
EF
Method
AUC
BEDROC t
0.5%
1%
5%
Slide-SP
76.70
40.70
19.39
16.18
7.23
Vina
71.60
9.13
7.32
4.44
Ncosc
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
2.84
2.20
Planet
71.60
10.23
8.83
5,40
DrugCLIP
80.93
50.52
38.07
31.89
10.66
FragCLIP
84.76
53.61
40.64
33.56
11.39
(w/o Fusion)
FragCLIP
85.44
59.32
42.93
37.23
12.45
4.5 FRAGMENT LINKING ON BCL-2
Fragments from virtual screening typically require growing or linking to form drug-like molecules. To demonstrate practical utility, have performed fragment linking the undruggable target BCL-2. FragCLIP retrieved 30 candidate fragments, which were docked with Glide to identify anchor conformations. Using DiffLinker, generated complete molecules by linking fragment pairs. As shown in Fig. 5 two high-scoring fragments occupied distinct subpockets and served as effective anchors, yielding linked molecule with Glide score of -11.96. This case highlights the potential of combining FragCLIP with generative models for designing novel compounds against challenging targets.
CONCLUSION
We present FRAGBENCH, the first benchmark for fragment-level retrieval on undruggable protein targets. Our findings reveal that docking-based methods struggle with fragments, especially in shal- low or cryptic pockets. To address this, we introduce FRAGCLIIP, a cross-modal framework that learns aligned representations via multi-view contrastive pretraining. By integrating global con- text and local interactions, FRAGCLIP outperforms existing baselines across all metrics. Together, TRAGBENCIL and FRAGCLIP establish foundation for fragment-centric modeling in FBDD, ad- vancing drug discovery for targets beyond the reach of conventional methods.
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ACKNOWLEDGMENTS
This work supported by Innovative Drug Research and DevelopmeentNattonnll Science and Tech- nology Major Project (No.20252D880255) and Beijing Frontier Research Center for Biological Structure Fundings.
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A LLM USAGE STATEMENT:
GPT-4o was used exclusively for language polishing. including grammar correction and clarity en- hancement. All scientific content and analyses were conducted independently of LLMss.
B EVALUATION OF FRAGMENT-POCKET LABEL QUALITY AND THRESHOLD SELECTION
To quantify the reliability of docking-based fragment interaction labelling and to determine appro- priate interaction and consensus thresholds for defining positive fragments, we conducted evalua- tion on 500 PDBbind complexes. For each complex, fragment labels derived from the experimental co-crystal pose using PLIP were treated as the reference, while labels generated from three indepen- dent random seeds Glide docking replicates served as predictions.
Experimental design. Two factors were varied to study their impact on label accuracy:
Interaction threshold: a fragment is considered positive a given docking run it forms at least & (1,2,3} distinct non-covalent interactions with the pocket.
Replicate consensus: a fragment is considered positive overall if it is predicted positive in at least m 21, 3} of the docking runs.
This yields nine labeling configurations (k, m), covering both lenient (low h m)) and conser- vative (high and m) regimes. Precision, recall, and F1 score were computed by comparing basis.
docking-deriver labels against the reference crystal-derived labels Hcatmaps and trend plots summarizing the results are shown in Fig.
Quantitative results. Table summarizes the performance across all configurations. Overall, the results exhibit a clear and monotonic precision-recall trade-off. Increasing the replicate-consensus requirement consistently improves precision, while higher interaction thresholds further tighten the definition of a positive fragment. As expected, stricter criteria reduce recall, but they substan- tially suppress false positives. In the context of constructing positive labels for downstream learn- ing-where precision is the primary concern- contiennooiiiuuattion enforcing both multiple interactions and crosscreelicate consistency provide the most reliable supervision.
Interpretation. These analyses highlight that:
Docking-derived fragment labels align closely with rrystal-structur interaction patterns across all threshold settings, demonstrating that docking provides robust and trustworthy source weak supervision in this context.
2. Multi-replicate agreement effectively filters out ceed-dependent fluctuations and stabilizes interaction assignments.
3. Requiring multiple interactions preferentially selects structurally meaningful and well- supported contact motifs, improving label reliability even at the cost of lower recall.
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Precision by interactinn Thresholds (Primary Metric to Labelling)
Recall by Interaction and Vorie Threenolde (Seoondary Lebelling)
0.38
0.9
0,693
8/617
0.50
(numbu di frsormatio noquico
0.8
Predec
0.80
0.724
0.811
0.003
0,800
0,644
0.499
dooking replicates ag greeing)
(number dooking repiicates agr greein))
Impect of Vote Threshold Precision (Primery Matric Laballing)
Impact Interaction Threshold on Predision (Primary Matric Laboliin)
1.00
1.00
K: Interact ono required
repiicales ageonng
Preciion
Precition
0.80
0.86
0.00
0.80
0.5
0.75
cocking replicates agrening)
required)
Figure 6: Precision and recall of fragment labels under different interaction and voting thresholds.
Table 4: Label accuracy across interaction and replicate-consensus thresholds, comparing docking- derived labels crystal-based labels.
Setting
Precision
Recall
F1
k=1, m
0.934
0,692
0.795
k=1, m-2
0.895
0.847
0.870
k=1, m=1
0.865
0,940
0.901
k=2, m=3
0.894
0.573
0.698
k=2,m=
0.847
0.726
0.782
m=2,m=1
0.784
0.861
0.821
k=3,m=3
0.883
0.499
0.638
k=3, m=2
0.811
0,644
0.718
3-3,m= m=1
0.724
0.800
0.760
Based on these observations, we adopt the setting k=2 and m-3 for constructing the benchmark. Under this configuration, the labeled positive fragments achieve a precision of 89.4%, which, in the absence of large-scale experimental fragment-binding datasets, provides sufficiently reliable supervision for building a high-quality benchmark.
C ABLATION STUDIES
To assess the contribution of each architectural component in FragCLIP, we perform ablations shown in Table We evaluate major variants:
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No Fusion Module. In this setting, retain all three encoders -ookket fragment, and molecule- but remove the fusion module responsible for cross-modal interaction between the frag- ment and molecule representations. This modification isolates the effect of the fusion mechanism.
No Molecule Modality. Here we disable the molccule encoder entirely and perform contrastive learning only between pocket and fragment representations. Architecturally, this reduces FragCLIP to a two-encoder framework analogous to DrugCLIP, but trained specifically on fragment-level data. Performance decreases notably compared the full model, indicating that molecular con- text provides valuable structural and chemical cues that improve fragment discrimination beyond pocket-fragment geometry alone.
Overall, the ablation results clearly show that both the fusion module and the molecule modality play essential and complementary roles. The fusion module enables richer cross-modal reasoning, while the molecule encoder provides contextual constraints that guide fragment-level preferences. Removing either component consistently degrades performance, confirming that the full tri-modal design of FragCLIP is proper for fragment retrieval.
Table Ablation study on model architecture.
Setting
AUROC
BEDROC
EF@0.5%
EF@ 1%
EF@ 2%
FragCLIP
0.593p.018
0.1150.003
6.853
5.7970.258
4.510p. 163
No Fusion Module
0,585
0.105
6.4280.211
5.0720.391
4.1740.271
No Molecule Modality
0.584..om 002
0.106g.006
6. 6.1
5,2960.217
4.049 2.24
*Subscripts denote standard deviations across three independent runs.
D IMPACT OF SEQUENCE- FILTERING ON FRAGMENT RETRIEVAL
To evaluate how sequence similarity between training and test proteins affects fragment retrieval performance, we construct three increasingly stringent de-homogenized training sets by filtering PDBbind according protein sequence identity with respect all test targets in test dateset. We consider three levels of sqquence-homologe filtering when constructing the training set:
PDB-ID-deuuplictedd setting (Default). Only protein -igand complexes with the exact same PDB ID as any test target are removed. This corresponds to the 100% identity setting and serves as our full training set.
90% / 60% % / 30% sequence-identity-flleee setting. All training proteins whose sequence iden- tity to any test protein exceeds 90% 60% / 30% are removed.
Table 0 reports the resulting training and validation sizes under each setting.
Table 6: Training/validation set sizes under different eequence-identiee thresholds.
Identity Setting
Train Pairs
Val Pairs
Change w.r.t. Full Set
100% (Full)
17,315
919
baseline
90% Filtered
14,223
744
33,267 (17.92%)
60% Filtered
12.487
657
-5,090 (27.91 97)9%
30% Filtered
10,376
556
-7,302 (40.05%)
Overall, stricter sequence-homologe filtering leads to a clear decline in fragment retrieval perfor- mance (Table 77.. The largest drop occurs when moving from the full PDB-ID-deduplictte setting (100%) to the 90% identity threshold, indicating that removing highly homologous proteins has the strongest impact. Further tightening the threshold from 90% to 60% produces a smaller relative de- crease. Notably, when the threshold is further restricted to 30%, the magnitude of the performance drop becomes even more marginal, confirming that the sensitivity to homology reduction diminishes stricter levels.
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Taken together, these results show that FragCLIP is sensitive to homology filtering expected. The 90% sequence-identiny setting provides a practical balance between avoiding information leakage and maintaining sufficient training diversity, and is therefore adopted as primary de- homogenized evaluation condition in the main paper.
Table 7: Fragment retrieval performance of FragCLIP under different sequence-homollgy filtering
thresholds on FragBench.
Setting
AUROC
BEDROC
EF@0.5%
EF@ %
EF@2%
EF@5%
FragCLIP (100%)
0.6370.007
0.147
10.74 10.324
8.155
5.737
3.7670.077
FragCLIP (90%)
0.5930.018
0.1 p.115
6.8530.582
5.7970.258
4.510p.163
3.000p.161
FragCLIP (60%)
0.593
0.091
4.843
4.283
3.569..448
2.660 3011
FragCLIP (30%)
0.5540.008
0.079
4.337o.186
3.9770.221
3.2130.255
2.2700.063
*Subscripts denote standard deviations across three independent runs.
E CONSTRUCTION OF FRAGMENT-LEVEL BENCHMARKS FOR DUD-E, DEKOIS 2.0, AND LIT-PCBA
In addition to FragBench, we construct fragment-level retrieval benchmarks for three widely used structure-based virtual screening datasets: DUD-E, DEKOIS 2.0, and LIT-PCBA. Our goal is to provide fragment-basse counterparts these classical molecule-level benchmarks, enabling consis- tent evaluation of fragment retrieval methods across diverse protein families and pocket types.
Fragment generation and interaction labeling. For each dataset, begin from the original active and decoy molecules associated with each target. Each molecule is decomposed into syn- thetically accessible fragments using the BRICS algorithm. For every target, the protein pocket is extracted from its reference binding structure, and each fragment is docked into the pocket using interactions are then quantified using PLIP. fragment is labeled as pos- itive if it forms at least two distinct non-covalent interactions with the pocket in a docking pose and this interaction pattern is consistently reproduced across three independent docking replicates. In all three datasets, we applied the same fingerprint-basee fragment clustering procedure used in FragBench to reduce redundancy and ensure diversity among the fragment candidates. To maintain consistency with the imbalance characteristics of the original molecule-level datasets (e.g.. DUD- E's heavy active-decoy skew), we sample negatives at fixed ratio of 1:90 relative to positives for each target.
Glide. Fragment-pocee
Dataset statistics. Table reports the number of targets, positive fragments, negative fragments, and dataset-level averages for all three fragment benchmarks.
Table 8: Fragment-level dataset statistics for DUD-E, DEKOIS 2.0, and LIT-PCBA.
Dataset
Targets
Positives
Negatives
Avg Pos/Target
Avg Neg/Target
DUD-E
96
8,740
786,600
91.0
8,193.8
DEKOIS 2.0
81
6,398
575,820
79.0
7,108.9
LIT-PCBA
15
1,256
113,040
83.7
7,536.0
Total
246
20,950
1,885,500
85.2
7.664.6
These fragment-level benchmarks complement FragBench and enable comprehensive evaluation of fragment retrieval performance across both druggable and challenging targets.
F IMPLEMENTATION DETAILS
All models were trained on 4 NVIDIA A100 GPUs (80GB) using batch size of 20. We employed a linear learning rate warm-up schedule for the first 5% training steps, followed by cosine de-
cay. The protein and molecular encoders implemented SEE()-equivariant 3D convolutional
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neural networks following the Uni-Mol architecture (Zhou et al. 2023). All contrastive losses use temperature parameter = 1, and we optimize using the Adam W optimizer with weight decay of
1 x 10 Models are trained for 100 epochs.
G CHEMBL DATA FILTERING PROTOCOL
To ensure the reliability and consistency of bioactivity data used in our benchmark, applied set of rigorous filtering criteria to extract high-confidence ligand-targe interactions from ChEMBL. The selection process involved both assay-level and activity-level filters, focusing well-annotated functional assays with validated outcomes. Specifically, the following conditions were enforced:
Only entries with assays www.cooocc.ccc..cccccc -ecoreeecoreeeeeeeeeeeeoooooeeee _ccore 9, indicating direct target assignment, and assays assay-type being either 'B' (bind- ing) or "F' (functional) were retained.
Assay Confidence and Type:
Activity Validity: We retained entries where activities data
was either NULL or Manually validated' ensuring manual curation.
Quantitative Bioactivity Measurements: We included entries with either valid combi- standard.value, and standard.units-wwe
nation of standard.relat standard.units E {pM, nM, μM} and standard.value E [0.001, 1,000,0000 textual activity comment indicative of biological activity. The accepted comments include: 'active', 'weak activity', 'slightly active', 'slight inhibition', 'potent inhibitor', 'partially active', 'partial antagonist', 'partial agonist', "non-competitive 'no significant activity', 'no effect', 'no activity', 'no action', 'inverse agonist', 'rreversible antagonist', 'inhibition not detected', 'inactive', 'dose-dependent effect', 'antagonist', 'agonist', 'activator', as well as comments beginning with 'not active' or 'no inhibit'.
antagonist', 'no significant effect',
Standard Types: Only the following potency-related measurement types were included:
Standard_Type (IC50; XC50, ECoo. AC5o. K2, Ka, FD50)
Relation Operators: To ensure comparability, we kept entries where:
Standard Relation {=, >,
H FRAGMENT CLUSTERING PROTOCOL
To ensure chemical diversity and reduce redundancy in the fragment pool, we performed cluster- ing based on molecular similarity. Fragments were first filtered ensure chemical validity-onie molecules with valid SMILES, successful sanitization, and an atom count between and 24 were retained.
For each valid fragment, computed feature-based circular fingerprints (FCFP6) using RDKit with a radius of and 4096 bits. Pairwise Tanimoto similarities were used to construct a distance matrix, followed by hierarchical agglomerative clustering with Ward linkage. A distance thresh- old of 0.9 was applied to define cluster membership.
From each resulting cluster, representative fragments sampled to maintain diversity across the dataset. This clustering process was applied in parallel across all fragment sets using Python's mult multiprocessing utilities for scalability.
I FRAGBENCH TARGETS DETAILS
Table 9 presents the detailed information for the targets included in the FragBench dataset. For each entry, we provide the UniProt ID, protein name, functional classification, and the reference PDB structure. Structural properties include the SiteScore and AA Num, which denotes the number of
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amino acid residues located within the 6Å binding pocket. Additionally, the table lists associated diseases to highlight the clinical relevance of each target.
Table 9: Targets and associated diseases formatted dual-row style
Uniprot
Protein Name
Class
SiteScore
AA Num
PDB
PO0749
Urokinase-type
Enzyme
0.799
23
Igi8
plasminogen activa- tor
Diseases: breast cancer, colorectal cancer, lung cancer, pancreatic cancer, rheumatoid arthritis, pulmonary fibrosis, neurodegenerative diseases
P62993
Growth factor sceptor-bound
Signaling / regulatory
0.557
15
1x0n
protein 2
Diseases: cervical cancer, colorectal cancer, chronic myeloid leukemia, hepatocellukar carcinoma, prostate
cancer (anti-and andrognnd resistance)
PO0441
Superoxide dismu-
Enzyme
0.372
10
2wz6
tase [Cu-Zn]
Diseases: amyotrophic lateral sclerosis (ALS), Parkinson's 's discase, cardiovascular diseases, COVID-19,
ischemic stroke
P17931
Galectin-3
Other
0.463
21
4bms
Diseases: NASH-related hepatic fibrosis, idiopathic pulmonary fihrosis, pancreatic ductal adenocarcinoma, IgA nephropathy, insulin resistance
P14735
Insulin-degrading
Enzyme
0.783
19
4gs8
enzyme
Diseases: Type 2 diabetes mellitus, Alzheimer's disease, Cancer (various types). Neurodegeneratiie disor- ders involving amyloid-3 aggregation, Nonalcoholic fatty liver disease
P19491
Glutamate receptor
Receptor
0.428
18
4u23
2
Diseases: Nicotine addiction, Alcohol use disorder, Schizophrenia and related psychoses, Alzheimers dis- ease, Amyotrophic lateral sclerosis
4zg9 idiopathic pulmonary fibrosis (IPF), systemic sclerosis (SSc), cancer (c.g., breast cancer, lung adenocarcinoma), cholestatic pruritus, cardiovascular and metabolic diseases
Q13822
Autotaxin
Enzyme
0.790
14
Diseases:
P47929
Galectin-7
Other
0.557
15
5h9q
Diseases: cancer (multiple types), SJS/TEN, preeclampsia, psoriasis, asthma
QQUIF8
Bromodomain ad-
Transcription epigenetic
0.787
16
5or9
jacent to zinc finger domain protein 2B
Diseases: orthodontic-related gingival overgrowth
P22734
Catechol O-
Enzyme
0.659
11
5pa0
methyltransfer
Diseases:
breast cancer, systemic lupus erythematosus (SLE), autoimmune diseases involving Tfh dysreg- ulation, allergic rhinitis, narcolepsy type
Q14145
Kelch-like ECH-
Signaling regulatory 0.762
12
5wiy
associated protein
Diseases: non-small cell lung cancer, KRAS-driven lung adenocarcinoma, neurodegenerative diseases, non- alcoholic fatty liver disease, sepsis-associated ferroptosis
P41 182
B-cell lymphoma Transcription / epigenetic protein
0.547
12
6c31
Diseases: breast cancer, autoimmune diseases, systemic lupus erythematosus. narcolepsy type 1, allergic rhinitis
Continued page...
18

---

Published as a conference paper at ICLR 2026
Uniprot
Protein Name
Class
SiteScore
AA Num
PDB
Q9NUW8
TyrosyI-DNA phos-
Enzyme
0.792
20
6w4r
phodiesterase
Diseases: non-small cell lung cancer, colorectal cancer, pancreatic ductal adenocarcinoma,, lung adenocar-
cinoma, low-grade epilepsy-associates developmental tumors
POI 116
GTPase KRas
Signaling / regulatory
0.758
20
7acf
Diseases: Non-small cell lung cancer, colorectal cancer, pancreatic ductal adenocarcinoma, lung adenocar- cinoma, adenomatoid odontogenic tumor
015151
Protein Mdm4
Signaling / regulatory
0.726
20
7c3y
Diseases: hepatocellular carcinoma, renal cell carcinoma, colon cancer, breast cancer (including metas-
tases), pulmonary fibrosis
Q92793
CREB-bindiggg pro- tein
Transcription / epigenetic
0.351
16
8fup
Discases:
colorectal cancer, pancreatic ductal adenocarcinoma, chronic myeloid leukemia, neurodegenera- tive disorders, inflammatory diseases
P16581
E-selectin
Receptor
0.481
20
8r5m
Diseases: acute mycloid leukemia, prostate cancer, pancreatic cancer, atherosclerosis, venous thrombosis
PO2879
Ricin
Other
0.750
13
8t9v
Diseases:
ricin intoxication/poisoning
PO2769
Albumin
Transport / carrier
0.455
13
8wdd
pancreatic carcinoma, gastric cancer with peritoneal metastasis, liver failure with systemic in- flammation, inflammatory bowel discase, acute ischemic stroke
Diseases:
PO2766
Transthyretin
Transport / carrier
0.497
14
leta
Diseases: Transthyretin amyloid cardiomyopathy. Hereditary transthyretin amyloidosis with polyneuropa- thy, pewddiiarrrmmiie--peeeoyye ATTR amyloidosis, Wild-type (senile systemic) transthyretin amyloidosis
P60568
Interleukin-2
Other
0.684
17
1py2
Diseases: multiple sclerosis, transplant rejection, high-risk neuroblastoma, metastatic melanoma, autoim- mune hepatitis, autoimmune diseases (general), lymphoma, advancedd/eetastatic renal cell carcinoma
PO8254
Stromelysin-1
Enzyme
0.752
16
lusn
Discases:
cancer (general, including mammary tumor models), metastatic melanoma, lung cancer, gastric and gastric carcinogenesis associated with Helicohacter pylori infection
Q92731
Estrogen receptor
Receptor
0.789
14
21sz
beta
Discases: breast cancer, triple-negative breast cancer, prostate cancer, lung cancer, glioblastoma, colorectal cancer
P56524
Histone deacetylase
Enzyme
0.799
17
2vqv
Diseases:
pulmonary arterial hypertension, myocardial infarction, cardiac fibrosis and cardiovascular dis- ease, ischemic stroke, diabetic nephropathy
P10275
Androgen receptor Receptor
0.657
16
2ylo
prostate cancer. metastatic castration-resistant prostate cancer, triple-negative breast cancer, an- drogenetic alopecia, hirsutism
Diseases:
P42574
Caspase-3
Enzyme
0.629
13
3dek
Diseases:
Spinal cord injury,
diaphragm atrophy, Cancer (various types),
Ischemiarreperfusion injury, Age-related macular degeneration
Q00987
E3 ubiquitin-
Enzyme
0.789
25
3lbk
protein ligase
Mdm2
Diseases: osteosarcoma, chronic myeloid leukemia, glioblastoma, melanoma, breast cancer
PO8235
Mineralocorticoid receptor
Receptor
0.690
15
3vhv
Continued on next page...
19

---

Published as a conference paper at ICLR 2026
Uniprot
Protein Name
Class
SiteScore
AA Num
PDB
Diseases:
hypertension (including resistant hypertension), heart failure, diabetic kidney disease, chronic kidney disease, primary aldosteronism
PO0747
Plasminogen
Coagulation factor
0.628
13
4cik
Diseases:
fibrotic renal disease, thrombotic/ibrinolyti disorders (VTE, pulmonary embolism, myocardial infarction, ischemic stroke), cancers, Alzheimers disease, submacular hemorrhage
P14740
Dipeptidyl pepti-
Enzyme
0.786
26
4ffw
dase 4
Diseases: type? 2 diabetes mellitus, obesity, autoimmune diabetes, bullous pemphigoid, colorectal cancer
P06802
Ectonucleotide py-
Enzyme
0.794
25
4gtz
rophosphatase/PDE
Diseases: calcium pyrophosphate deposition disease (CPPD), chondrocalcinosis. breast cancer stem cell generation
P45452 Collagenase 3
Enzyme
0.662
10
4119
Diseases: ostcoarthritis, cancer (including breast and esophageal cancer), intestinal fibrosis in Crohns dis- ease, pulmonary fibrosis, keratoconus
Q8NB16
Mixed lineage ki- nase domain-like protein
Signaling regulatory
0.726
14
4mwi
Diseases: dementia in Awwheieerssdisseeee
non wat.allooolic_fatt diseese,
amy-
otrophic lateral sclerosis, liver fibrosis, diabetic kidney disease
PO3951
Coagulation factor
Coagulation factor
0.763
29
4na8
XI
Discases: deep vein thrombosis, ischemic stroke, myocardial infarction, venous thromboembolism, car-
dioembolic stroke
Q9BY41
Histone deacetylase
Enzyme
0.748
16
4rn2
8
Diseases: breast cancer, glioma, endometriosis, peritoneal fibrosis, Schistosoma mansoni infection (schis- tosomiasis)
P08684
Cytochrome P450
Enzyme
0.555
11
5alp
3A4
Diseases: Affecting drug metabolism and drug interactions
PO8246
Neutrophil elastase Enzyme
0.774
21
5abw
Diseases: breast and lung cancer metastasis, cystic fibrosis airway inflammation, COVID-19 assoccated lung injury, atherosclerosis, Alzheimers disease with inflammatory exacerbation
014965
Aurora kinase A
Enzyme
0.773
16
5dn3
Diseases: neuroendocrine prostate cancer, breast cancer, pancreatic cancer, non-small-cell lung cancer,
acute myeloid leukemia
P21836
Acetylcholinestrrsse Enzyme
0.760
14
Seih
Diseases: Alzheimer's disease, Myasthenia gravis, Vascular dementia, Parkinson's disease (symptom-
related), Autism spectrum disorders
P39748
Flap endonuclease
Enzyme
0.785
18
5fv7
Diseases:
oral squamous cell carcinoma. breast cancer (including paclitaxel-resistant subtype). hepatocel- lular carcinoma, glioma, hepatitis B virus infection (cccDNA formation)
PO9958
Furin
Enzyme
0.362
13
5mim
Diseases:
EARS-CoV-2 infection, atherosclerosis, epilepsy, cancer/metastasis, MERS-Cor CoV infection
PO9382
Galectin-1
Other
0.643
23
5mwt
Diseases: cancer (multiple types), HIV infection, fibrotic diseases, neurodegeneratiie diseases, retinal dis-
eases (nAMD, DME, RVO)
Continued on next page...
20

---

Published as a conference paper at ICLR 2026
Uniprot
Protein Name
Class
SiteScore
AA Num
PDB
PO6276
Cholinesterase
Enzyme
0.476
12
5nn0
Diseases: Alzheimer's 's disease, Myasthenia gravis (neonatal AChR-MG), Neonatal MuSK-MG, Parkinson's discasc gait impairment, Dementia (including dementia with Lewy bodies)
075164
Lysine-specific
Enzyme
0.591
10
5var
demethylase 4AA
Diseases: non-small cell lung cancer. prostate cancer, glioma, hepatocellular carcinoma, neuropathic pain
Q95PM0 Cysteine protease
Enzyme
0.792
23
6ex8
(Fragment)
Diseases: acute myeloid leukemia (AML), MLL-fusion leukemia, Wilms tumor, breast cancer (via PPO6-proflin-1-FNN axis)
Q03111
Protein ENL
Transcription / epigenetic
0.606
12
6hpx
Discases: acute myeloid leukemia (AML), MLLL-fusion leukemia, Wilms tumor, breast cancer, kidney de-
velopmental defects associated with ENL mutations
P07550
Beta-2 adrenergic
Receptor
0.798
17
6n48
receptor
Discases: asthma, chronic obstructive pulmonary disease (COPD), melanoma, kidney diseases, smoking-
related emphysema
P10415
Apoptosis regulator
Signaling regulatory
0.721
27
6o01
Bcl-2
Diseases: dilated cardiomyopathy. triple-negative breast cancer, retinal neurodegeneration, osteosarcoma,
lumbar disc degeneration
P07339
Cathepsin D
Enzyme
0.624
19
6qbg
Diseases: breast cancer, ovarian cancer, Alzheimer's discase, triple-negative hreast cancer, non-alcoholic steatohepatitis (NASH)
Q8N884
Cyclic GMP-AMP synthase
Enzyme
0.705
13
7ftm
Discases: non-small cell lung cancer, esophageal cancer / esophageal squamous cell carcinoma, hepatocel- lular carcinoma, glioma, ovarian cancer
Q9Y657
Spindlin-1
Transcription epigenetic
0.784
15
7ocb
Discases: non-small cell lung cancer, esophageal squamous cell carcinoma, hepatocellular carcinoma, glioma, ovarian cancer
A5H660
histone deacetylase
Enzyme
0.756
16
7p2v
Diseases: peripheral T-cell lymphoma, cutancous T-cell lymphoma, multiple myeloma, colorectal cancer (MSS/pMMR), Hodgkin lymphoma
POC6X7
Replicase polypro-
Other
0.594
15
8c0g
tein lab
Diseases: SARS-CoV-2 infection (COVID-19), Human coronavirus infection
P61964
WD repeat-
Transcription epigenetic
0.748
15
8g3e
containing protein
multidrug resistant cancer, MLL fusion leukemia, measles virus infection, nonalcoholic steato- hepatitis, pulmonary hypertension
Diseases:
21


Reviews:
[{"review_id": "4NMyTiBRsv", "reviewer": "Reviewer_3seo", "summary": "This paper introduces a fragment-level virtual screening benchmark for hard, shallow pockets and a tri-modal model that embeds pockets, fragments, and full molecules into one space so candidates can be ranked by similarity for early enrichment. It motivates fragment retrieval as a way to reach cryptic or transient sites where docking often struggles and shows that fragment cues can also boost full-molecule screening.", "questions": "1. Could the authors explain clearly why covalent ligand complexes, protein–ligand complexes within 6 Å of nucleic acids, and very small pockets (for example, <10 residues) were removed during curation?\n\n2. Could the authors explain how they enforce factual accuracy in the agent–human curation loop?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The tackling problem is interesting and meaningful.\n2. The model is explained clearly.\n3. The virtual screening result seems promising", "weaknesses": "1.  Ground truth from docking poses is weak supervision. Labels come from Glide-generated poses plus PLIP-detected contacts, which can be wrong or biased by receptor preparation, protonation, tautomers, and grid settings. This can add false positives or false negatives and tie the dataset to a single docking engine rather than actual binding. \n\n2. Marking a fragment as positive when “at least one atom makes one non-covalent contact” is a loose condition. A single contact can appear or disappear across docking \"seeds\". Fragments usually need multiple complementary contacts and burial to bind stably\n\n3. The 54 “undruggable” targets lack basic context. The paper does not say what protein classes they are or which diseases they are linked to. Without this, readers cannot judge medical relevance or prioritize use cases, and it is unclear whether success on the benchmark would transfer to programs that help patients.\n\n4. Table 1 does not report standard deviations, which is a problem because early enrichment (EF), especially at very small top percentages, commonly can vary a lot across random seeds, docking seeds, and splits.\n\n5. FragCLIP follows DrugCLIP’s contrastive pocket–molecule retrieval setup and mainly adds a fragment branch plus fusion, but there is no controlled, head-to-head comparison between the two model architectures. \n\n6. Missing citations for several previous works, such as schema-constrained decoding, validation heuristics, and baseline choices.", "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": "xKtHnoZQh4", "reviewer": "Reviewer_M5Hq", "summary": "This paper introduces FragBench, a new benchmark for fragment screening against undruggable proteins, and FragCLIP, a tri-modal contrastive learning model. FragCLIP jointly encodes pockets, fragments, and parent molecules into a latent space and is trained with a contrastive learning method. This paper compared FragCLIP with docking methods (Glide, Vina) and ML methods (DrugCLIP, Onion-Net, ...) and outperforms all baseline methods.", "questions": "1. Why not include Dekois 2.0 and LIT-PCBA for benchmarking, just as many other works? Datasets like Dekois 2.0 and LIT-PCBA are standard in many recent works and would make the evaluation more comprehensive.\n2. “Positive fragments are defined as substructures of known active ligands that make contact in a docked pose“. This is the most important assumption in this paper. However, a fragment may play roles more than binding a protein in an active ligand, e.g., acting as a linker, influencing ADMET Properties, and influencing physicochemical properties. I think maybe a more detailed discussion or analysis of this assumption will improve the quality.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. Despite the limited novelty of FragCLIP (similar to DrugCLIP), the motivation of this paper is pretty good. Fragment-based drug design (FBDD) is one of the most powerful tools for tackling targets previously considered \"undruggable.\", and I think that FragCLIP has the potential to be a useful tool for searching active fragments. Hope that you can tackle the problem of linking fragments in the future.\n2. Introduce FragBench, a benchmark of curated undruggable targets with shallow or cryptic pockets.\n3. Propose FragCLIP for solving the problem of drugging undruggable targets, and significantly outperforms all baseline methods on two fragment-based benchmarks (FragBench, FragBench-DUDE). FragCLIP also outperforms all baseline methods on the DUD-E benchmark.", "weaknesses": "1. The manuscript would benefit from an analysis of the sequence similarity between training and testing proteins. Performance is often dominated by this factor, and showing how the model performs on targets with low homology to the training set is critical for demonstrating generalization. Please consider stratifying test results by sequence identity.\n2. The discussion is missing a comparison to recent high-performing methods. For example, LigUnity [1] achieves an EF 1% > 50% on DUD-E. The authors should benchmark against or, at a minimum, discuss these state-of-the-art results to properly contextualize FragCLIP's performance.\n3. The evaluation should include a comparison to important recent structure-based methods like EquiScore [2] and RTMScore [3]. This is necessary to understand how FragCLIP compares to methods that explicitly model 3D interactions and geometry.\n\n\n[1] Feng B, Liu Z, Li H, et al. Hierarchical affinity landscape navigation through learning a shared pocket-ligand space[J]. Patterns, 2025, 6(10).\n\n[2] Cao D, Chen G, Jiang J, et al. Generic protein–ligand interaction scoring by integrating physical prior knowledge and data augmentation modelling[J]. Nature Machine Intelligence, 2024, 6(6): 688-700.\n\n[3] Shen C, Zhang X, Deng Y, et al. Boosting protein–ligand binding pose prediction and virtual screening based on residue–atom distance likelihood potential and graph transformer[J]. Journal of Medicinal Chemistry, 2022, 65(15): 10691-10706.", "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": "DpxgbRC2rx", "reviewer": "Reviewer_JnXH", "summary": "The authors first filter undruggable pairs from PDB to construct the \"FragBench\" benchmark. Then build the FragCLIP system by contrastive learning.", "questions": "see weaknesses.", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Undruggable target is an important research topic.\n- The tri-modal contrastive learning is novel.", "weaknesses": "Major:\n\n-\tIn Figure 1, the author only compares FragBench with DUD-E. The authors are encouraged to compare more binding datasets.\n\n-\tSome hyper-parameters are not explained. For instance, why are the site score <0.8 and LE > -0.15 used as thresholds? \n\n-\tIn line 210, it said there are 1387 pock-ligand pairs; but in line 251, it said that there are only 54 undruggable targets. Are these numbers consistent? The 54 targets do not look like an adequate number for a benchmark.\n\n-\tIn line 251, each target has average 183 positive fragments. It’s surprising that single target can be bind with so many fragments. Can authors provide more discussions and cases here?\n\n-\tWhat is the motivation to use retrieval-based methods rather than generating the fragments SMILES directly? It would also be nice to compare the generation models.\n\n-\tAre baseline methods in Table 1, e.g., DrugCLIP, finetuned on your fragment data? Otherwise, the comparison is not fair, i.e., zero-shot vs. finetuned model. \n\n-\tData release: do authors have plans to open-source the data, which is essential for a benchmark.\n\nMinor:\n\n-\tSome reference styles are not correct. E.g., line 52 “limited by factors like protein solubility or crystal quality Erlanson et al. (2016); Jhoti et al. (2007).”, the referred papers should in brackets.\n\n-\tIn line 221, the quote marks are in wrong directions.", "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)