SAIR: Enabling Deep Learning for Protein-Ligand Interactions with a Synthetic Structural Dataset
Authors: Pablo Lemos
Zane Beckwith
Sasaank Bandi
Maarten Van Damme
Jordan Crivelli-Decker
Benjamin J. Shields
Thomas Merth
Punit K Jha
Nicola De Mitri
Tiffany Callahan
AJ Nish
Paul Abruzzo
Romelia Salomon-Ferrer
Martin Ganahl
Venue: ICLR 2026 Poster (2026)
Keywords: Protein
Ligand
Dataset
Affinity
Primary area: datasets and benchmarks
TL;DR: We introduce SAIR, the largest public dataset of protein-ligand 3D structures with activity data

Abstract: Accurate prediction of protein-ligand binding affinities remains a cornerstone problem in drug discovery. While binding affinity is inherently dictated by the 3D structure and dynamics of protein-ligand complexes, current deep learning approaches are limited by the lack of high-quality experimental structures with annotated binding affinities. To address this limitation, we introduce the Structurally Augmented IC50 Repository (SAIR), the largest publicly available dataset of protein-ligand 3D structures with associated activity data. The dataset comprises $5,244,285$ structures across $1,048,857$ unique protein-ligand systems, curated from the ChEMBL and BindingDB databases, which were then computationally folded using the Boltz-1x model. We provide a comprehensive characterization of the dataset, including distributional statistics of proteins and ligands, and evaluate the structural fidelity of the folded complexes using PoseBusters. Our analysis reveals that approximately $3 \%$ of structures exhibit physical anomalies, predominantly related to internal energy violations. As an initial demonstration, we benchmark several binding affinity prediction methods, including empirical scoring functions (Vina, Vinardo), a 3D convolutional neural network (Onionnet-2), and a graph neural network (AEV-PLIG). While machine learning-based models consistently outperform traditional scoring function methods, neither exhibit a high correlation with ground truth affinities, highlighting the need for models specifically fine-tuned to synthetic structure distributions. This work provides a foundation for developing and evaluating next-generation structure and binding-affinity prediction models and offers insights into the structural and physical underpinnings of protein-ligand interactions. 
The link to the data will be added upon publication, to preserve anonymity of the submission.

Published as a conference paper at ICLR 2026
SAIR: ENABLING DEEP LEARNING FOR PROTEIN- LIGAND INTERACTIONS WITH A SYNTHETIC STRUC- TURAL DATASET
Pablo Lemos'
Zane Beckwith
Sasaank Bandi
Maarten van Damme
Jordan Crivelli-Decker
Benjamin J. Shields'
Thomas Merth
Punit K. Jha'
Nicola De Mitri'
Tiffany J. Callahan
AJ Nish
Paul Abruzzo²
Romelia Salomon-Ferrer'
Martin Ganahl
SandboxAQ
NVIDIA
ABSTRACT
Accurate prediction of protein-ligand binding affinities remains cornerstone problem in drug discovery. While binding affinity is inherently dictated by the 3D structure and dynamics of protein-ligand complexes, current deep learning approaches are limited by the lack of high-quality experimental structures with annotated binding affinities. To address this limitation, we introduce the Struc- turally Augmented IC50 Repository (SAIR), the largest publicly available dataset of protein-ligane 3D structures with associated activity data. The dataset com- prises 5. 244, 285 structures across 1, 048, 857 unique protein-ligand systems, cu- rated from the ChEMBL and BindingDB databases, which were then computa- tionally folded using the Boltz-1x model. We provide comprehensive charac- terization of the dataset, including distributional statistics of proteins and ligands, and evaluate the structural fidelity of the folded complexes using PoseBusters. Our analysis reveals that approximately 3% structures exhibit physical anomalies, predominantly related to internal energy violations. As initial demonstration, we benchmark several binding affinity prediction methods, including empirical scoring functions (Vina, Vinardo), 3D convolutional neural network (Onionnet- 2), and graph neural network (AEV-PLIG). While machine learning-based mod- els consistently outperform traditional scoring function methods, neither exhibit a high correlation with ground truth affinities, highlighting the need for models specifically fine-tuned to synthetic structure distributions. This work provides a foundation for developing and evaluating next-generation structure and binding- affinity prediction models and offers insights into the structural and physical un- derpinnings of -rigein-ligand interactions. The link to the data will be added upon publication, to preserve anonymity of the submission.
INTRODUCTION
Understanding the interaction between proteins and ligands is a fundamental problem in chemical biology and drug discovery. The binding affinity of ligand to its target protein, as well as off- target proteins, is critical parameter when it comes to designing small-molecule drugs. In principle, the binding affinity can derived from the structural information of the protein-ligand complex, as the threc-dimensional structure of the complex describes most of the interaction between the protein and the ligand. In practice, however, there are limitations to predicting affinity from structural data, both from experimental and computational perspective. From experimental perspective, despite tremendous advances in the field, there are significant challenges generating experimental
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structures for some proteins, limiting the accessibility and resolution of some structures. Addition- ally, in spite of significant advancement in higher throughput methods, the effort needed to produce experimental structural information makes it difficult to efficiently integrate it into the design ey- cle. From the computational perspective, despite their tremendous utility, traditional methods used to calculate binding affinities like MM/GBSA (Wang et al.] 2019) and free energy methods (e.g- Cournia et al. [2117] [Crvelli-Deckee et al.) [024] York 2023) rely on the use of force fields which limit their accuracy, while quantum mechanical based methods remain prohibitively expensive. Ad- ditionally, traditional methods strongly depend on the quality of the protein-ligand complex, with small inaccuracies producing large errors the estimation of binding affinities.
To address these limitations, one approach is to learn surrogate functions that approximate bind- ing affinity directly from protein sequences and ligand SMILES representations (e-g.. 12t.Oktükk al, 2019 Jiang et al., 2022] Limbu & Dakshanamurthy 2022). However, binding affinity is fundamen- tally determined by the three-dimensional (3D) structure of the protein-ligand complex, which is not fully captured by primary sequence information alone. As a result, deep learning methods that operate on 3D structural imputs-whether using convolutional neural networks (e.g., Jiménez et al.) 2018 Zheng et al. 2019: Wang et al. 202T) or graph neural networks (e.g.. Son & Kim 2021)
generally more accurate and robust.
An important issue for scaling deep learning-basee based affinity prediction methods utilizing 3D struc- tures is the availability of high-quality crystal structure data and accurate binding affinity values. The number of known protein-ligand structures (both from cryo-EM or from X-ray crystallography) that are paired with measured binding affinity values is fairly limited considering the number of possible combinations that may exist nature (Askr et al.) 2023 Libouban al., [2023] Wang 2024 Zeng et al.) 2024). Moreover, surrogate models trained on these data are heavily impacted by the quality of crystal structures and accuracy of measured binding affinities used. There has been
substantial progress in recent years at improving the coverage of these datasets, however existing datasets still lack sufficient coverage both protein and ligand space.
One possible solution to address the lack of data coverage, is to augment existing datasets with high-confidence computationally folded structures, through a process termed distillation. This ap- proach is frequently used in the field and has been applied to number of recent works such in AlphaFold (Jumper et al. 2021 Abramson et al.] 2024), Chai-1 (Chai Discovery 2024), or Neu- ralPlexer (Qia a 2024). Other groups have attempted to leverage experimentally determined strucutres by leveraging the PDB and linking them with other external resources like BindingDB. For example, the PLINDER dataset (Durairaj et al. 2024), utilized this approach to curate nearly 450k protein-ligand pairs for both apo and holo structures. However, experimentally derived affinity values were only included if available from BindingDB and only represents smaller fraction of the full dataset. In another example, the CrossDocked dataset Francoeur et al. (2020) used experimen- tally solved bound structures and docked bound ligands other similar binding pockets. However, this dataset doesn't 't include experimentally verified affinity values and only includes poses with bi- nary classification labels for use in downstream machine learning tasks. Thus, there is clear need
Figure 1: Three example protein-ligand complexes from our SAIR dataset. Protein chains are color coded by PLDDT score with blue, yellow, and red regions corresponding to high, medium, and low confidence regions. From left to right the first complex corresponds to sample 4702 with Uniprot ID C7C422 and Ligand InchiKey BFOYHALOQCJINB-AA SUAOCCCINNN-UUAAAYAAAAAAAAAAAAAA responds to sample 501640 with Uniprot ID P28062 and Ligand InchiKey ODIWGDOSSAWSIL GKWCIWIWSA-N and the third complex corresponds to sample 253438 with Uniprot ID PO7858 and Ligand InchiKey
the second complex cor-
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to further enhance the availability of protein-ligame pairs with bioactivity and binding affinity data to enable the training of large-scale supervised machine learning tasks.
In this work, present the Structurally Augmented IC50 Repository (SAIR): the largest publicly available dataset of protein-ligand 3D structures | with annotated potencies (some example struc- tures shown in Fig. T.. Our data consists of T. 048, 857 protein-ligand complexes, which were obtained from the ChEMBL (Gaulton et al. 2012 Zdrazil et al. (024) and Binding (Liu et al.) 2007 2025 datasets. The data were preprocessed and filtered described in $2 and the pro- rein-ligamd complex structures were folded using the Boltz-1x model (Wohlwend et al., 2024), publicly available implementation inspired by AlphaFold
Results derived from our dataset are
presented in [B] including the performance of two binding affinity prediction models: one trained on protein sequence and SMILES representations, and another trained on 3D structural data. We conclude with a summary of key findings in $
Our main contribution is the public release of the SAIR dataset. The paper describes how the dataset was obtained, and presents some analyses using the data. The link to the data will be added upon acceptance, to preserve the anonymity of the submission.
2 DATASET CONSTRUCTION
Table 1: Comparison of Protein-Ligand Binding Databases
Database
Protein- Pairs
Structural Data Type
Potency Data
CroksDocked
22.5m
Synthetic + Experimental
No
PDBbind+ (CITE)
27,385
Experimental
Yes
Binding MOAD
41,409
Synthetic Experimental
Yes (15,223 entries)
PLINDER
449,383
Experimental
Yes (from BindingDB)
DockGen
41,791
Synthetic
No
SAIR (This Work)
1,048,857
Synthetic
Yes
2.1 DATASET CURATION
Bioactivity data were obtained from the ChEMBL35 release (Gaulton et al. 2012) and BindingDB (1Q2025) (Liu et al. 2007), and subsequently curated using minimal set of filters designed to retain a large volume of high-quality data. The specific filters are described below.
ChEMBL35: 1. Removed entries missing ligand SMILES or pchembl values. 2. Removed entries which: did not have a UniProt ID for the protein target, referenced multiple protein targets, or referenced protein variant. 3. Removed entries with data validity commen[] 4. Removed entries where standard relation was < or >. This step ensures that any measured values obtained are within the limit of detection for the assay. 5. Only included assays that were flagged by ChEMBL as measuring binding (c.g., K,, IC50, K²). 6. Removed measurements outside of a reasonable
biochemical assay dynamic range (1 PM < x < 100 pM).
BindingDB: 1. Removed entries missing molecule SMILES or IC50 values. 2. Removed entries without a UniProt ID for the protein target or referenced multiple protein targets. 3. Removed entries where reported IC50 values contained inequalities (i.e. or >). This step ensures that any measured values obtained are within the limit of detection for the assay. 4. Remove measurements outside of reasonable biochemical assay dynamic range (1 pM < x < 100 jM).
After initial curation, data from both sources were merged into single table. While this curation strategy introduce variability in IC50 values by combining data from different assays Landrum & Riniker (2024), this dataset still fully compatible with the maximal curation strategy outlined Landrum & Riniker (2024) for data points from ChEMBL. Because BindingDB does not perform curation at the level of specific assays, the maximal curation strategy is not compatible with that source. For protein-ligand complexes that appear in both ChEMBL and BindingDB, keep the information from both datasets.
'The data validity comment was introduced in ChEMBLI5 and includes information about the quality of the entry and to allow users to make an informed decision whether to include that value in their analyses https//ddcco..hhogg....mm//////t
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All bio-activity values were converted pIC50 units (- (-log10). SMILES strings for the ligand molecular structures were standardized by the removal of salts, protonation at neutral pH (where possible), and canonicalization using RDkit. Note that the choice of neutral pH for the ligand pro- tonation is immaterial for the subsequent computational prediction of the protein-ligand structures, as current cofolding models do not predict the positions of hydrogen atoms.
A coarse ligand library filter was applied to exclude likely false positives/false negatives by remov- ing PAINS and molecules with molecular weights exceeding 1250 Da. Protein-ligand complexes containing proteins with more than 2000 amino acid residues were excluded, in order to increase the probability of successful prediction by the cofolding model on current GPU hardware. Next, duplicate entries were removed based on UniProt accession and canonical SMILES.
The amino acid sequence for each protein was obtained from its UniProt entry using the accession number provided in the ChEMBL or BindingDB dataset. Note that this canonical sequence from UniProt may differ from the one used in the original bioactivity assay. For instance, the experimental protein may have been truncated construct, mutant, or specific quaternary structure (e.g.. homodimer), whereas our analysis used the monomeric sequence from the database.
Finally, to avoid data leakage when using this dataset to train or evaluate models that use structural data from the PDB for training, protein-ligand systems that already have experimentally-sote structures in the PDB were removed. The existence of a corresponding structure in the PDB was determined by finding the Chemical Component Dictionary (CCD) identifier of the ligand (by first computing its InChlKey Heller et al.) (2015) using RDKit) and looking for matches this (Uniprot ID, CCD ID) pair in the PDB. This search utilized the RCSB GraphQL search APIres and the PDBe REST API provided by EMBL-EB(SIF
This results in 1, 048, 857 complexes, with 936, 702 from ChEMBL and 613, 597 from Binding (see table in A Note the number of complexes from each source adds up more than the total number, because of complexes that appear in both sources. For structure generation, duplicated complexes were only folded once. The distribution of PIC50 values is shown Fig. 2a
2.2 STRUCTURE PREDICTION
We used the Boltz-Ix Ix folding model to generate 3D structures for all protein-ligand complexes described in $2 Boltz-1 is a publicly available implementation of AlphaFold + We selected Boltz-1x over other models, such AlphaFold 3, primarily ensure the dataset could be fully open-sourced as a community resource. At the time of generation, the weights and training code for AlphaFold were not available under license permitting the generation and redistribution of a dataset of this scale (> 5M structures). Boltz-1x x is MIT-licensed, ensuring the pipeline remains reproducible. Additionally, Boltz- Ix extends the base model by introducing a guiding potential to the diffusion process to prevent clashes, resulting in more physically realistic binding poses. Due to the lack of quaternary structure information, all complexes were treated as monomeric assemblies. We generated five structure samples per complex, as this represents the maximum number we can compute on a single GPU for the longest protein sequence in the dataset (see $ below for more details). While it is common practice to increase sample diversity by varying random seeds across multiple runs, we did not apply this technique due to resource constraints
The Boltz-Ix model was run using three recycling steps and 200 sampling steps (any other settings are the defaults as of the boltz- Ix release). Note that, as all these systems are monomeric, the pairing strategy is irrelevant.
Multiple sequence alignments (MSAs) for input to the model were generated using the MMseqs2 tool (Steinegger 2017) (via the ColabFold Mirdita et al. (2022) project). This used the UniRef30 sequence database version 2302 and the ColabFoldDB metagenomic sequence database version 202.08.
"There are some minor changes between AlphaFold 3 and Boltz-1, 1, such the strategy used for Multiple Sequence Alignment (MSA) subsampling. Further, Boltz-1's I's MSA subsampling is deterministic with respect to the random seed, unlike other cofold- ing models such AlphaFold3 and Chai-1 (Chai Discovery) 2024)). where seed variation is primary source of stochasticity. As a result, we do not expect significant diversity gains from seed variation in Boltz-1x.
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All Sources
ChEMBL
BindingDB
Cell
Biochemical
Homogenate
Proquency
0.2
0.0
10
10
10
10
In5so (TC(00)MM))
Togoo (IC50)MM))
Ingin (TC550 (TC)50.M))
logio (IC50/MM)
Tog1o (IC80))M)
logan (TC18))eM)
(a) Distribution of pIC50 values stratified data source and assay type.
Distribution of Protein Families SAIR Dataset
Analssis Type
8%
40%
[ligh Conf
Blochemical
Cell
6%
Frequency
30%
Frequency
4%
20%
2%
0%
0%
other
phosplatoee
kinsse
enyyme
mechar receptot
GKE
500
1000
1500
2000
Sequence Length
(b) LEFT: Distribution of protein familics. The thick, shaded bar shows the distribution for the full dataset; whereas the thin solid bar shows the distribution for high confidence predictions only. RIGHT: Distribution of protein sequence lengths.
Figure 2: Dataset Statistics. (a) pIC50 distribution. For histograms except the first, the overall distribution is shown in grey. (b) Protein family and sequence length distributions.
RESULTS
3.1 DATA STATISTICS
3.1.1 PROTEINS
The 048, 857 protein-ligand systems in the dataset comprise 5, 149 unique protein sequences. Of
these 5, 149 proteins present in our dataset, 2, 150 are believed to have no structures deposited in the PDB. The distribution of sequence lengths is shown in the right panel of Fig. 26 Most sequences fall within the 300-500 amino acid range. Beyond 500 residues, the frequency decreases steadily, with very long sequences (e.g.. 1500 amino acids) appearing only rarely.
Sequence clustering with MMseqs2 (Steinegger 2017) using reasonable values for the minimum sequence identity and minimum coverage (MMseqs2 flags --mi -----ii and -c, respectively) revealed the presence of large number of singleton clusters. For example, setting --min-seq-ic and -c to [0.8, 0.8). [0.5, 0.7]. and [0.3, 0.2] resulted in 3793, 2818, and 1862 clusters, respectively.
Proteins were assigned to family by using metadata provided by the UniProt database. We first classified them into enzymes or non-enzymes by looking at the presence of an enzymatic activity number (EC number). Enzymes were further subdivided into kinases (EC 2.7.x), phosphatases (EC = 3.1.z) and other enzymes. Non-enzymes were subdivided by looking at their gene ontology codes (GO code). For example, the presence of GO 0004879 implies that the protein is nuclear receptor.
The distribution of protein families accross different assays is shown in the left panel of Fig. 2b We see that the biochemical assay has a
larger proportion of phosphatases, kinases and enzymes, while the cell assay has more nuclear receptors and GPCRs. There is also a larger number of proteins in the
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Failure Rates per PoseBuster Category
Analysis Type
All Sources
Failure Rate (%)
Biochemical
Cell
RDDK Fatline
aramatic ring. Seirang
band ang
bead Jenggho
double. Hatmmm
Interual Loeees
Internal atteri-elaaii claol)
nuniver arw.sati..cc..oo. fuigg
nunber claolig
number deaabl Jaunrds
nutaher Jong Jong--utlleet Jaanda
nuniber albort Lautlier Javnds
sunber salld Lungleo
namberr boondo
numberr valit mosteccii piatra
PoseBuster Failure Type
Figure 3: Failure rate for each PoseBusters check, defined as the number of structures failing a given check divided by the total number of failed structures. Bar colors indicate assay type. The first entry in the x-axis corresponds to cases where RDKit failed to load the ligand.
cell assay, for which we could not parse family information. The figure also shows the distribution when we keep only proteins for which the folding model has high confidence, which shows that the model is much more confident in its prediction of kinases and enzymes.
3.1.2 LIGANDS
We used RDKit (Landrum al., 2025) to compute a range of chemical descriptors, as summarized in $ The values capture the statistics of the unique ligands in the dataset.
worth noting that we did not perform any filtering on the dataset on the basis of things like liugglikenesss" of the ligands, for example filtering samples with ligands below certain molecular weight. This is to avoid losing useful and chenically-meaning species, such as ionic cofactors or small organic fragments that can teach model protein-small- molecule interaction chemistry. It is our expectation that users will choose to filter samples based on ligand characteristics according to their use case.
data points of biologically-relevant
3.2 POSEBUSTERS
We evaluated all generated protein-ligand structures using PoseBusters (Buttenschoen et al. 2024). table with summarized results can be found in $C Overall, Boltz-Ix performs well in generating physically valid structures, with only approximately 3% of structures failing any PoseBusters check. This number is consistent with the performance reported in Wohhwend et al. (2024). Notably, only 0.53% of ligtein-ligand complexes had all five generated structures fail. Focusing different protein families, we find that the model has lower failure rates for kinases, and phosphatases; and fails more often for GPCRs. One interesting case are nuclear receptors, where the overall failure rate is low, but there is a relatively large number of complexes, for which all structures failed (more than for any other family) indicating that some of the nuclear receptor complexes are particularly hard to fold.
For the assemblies that failed PoseBusters validation, we present a more fine-grained analysis of individual test outcomes in Fig. 3 It is important to note that the failure rates shown in Fig. 3 are calculated relative to the 3% of structures that failed, not the entire dataset. Across all assay types" the most frequent source of failure is the internal energy check, which accounts for more than half of PoseBusters failures. High internal energy often indicates minor steric clashes or bond length anomalies common in raw generative outputs, rather than fundamental structural failures. These
We do not show results for the homogenate assay, there are not enough structures, compared with the rest.

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Spoarman Correaatin (IC 5tpprurnnn Correlation (IC 515pcarman Correlation (IC 50)
(+)
IPTM (+)
Complex iPDE
0.4
0:27
0.32
0:22
0.200
-0.4
Complex PDE (-)
Complex iPLDDT (+)
Complex pLDDD
0.29
0.01
-0.05
-1,8
-0.05
-0.4
Cell
Cell
Cell
Confidence Score (+)
Chains (+)
Interattion
0.4
0.11
0.00
-0.01
-103
-0.1
Cell
Cell
All
Binch
Cell
Figure 4: Comparison of the Spearman correlation r, between different Boltz- confidence metrics, and the experimental IC50 activity, by assay type. The signs shown in the title of each panel indicate the expected direction of correlation: negative for PDE and iPDE (as they represent distances), and positive for all other metrics.
can often be resolved via standard energy minimization (e.g., with OpenMM (Eastman 2023
without altering the binding pose significantly. Other common failure modes include the number of bonds, abnormal bond lengths, and ligands that could not be loaded by RDKit.
3.3 BOLTZ CONFIDENCE METRICS
Given that we have access to experimental binding potencies for the corresponding complexes, we assessed whether Boltz-Ix's confidence metries correlate with binding affinity. Prior work has demonstrated that AlphaFold confidence scores correlate with binding affinity in protein-prote interactions (Zambaldi et al., 2024) (PPIs). Here, we explore whether similar correlations exist in the context of protein-ligand interactions. Results are shown in Fig. Focusing the blue bars (Spearman correlation averaged across assay types), we observe significant correlation be- tween certain confidence metrics, particularly those involving the protein-ligaan interface namely iPTM, complex iPDE, and complex iPLDDT- and experimental potency. These findings suggest that Boltz-1x² 1x't structural confidence metrics provide some predictive signal for protein-ligand bind- ing affinity. Notably, the strength of the correlation varies by assay type: it highest for biochemical assays and weakest for cell assays. We hypothesize that this is caused by the great specificity and accuracy of biochemical assays, while cellular and homogenate assays may introduce additional confounding factors such as off-target binding, permeability, and intracellular dynamics. To further probe protein- ligand interaction quality, we introduce new metric interaction PTM - defined as the average of the off- off-diagonal values the pair chains ptm confidence head. This metric captures the confidence of the protein with respect to the ligand, and vice versa, and is analogous the "interaction PAE" described in Zambaldi et al. (2024). We find that interaction PTM exhibits moderate positive correlation with binding affinity (r'a 0.25), ranking second only to iPTM (r. 0.27) in predictive power across our dataset.
We can furthermore look the similarity between generated protein chains and protein chains from the training dataset. We find a high degree of correlation (Spearman correlation of 0.47) between the global PTM confidence and the highest TM-score (normalized by query length) to structures in the training set. This metric is independent of the ligand, which partly explains the poor correlation to the binding affinity. It is better suited for evaluating the global shape of the protein.

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Speaman Correlation
Pearson Correlation
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0.0
0.75
Kendall Correlation
All Sources
0.2
0.70
High Conf
Blochemical
AUC
0.65
0.1
0.60
0.55
0.0
0.50
RMSE (Rescaled)
MAE (Rescaled)
1.00
1.0
0.75
0.50
0.5
0.25
0.0
0.00
Vina minimized
Vina
Vinardo
OntonNet
AevPlig
IPTM
Vina minimized
Vina
Vinardo
OnionNet
AevPlig
IPTM
Figure 5: Comparison of binding affinity prediction performance across various methods and assay types. Top/Middle Rows: Correlation metrics (Spearman, Pearson, Kendall) and AUC (ranking ability). Higher is better. Bottom Row: Error metrics (RMSE, MAE). Lower is better. To ensure a fair comparison between methods outputting PIC50 (MI. models) and those outputting energy scores (Vina, Vinardo) confidence probabilities (iPTM), all predictions to the experimental PIC50 range before calculating RMSE and MAE. Nested Bars: The wider, semi-transparent bars represent the full dataset ("All Sources"). The narrower, solid bars nested within them represent the subset of structures where Boltz- Ix had high confidence (> 0.8).
linearly recalibrated
3.4 BINDING AFFINITY MODELS
We use the SAIR dataset benchmark the performance of several binding affinity prediction models. The combination of cofolding-generated structures with structure-based affinity predic- tion represents promising and increasingly adopted approach in the scientific community. By providing thehhhhhoughhut structural models paired with experimentally observed IC50 values, the SAIR dataset enables rigorous evaluation of this emerging class of predictive methods.
The field of protein-ligand binding affinity prediction is supported by a vast and diverse body of literature, and comprehensive comparison of all available methods beyond the scope of this work. Instead, we focus on three representative and methodologically distinct approaches to binding affinity prediction:
Empirical scoring functions: We employ two different traditional empirical scoring functions: AutoDock Vina (henceforth referred to as Vina) (Trott & Olson 2010) and Vinardo (Quiroga & Villarreal 2016), both calculated using the GNINA library (McNutt et al. 2021). We additionally evaluate first minimizing the ligand pose using the Vina scoring function before scoring the resulting structure, again using Vina (this is referred as "Vina minimized").
Convolutional neural network (CNN): As first method of structure- struetur-bbased machine learning affin- ity prediction, employ fire--immnsiioal CNN, and represent the input by voxels. There are various available 3D CNN methods for affinity prediction, but we use Onionnet- 2 (Wang al. 2021 of the state-of-the-art methods.
projecting it into 3D
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Graph neural network (GNN); As an alternative structure-based machine learning method, consider a GNN. GNNs are, theoretically, better suited for the task of affinity prediction, as protein- ligand systems are easily represented as graphs. However, regression from graphs is generally harder task than regression using voxels, as graph convolutions are non-trivial (Zhang et al., 2019). As our GNN, we the AEV-PLIG model (Warren et al. 2024 Valsson et al. 2005). which recently showed state-of-the-art performance in structure-based binding affinity prediction.
In all cases (with the exception of the "Vina minimized" approach, as explained above), the given affinity prediction tool is evaluated on the predicted three-dimensional protein-ligand structure as-is.
There are many other methods that could be used for benchmarking binding affinity prediction. For example, the recently developed Boltz-2 (Passaro et al. 2025) accomplishes accurate binding affinity prediction via regression from intermediate embeddings. However, it is trained on similar data to what we present here, therefore it was not used for comparison. On the side of physics- based methods, Free Energy Perturbation (FEP) methods are considered the most accurate, however they are very computationally expensive, making it difficult to use them in a dataset of millions of structures like the one presented in this work.
We use four metrics to compare the performance of the various binding affinity methods. Details about them are shown in $D
We restrict our evaluation to structures derived from CHEMBL, as BindingDB includes experimental protein-ligand complexes that were used in the training of AEV-PLIG. Although the structures in our dataset are synthetically generated and not identical to those used in training, we exclude BindingDB entries to minimize the risk of data leakage and to ensure a fair comparison. As an additional consideration, AEV-PLIG was trained using the BindingNet database Li et al.] (2024), which contains synthetically generated structure-activity relationship data derived from ChEMBL. This could account for AEV-PLIG's enhanced performance. Nevertheless, it should be noted that the BindingNet training set represents a minor portion of the overall SAIR database (55%), and the structures are likely distinct from those in the Boltz computed set.
We present the results of the model comparison in Fig. 5, Across all assay types, the GNN method achieves the highest performance, followed by the CNN method, with the empirical scoring fune- tions performing the worst. However, none of the methods achieve very high correlation, with Spearman correlations comparable to the ones achieved by some of the interface confidence metrics (even though those were not specifically tuned for binding affinity prediction), such as iPTM, also shown the figure. To compute absolute metrics RMSE and MAE, outputs need to be rescaled, as different methods output values in different units. We therefore apply linear rescaling to the out- puts from each method (see details in SD. We find similar performance accross all methods, with again some slightly better performance by the machine- figure show the results when we only keep structures for which Boltz- 1x predicts a high confidence (> 0.8). We find that keeping only these structures improves performance of almost all models, as the structures are more likely to be correct.
methods. The thinner bars in the
It is important to note that both the GNN and CNN models were originally trained on experimental structures, and our evaluation is conducted on synthetic structures generated via cofolding. Fine- tuning these models on subset of the synthetic dataset would likely improve their performance and better align them with the structural distribution seen at inference time.
As noted in our dataset analysis (Fig. 2b). the dataset contains uneven distribution of protein fam- ilies. To ensure that our benchmarking results are not driven solely by well-represented targets (c.g., Kinases), we also performed family-balancee evaluation. In this analysis, metries are calculated using sample weights inversely proportional to the frequency of the protein family. As detailed in {G we observe that the performance ranking of models and the magnitude of correlations remain consistent under this balanced evaluation (Fig. [). providing empirical evidence that the predictive signal generalizes across diverse protein targets.
3.5 TRAINING MODELS ON SAIR
A primary motivation for creating SAIR is to address the scarcity of structure- -fffturr-aaffiity data available for training deep learning models. While the benchmarks in Fig. 5 assses the zero-shot performance of existing models on our dataset, a critical question is whether SAIR can serve as an effective training resource to improve model generalization.

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Recent independent work by Wei et al. (2025) provides strong empirical validation of SAIR's utility in this context. In their development of GatorAffinity. a geometric deep learning scoring function, the authors utilized over million protein- pogein- complexes from SAIR for large-scale pre-training before fine-tuning on experimental structures. Their analysis revealed two key findings relevant to our dataset:
Data Scaling Law:
The study demonstrated a power-law relationship between the size of the syn- thetic pre-training data and downstream model performance. Specifically, augmenting their training set with SAIR consistently reduced the Root Mean Square Error (RMSE) on the independent PDB- bind benchmark, with the best performance achieved when combining SAIR's IC50 data with K and K, datasets.
State-of-the-Art Performance: By leveraging the scale of SAIR, the GatorAffinity model was able to outperform existing state-of-the-art methods, including GIGN and PSICHIC, achieving an RMSE of 1. 1.293 on the PDBbind benchmark compared 1.343 without the use of SAIR.
These results confirm that despite the synthetic nature of the structures, SAIR contains strong, learnable signal that materially improves the performance of structure-based affinity prediction mod- els when used for pre-training-
4 CONCLUSIONS
In this work, we introduce the Structurally Augmented IC50 Repository (SAIR), large-scale dataset of protein-ligand 3D structures paired with annotated binding affinities. Comprising 5, 244, 285 synthetically-generatee structures representing 1, 048, 857 protein-ligand complexes, each annotated with expeinennally-determined potency, the dataset is designed to significantly ex- pand the volume of data available for training and evaluating structure-based deep learning models in drug discovery.
We rigorously evaluated the quality of the generated structures using PoseBusters and observed low overall failure rate of approximately 3%. To assess the utility of the dataset for predictive modeling, we benchmarked several structure-based binding affinity prediction methods. Graph neural networks performed best, followed by convolutional neural networks and empirical scoring function methods. However, all models achieved only modest correlations, comparable to those obtained from the folding model's interface confidence metrics. This suggests that models trained on experimental structures may not generalize well to synthetic data, highlighting the potential need for fine-tuning on generated complexes.
Looking ahead, this work opens several promising avenues for future research. We also aim to use SAIR to improve the performance of binding affinity prediction methods. More broadly, fine-tuning existing affinity prediction models-or developing new architectures specifically optimized for syn- thetic protein-ligand complexes-could lead to significant gains in predictive accuracy. Beyond affinity prediction, the dataset may also support self-distillation strategies for cofolding models, as has been demonstrated in other data modalities.
Finally, SAIR represents a valuable resource for inverse design tasks, enabling generative ap- proaches to create new ligands conditioned on a target protein-further expanding the possibilities for structure-based drug discovery.
Limitations: While SAIR provides an unprecedented scale of data, has inherent limitations. First, as distilled dataset, it carries the inductive biases of the teacher model, Boltz-Ix. To assist users in identifying potential mode collapse or model bias, we have included "pocket diversity" statistics in the dataset metadata (see SFD, allowing users to filter out proteins where the model fails to generate diverse binding modes.
Second, validation via PoseBusters ensures chemical plausibility (e.g., valid bond lengths, low en- ergy), but it does not guarantee biological correctness. Because we explicitly filtered out proteins present in the PDB to prevent data leakage, ground truth structures are not available for RMSD comparisons. Users should treat the provided confidence metrics (iPTM, iPLDDT) as proxies for biological plausibility rather than guarantees.
Finally, in this version of the dataset, we folded the monomeric protein chain and the ligand of inter- est; cofactors and ions were not explicitly modeled. To mitigate this, we have added drug_like ikke filter column to the dataset metadata, allowing users to exclude tiny ligands, ions, or fragments that may be artifacts cofactor-dependent.
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ETHICS STATEMENT
The dataset is curated from publicly available, open-access databases, specifically ChEMBL and BindingDB, which contain experimentally determined bioactivity data. We have not used any private or sensitive human data. The primary purpose of this dataset to advance drug discovery through computational methods, field that aims develop new treatments for diseases. The potential societal benefits are significant and do not involve foreseeable harm.
The dataset, link to which will be provided upon acceptance, will be released publicly promote open science and collaboration in the research community. We believe that providing this resource the public will foster a collaborative environment and accelerate scientific progress.
REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our work, have provided comprehensive details on the dataset's construction, model evaluation, and analyses in the main paper and its appendices.
Furthermore, for full transparency and to aid in future research, we have provided detailed manifest of the dataset's file organization and a description of each column in the central sai r parquet file in $ Upon acceptance, the link to the dataset will be made publicly available, allowing other researchers to replicate our findings and build upon our work
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Table Data distribution by source and assay. Note that the total number between both assays larger than the number of generated structures. That is because of protein-ligand pairs that appear in both datasets
Assay
Source
BindingDB
ChEMBL
biochem
2,293
416,331
cell
0
183,286
homogenate
0
13,980
na
934,409
Total
936,702
613,597
Barbara Zdrazil, Eloy Felix, Fiona Hunter, Emma J Manners, James Blackshaw, Sybilla Corbett, Marleen de Veij. Harris loannidis, David Mendez Lopez, Juan F Mosquera, et al. The chembl database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic acids research, D2IDI):D1180-D113 2024.
Xin Zeng, Shu-Juan Li, Shuang -ing Lv, Meng-Liang Wen, and Yi Li. A comprehensive review of the recent advances on predicting drug-targes affinity based on deep learning. Frontiers in Pharmacology, 15: 1375522, 2024.
Si Zhang, Hanghang Tong, Jiejun Xu, and Ross Maciejewski. Graph convolutional networks: a comprehensive review. Computational Social Networks, 6(1):1-23, 2019.
Liangzhen Zheng, Jingrong Fan, and Yuguang Mu. Onionnet: multiple-layer intermolecular- contact-based convolutional neural network for protein-ligand binding affinity prediction. ACS omega, 1(14):1555-115966 2019.
HARDWARE SPECIFICATIONS
This work was performed on an H100 cluster in DGX Cloud on Google Cloud provisioned by NVIDIA's AI Accelerator team. This was initially provisioned as managed 35-Node (280 GPUs) cluster, and was non-disruptively scaled up to 96 nodes (760 GPUs) to allow for scale-out workloads.
In total, these calculations used approximately 130k GPU-hours of compute time on this cluster.
A DATA DISTRIBUTION
shows the distribution of datapoints by source and assay.
B CHEMICAL DESCRIPTORS
Table 3 shows a summary of the chemical descriptors of the different ligands in our dataset.
C POSEBUSTERS RESULTS
Table summarizes the posebusters results accross different protein families.
D METRICS
In 3.4
the following metrics compare the various affinity prediction models.
Spearman Correlation: The Spearman correlation between the predicted and experimen- binding affinity, defined as:
6 6Σ d? r. =
(1)
n2nn² - 1)
where d; is the difference between the ranks of the predicted and experimental binding affinities, and is the number of samples.
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Table Chemical descriptors across the dataset (aggregation of unique ligand entries).
Descriptor
Min
Max
Mean
Stddev.
Molecular weight
17.0
1 1.25++03
4.46++02
1. 1.25++02
Heavy atom count
1.00
94.0
31.7
8.88
Hetero atom count
0.00
46.0
8.55
3.31
H-bond acceptor count
0.00
36.0
5.89
2.33
H-bond donor count
0.00
25.0
1.89
1.58
Topological polar surface area
0.00
6.40e+02
92.9
43.3
Wildman- Crippen LogP
-13.1
19.2
3.80
1.72
QED (Drug-likeness)
0.00684
0.948
0.498
0.204
Rotatable bond count
0.00
53.0
6.11
3.69
Fraction Csp3
0.00
1.00
0.321
0.177
Aliphatic carbocycle count
0.00
19.0
0.328
0.720
Aliphatic heterocycle count
0.00
20.0
0.754
0,828
Aliphatic rings count
0.00
21.0
1.08
1.08
Aromatic carbocycle count
0.00
20.0
1.55
0.935
Aromatic heterocycles count
0.00
11.0
1,45
1.10
Aromatic ring count
0.00
20.0
3.00
1.16
Bridgehead atom count
0.00
20.0
0.110
0.598
Spiro atom count
0.00
6.00
0.0392
0.210
Table Summary of PoseBusters results by family
Family
Metric
Structures
Assemblies
Overall Analysis
Total failed
166,241
5,526
Total
5,244,285
1,048,857
Percentage failed
3.17%
0.53%
Enzyme
Total failed
59, 169
1,944
Total
1,699.025
339,805
Percentage failed
3.48%
0.57%
Kinase
Total failed
34,160
1,639
Total
1,531,115
306,223
Percentage failed
2.23%
0.54%
Other
Total failed
46,248
1,200
Total
1,170,445
234,089
Percentage failed
3.95%
0.51%
Phosphatase
Total failed Total
12,181
329
418,895
83,779
Percentage failed
2.91%
0.39%
GPCR
Total failed
10,304
188
Total
267,650
53,530
Percentage failed
3.85%
0.35%
Nuclear Receptor
Total failed
4,179
226
Total Percentage failed
157,155
31,431
2.66%
0.72%
Pearson Correlation: The Pearson correlation between the predicted and experimental
binding affinity, defined as:
E(z, - (2) 7)
VE 2) ( 7)
where æ; and it are the predicted and experimental binding affinities, respectively, and and y are the means of the predicted and experimental binding affinities, respectively.
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Kendall's Tau: Kendall's Tau is measure of the ordinal association between two quanti- ties, defined as:
(nc - Rdd) nnnn - 1)
where nh is the number of concordant pairs, and na is the number of discordant pairs, and 7t is the number of samples.
Area Under the Curve (AUC): The AUC is measure of the ability of model to distin- guish between positive and negative samples. It is defined as the area under the Receiver Operating Characteristic (ROC) curve, which is plot of the true positive rate against the false positive rate. To calculate the AUC, we first need to define threshold for the pre- dicted binding affinity, and then compute the true positive rate (TPR) and false positive rate (FPR) for that threshold. We use a threshold of 100nM, which is a common threshold for binding affinity prediction.
Root Mean Squared Error (RMSE): Measures the square root of the average squared differences between predicted and actual values:
1
RMSE
0.)²
T1
Mean Absolute Error (MAE): Measures the average magnitude of errors:
MAE
1
(5)
A direct comparison of eπτоr metrics (RMSE and MAE) is complicated by the differing output units of the benchmarked methods. Deep learning models (AEV-PLIG, OnionNet-2) predict PIC50 values directly, whereas empirical scoring functions output binding free energies (Vina, Vinardo in kcal/mol) or confidence probabilities (iPTM E [o. 1)). To enable a fair comparison of predictive power, we applied linear recalibration all methods before calculating RMSE and MAE. For each method, we fit a simple linear regression model y oz + 3 to map the raw model scores x the experimental PIC50 values y. This removes systematic shifts and scaling differences (e.g., mapping kcal/mol to pIC50), ensuring that the reported error metrics reflect the model's 's ability to capture the underlying affinity signal rather than its unit scaling.
E POCKET DIVERSITY
We can model to gain insight into the effect that changing the input ligand has in the gen- crated protein conformation. If we give Boltz-Ix Ix sufficiently distinct ligands, is the model able to detect different appropriate binding sites, will re-use the pockets it has seen during training?
To address this, we need to define a pocket. First, we define pocket residue as every residue that has non-hydrogen atom within 6A to the closest ligand atom, cutoff commonly adopted in the field. The set of pocket residues defines pocket, and two pockets are considered similar if
1p1 Op2l
min (prl. (pzl) threshold,
where we defined the threshold to 0.8. That allows us to cluster the detected pockets per protein into groups, such that no group shares a similar pocket.
Fig. shows both the diversity in pockets over the five generated structures per protein/ligand com- plex (left panel), and the diversity for given protein models are known to generate similar conformations for differrnt diffusion samples, which we also find when looking the pocket diversity, with most complexes generating ligands in the same pocket for all five generated samples.
change ligands (right). AlphaFold3-Iin
However when looking at protein chain, with different ligands, find significant fraction of systems with a variable set of pockets. The most extreme example of this is protein P10636, where found well over thousand different potential binding sites for 345 different ligands.
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This shows potential reason for the fat tail in the number of distinct pockets per protein. When Boltz- 1x uncertain about the structure or if the protein very flexible, then we find very diverse set of protein conformations. The pocket-residues will similarly change lot, and there is well defined binding site.
Distinct pockets per batch
Distinct pockets per protein
80%
Fraction of predictions
Fraction of proteins
40%
60%
30%
40%
20%
20%
10%
0%
0%
3
4
2
10> 10
Number of pockets
Number of pockets
Figure 6: Pocket diversity for different samples and different ligands. Pocket similarity deter- mined using Eq. (6). Left: The diversity of pockets in the five generated structures per protein/ligand complex. Right: The diversity of pockets for different proteins.
We can also study pocket similarity to the training set. Fig. 7 shows the result of performing similarity search of all generated structures against the Boltz- Ix training data, the PDcket-LDDT score, as defined in Durairaj et al. (2024). The pocket-LDDT is defined by structurally aligning predicted structures to ground truth structures, and calculating the average LDDT over the backbone carbon atoms in the aligned pocket residues. In our case, this score is not measure of correctness, more measure of similarity to training dataset.
We find correlation between the pocket-L.DDT and the interface confidence (Spearman corre- lation of -0.02), but we find most pockets to be highly similar to the training data. We have previously seen that Boltz is able generate multiple distinct binding poses, which together implies that Boltz successfully places ligand atoms in plausible looking pockets.
IC50 DATASET STRUCTURE AND CONTENTS
This appendix provides detailed description the SAIR dataset, its file organization, and the contents of its primary data files.
F.1
DATASET MANIFEST
The IC50 dataset organized into the following primary components:
sair, wear.parguee parquet: This the central dataframe of the dataset, containing all curated IC50 data, associated original source metadata, results from PoseBusters structural valid- ity checks, Boltz-Ix prediction confidence measures, and other relevant metadata for each protein-ligand complex.
This directory contains all the predicted 3D structures generated by the Boltz-Ix co-folding model. For each unique protein-ligand complex, five distinct predicted structures (referred to as "models") are provided. Each structure is stored as a cif file, named according to the convention sample_kentry id> www.mmeeelooolceo Here, entry id corresponds to the entry id field sair parquet, and model identifies the specific sample (0 through from the co-folding model.
structures/:
cif.
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Distribution of pocket LDDT scores
8%
Frequency
6%
4%
2%
0%
0.0
0.2
0.6 Pocket LDDT score
0.4
0.8
1.0
Figure 7: The distribution of pocket LDDT's calculated, comparing the generated structures to the training dataset.
prediictio..coniideneee. This directory stores the raw json confidence files directly produced by the Boltz-Ix model. The files follow the naming convention confidence Senfideeccesample sample...le.................eeee eenttyy id> model consistent with the structure files. These files contain detailed confidence metrics beyond those summarized sair. parquet.
json, with entry id and
This directory contains the raw npz files for Predicted Lo- cal Distance Difference Test (pLDDT) values, also output directly by the Boltz-1x 1x model. These files are named pldd= <entry, ids model <model> npz, using the same entry id and model identifiers. pLDDT values provide residue-level confidence esti- mates for the predicted structures.
prediction.plddeee
F.2 SAIR. PAIRUAA COLUMN DESCRIPTIONS
The sai parquet file serves as the main tabular data source, consolidating key information for each cructure-potency Pair.Table 5 Table Table and Table g describe each column in sair. parquet, categorized for clarity.
G ROBUSTNESS ANALYSIS
To address potential concerns regarding distillation bias spacissiiif,,llly.. that the model might perform well only on protein families that are over-represented the dataset or high-confidence subset (such as Kinases)-wy conducted robustness check using family-balanced metrics.
Fig. presents the performance all benchmarked models when evaluated with sample weights inversely proportional to the frequency of the corresponding protein family. Comparing these results to the standard benchmarks Fig. 5 we observe consistent trends: machine learning models (AEV- PLIG, OnionNet-2) continue to outperform empirical scoring functions, and confidence metrics (iPTM) maintain their correlation with affinity. This stability indicates that the reported performance not artifact of dataset imbalance.
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Table 5: Description of Columns in sair. Identifiers and Inputs
Column Name
Description
Identifiers
entry-id
Unique identifier for the protein- -igein- complex, common across all 5 predicted models for that complex. Unique identifier for the sample for this entry id, ranging from 0 to 4 (included).
index
Inputs
protein
UniProt accession ID of the protein target. Amino acid sequence of the protein. Sanitized SMILES string of the ligand, used for structure generation.
sequence
SMILES
SECSMILES
Original SMILES string of the ligand as provided in the raw underlying data source (ChEMBL BindingDB).
Table 6: Description of Columns in w.... parquet: Potency Data and Metadata
Column Name
Description
Potency Data and Metadata source
Origin of the bioactivity data: 'ChEMBL' or 'BindingDB' Description the assay as provided by the source database. Original potency value (e.g., IC50, Ki, Kd) from the under- lying data source its native units. Type of assay (e.g.. 'biochemical', 'cell', "homogenate', or 'na').
description potency
assay-type
assay-id
The original ChEMBL assay ID (if available) for granular grouping.
PIC50
Potency value converted to negative-log units.
family
The protein family classification for the target protein. Boolean flag indicating if the ligand is drug-like (excludes ions/cofactors).
drug.like
pocket.diversity
Metric indicating the diversity of binding modes generated for this protein.
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Table 7: Description of Columns in sair. pairuearuuet: Binding Affinity Predictions and Model Confidence
Column Name
Description
Binding Affinity Prediction Results snnardo_scors
Vinardo docking score for the predicted structure. Vina docking score for the predicted structure. Vina docking score for the predicted structure, after mini- mization.
vina.scom score vina_score_min
aevplig.score onionnet.score
Predicted binding affinity score from the AEV-PLIG model. Predicted binding affinity score from the Onionnet-2 model.
Model Confidence Estimates confidence_score
Overall confidence score produced by the Boltz-lx model for the predicted structure. Predicted TM-score (PTM) specifically for the interaction between differrnnt chains.
chains.ptm
ptm
Predicted TM-score, global metric of structural accuracy. Interface Predicted TM-score, focusing on the accuracy of the protein-ligand interface.
iptm
complex.pde complex-ipde
Complex Predicted Distance Error.
Complex Interface Predicted Distance Error, focusing rors at the interface.
complex.plddt
Predicted Local Distance Difference Test (pLDDT) for the complex.
complex.ipldd:
Interface Predicted Local Distance Difference Test (iPLDDT) for the complex. Custom metric: Average of off-diagonal terms in the pair chains PTM confidence head (protein-ligand interaction confidence).
interaction.pit ptm
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Table 8: Description of Columns in sair parquet: PoseBusters Results
Column Name
Description
PoseBusters Results
(Boolean flags indicate "true' for pass/success, 'false' for fail/issue, counts are integers)
mol_pred_loaded
Indicates if the predicted ligand molecule could be success- fully loaded by RDKit.
sanitization
Indicates if the ligand molecule passed RDKit's sanitization checks.
inchi.convertible
Indicates if the ligand molecule is convertible an InChl string.
aloms
wal.atoms.conneeee connected
Indicates if all atoms in the ligand are connected (no discon- nected fragments).
bond.lengths
Indicates if all ligand bond lengths are within expected ranges.
bond.angles
Indicates all ligand bond angles are within expected ranges.
internal steric clash
Indicates the presence of internal steric clashes within the ligand.
aromatic.ring..llateess
Indicates if aromatic rings in the ligand maintain expected flatness.
fouble.bond.flatee..
Indicates if double bonds in the ligand maintain expected flatness.
internal.energy
Indicates if the ligand's internal energy is within reasonable bounds (passed internal energy checks). Indicates if the conditioned (input) molecule could be loaded.
mol.cond.loaded
passs..aleece.cheekks passes kekulization
Indicates if the ligand passes valence rules.
Indicates if the ligand can be successfully kekulized by RD- Kit.
number.clashes
The count of clashes detected between the protein and lig- and.
number.shortt.otllier...oo number_longgoutt.eer.bboooo
The count of bonds in the ligand that are unusually short.
The count of bonds in the ligand that are unusually long. The count of valid bonds in the ligand.
number.valid.bondi
number valid angles number valid noncov pairs
The count of valid bond angles in the ligand.
The count of valid non-covalent pairs between protein and ligand.
number.arooatii..iing number.double.bot
The count of aromatic rings in the ligand. The count double bonds in the ligand. Boolean flag indicating whether the structure passed all PoseBusters checks.
all.passed
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0.30
0.30
Spearman Correlation
Pearson Correlation
0.25
0.25
0.20
0.20
0.15
0.15
0.10
0.10
0.05
0.05
0.00
0.00
0.75
All Sources (Balanced)
0.20
High Conf
Kendall Correlation
0.70
Biochemical (Balanced)
Cell (Balanced)
0.15
0.65
AUC
0.10
0.60
0.05
0.55
0.00
0.50
1.2
1.0
RMSE (Rescaled)
1.0
MAE (Rescaled)
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
0.0
Vina minimized
Vina
Vinardo
OnionNet
AevPlig
iPTM
Vina Vina minimized
Vinardo
OnionNet
AevPlig
iPTM
Figure 8: Family-Balaneee Balanced Performance Benchmarks. Comparison of binding affinity prediction performance using weighted metrics. The "All Sources (Balanced)" and "Biochemical (Balanced)" bars report metrics calculated using sample weights inversely proportional protein family fre- quency. The consistency with the main benchmarks suggests the model performance robust to family distribution biases.
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H
USE OF LARGE LANGUAGE MODELS
Large Language Models were used to proofread and improve earlier versions of the manuscript.
23


Reviews:
[{"review_id": "FlfTAZc1cO", "reviewer": "Reviewer_VX3P", "summary": "The manuscript introduces SAIR, a large-scale dataset of protein–ligand 3D complexes paired with activity labels. Activity data are curated from ChEMBL and BindingDB, and corresponding complex structures are computationally generated using the Boltz-1x model—this synthetic-structure generation is the core contribution. The work contributes: (1) a large, publicly available synthetic 3D structure–activity resource built with Boltz-1x from ChEMBL/BindingDB, (2) quality analysis highlighting failure modes, and (3) an initial benchmarking suite motivating methods calibrated to this data regime.\n\nAs an initial benchmark, they compare empirical scoring functions (Vina, Vinardo), a 3D CNN (OnionNet-2), and a GNN (AEV-PLIG). While ML models outperform classical scoring, correlations with ground-truth affinities remain modest, underscoring the need for models fine-tuned to synthetic structure distributions.", "questions": "1.  Why was Boltz-1 selected over more robust alternatives (e.g., AlphaFold 3)\n\n2.  Show evidence that training on SAIR improves performance vs. existing datasets (e.g., pretrain/fine-tune on SAIR vs. not)\n\n\n3.  Will the authors include stronger modern 3D baselines and fine-tune foundation models (e.g., Boltz-2), and compare frozen vs. full fine-tuning?\n\n4.  Add ablations comparing sequence-only, 2D-only, randomized poses, and alternative pose generators to quantify sensitivity to 3D quality and pose source.\n\n\n5.  Will the authors report RMSE/MAE, enrichment/hit-rate metrics, and calibration with confidence intervals?\n\n6.  The exact split protocols (by protein/scaffold/time), identity/scaffold thresholds, preprocessing steps, and measures to prevent leakage", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.  Constructs the largest dataset of predicted protein–ligand 3D complexes paired with activity labels; if released, it would be a valuable community resource for training and benchmarking.\n\n2.  Provides a preliminary benchmarking of multiple models on the dataset, offering a baseline reference for future methods.", "weaknesses": "1) Model choice: The use of Boltz-1 for complex generation is not justified against more robust contemporaries (e.g., AlphaFold 3).\n\n2) Benchmarking impact: Current results do not show that the dataset materially improves performance of existing models.\n\n3) Baselines and finetuning: Baselines omit modern 3D algorithms; no fine-tuning of foundation models (e.g., Boltz-2) or comparisons of frozen vs. full training.\n\n4) 3D utility ablations: No comparisons to sequence-/2D-only models or to randomized/alternative poses to assess sensitivity to 3D quality and pose source.\n\n5) IC50 Values from diverse assay conditions (system, temperature, readout) are pooled without harmonization or consistency analysis.\n\n6) Over-reliance on correlation/AUC; missing RMSE/MAE and decision-centric metrics (e.g., enrichment, hit rate).\n\n7) Experimental clarity: Dataset splitting (protein/scaffold/time) and leakage prevention strategies are not specified.", "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": "NZUg8jpw5P", "reviewer": "Reviewer_gzGf", "summary": "This paper introduces SAIR, the largest dataset of 3D protein-ligand structures with annotated binding affinity data. The dataset comprises over 1 million unique protein-ligand systems and a total of 5.2 million structures, was created by computationally folding complexes from the ChEMBL and BindingDB databases using the Boltz-1x model. The primary goal of SAIR is to provide a comprehensive resource for training and evaluating structure-based deep learning models in drug discovery.", "questions": "See Weaknesses", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The paper addresses an important real-world problem in structure-based drug discovery, i.e., lacking of large-scale protein-ligand 3D structural data with bioactivity annotations.\n2. The authors provide detailed descriptions of the specific steps for filtering and processing data from ChEMBL and BindingDB.\n3. The authors provide comprehensive and insightful experimental analysis.", "weaknesses": "1. One core conclusion of this work is that models trained on experimental structures show modest correlations on SAIR's synthetic structures, which the authors attribute to data distribution differences. However, it would be better to include an important validation experiment: fine-tuning or training (from scratch) a model on a SAIR subset, then evaluating on a held-out SAIR test set. Demonstrating significant performance improvement would more convincingly prove SAIR's value as a \"training resource.\" Currently, the paper positions SAIR more as a \"benchmark exposing existing model limitations\" rather than demonstrating its potential for \"enabling new model development\".\n\n2. The generated dataset relies on the structural quality generated by Boltz-1x. While the authors validated using PoseBusters with a low failure rate (~3%), complete dependence on a single generative model may introduce biases specific to that model. For instance, the model might underperform on specific protein families. It would be beneficial to explore these risks more thoroughly in the discussion.\n\n3. Fig. 4 shows that \"internal energy\" is the primary cause of PoseBusters validation failures. Does this suggest the generated conformations may not be physically reasonable?", "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": "UbO8aCAl0o", "reviewer": "Reviewer_rhF2", "summary": "The paper introduces **SAIR**, a million-scale synthetic structure–activity dataset ($\\approx$ 1.05M protein–ligand systems) built by curating bioactivity from ChEMBL and BindingDB, then generating 3D complexes using Boltz-1x co-folding. The curation removes entries with missing identifiers, validity flags, inequalities, and measurements outside a defined dynamic range; ligands are standardized (desalting, neutral-pH protonation, RDKit canonical SMILES). To avoid leakage from experimental structures, (UniProt, CCD) pairs present in PDB are excluded. The authors assess structural plausibility with PoseBusters, report overall low failure rates (only $\\approx$ 0.53% of complexes have all five poses failing), analyze confidence metrics (iPTM, interaction-PTM, complex iPLDDT) vs potency, and benchmark a small set of affinity predictors (Vina/Vinardo, OnionNet-2, AEV-PLIG).", "questions": "1. **Affinity aggregation strategy over five structures.** For each protein–ligand pair, do you report model performance per-structure, or do you aggregate (mean/min/best) across the five predicted structures?\n2. **Pocket diversity in main text.** I saw that there are no information about pocket diversity in the main text despite its detailed explanation is provided in appendix. In my opinion, high pocket diversity for several proteins may introduce unintended bias in data. I'd like to know about the authors opinion of this issue. If this is problematic, giving diversity of protein pocket and let the users handling about that issue themselves can be a solution.\n3. **Cofactors and tiny ligands.** How are cofactors/ions handled (e.g., heavy-atom count near 1, as described in table 3)? Are such entries filtered or annotated, and how do they affect pocket identification and affinity correlations?\n4. **About the degradation of performance of OnionNet-2.** In Fig. 6, OnionNet-2 attains only Pearson r $\\approx$ 0.35 on SAIR, whereas its reported performance on the CASF-2016 scoring set is r $\\approx$ 0.864. Given that OnionNet-2 relies primarily on protein–ligand contact patterns, this large gap suggests potential issues with pocket localization/contacts in the co-folded structures.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- **New dataset for proper demand.** If fully released, SAIR could be a valuable pretraining/analysis data source for structure-based binding affinity prediction models. Since the major limitation of training PLI models is lack of paired data, this approach is suitable in terms of its purpose and time.\n- **Scale and transparency.** Clear end-to-end pipeline from bioactivity ingestion to synthetic structure generation; explicit de-duplication and PDB-leakage control. As the authors stated, the computational cost for making this data is very expensive and thus this dataset is invaluable.\n- **Interesting diagnostics.** Introduction/usage of interface-focused confidence metrics (interaction-PTM, iPTM) and assay-aware correlation analyses (biochemical > cell).", "weaknesses": "Despite the strengths of the proposed dataset, my major concern is rooted in the fact that creating a synthetic structure-affinity dataset should minimize uncertainty in both structure and affinity.\n1. **More rigorous validation of predicted structure.** All complexes are treated as monomers with canonical UniProt sequences, despite many targets (e.g., GPCRs) being oligomeric or using non-canonical constructs. Recent studies show that co-folding models can predict plausible-looking yet physically inconsistent pockets/poses (including persistent ligand placement after pocket disruption), yielding false positives in downstream analyses. [1,2] Thus, I suggest that the authors include an additional assessment of pocket validity. For instance, they could examine whether the predicted pocket residues are consistent with experimentally validated binding sites observed in highly similar structures associated with proteins with similar sequence, especially for the proteins from rare protein family.\n2. **Potential distillation bias.** Because Boltz-1x uses deterministic MSA subsampling and results improve most on a high-confidence subset, the dataset may over-represent 'easy' (well-constrained/high-MSA or training-set–similar) proteins and under-represent rare/low-MSA targets. I would welcome analyses quantifying how confidence filtering shifts family/MSA composition and whether conclusions hold under family-balanced or MSA-balanced evaluations. I think this may results in making false positive data for rare protein family.\n3. **Confidence wording vs effect size.** In the page 7's first and second paragraphs, calling $r_{s}\\approx 0.25$ \"strong\" overstates the effect; the paper's own narrative elsewhere acknowledges low absolute correlations comparable to interface metrics.\n\n[1] Masters, Matthew R., Amr H. Mahmoud, and Markus A. Lill. \"Investigating whether deep learning models for co-folding learn the physics of protein-ligand interactions.\" _Nature Communications_ 16.1 (2025): 8854.\n[2] Škrinjar, Peter, et al. \"Have protein-ligand co-folding methods moved beyond memorisation?.\" _BioRxiv_ (2025): 2025-02.", "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": "1fvp7wtywo", "reviewer": "Reviewer_947p", "summary": "This paper introduces the Structurally Augmented IC50 Repository (SAIR), a dataset of over 1 million unique protein-ligand pairs with associated activity data curated from ChEMBL and BindingDB. The 3D structures for these complexes were computationally generated using the Boltz-1x co-folding model. The paper presents a characterization of the dataset, an evaluation of the structural quality using PoseBusters, and benchmarks for several binding affinity prediction models on the new data.", "questions": "No questions.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The dataset's size is a significant contribution that may contribute to addressing the data scarcity problem in structure-based modeling. The authors' commitment to making the dataset publicly available is highly commendable.\n2. The use of a standard validation tool (PoseBusters) to confirm the physical plausibility of the computationally generated structures adds credibility and provides users with confidence in the data's quality.\n3. The analysis correlating Boltz-1x confidence metrics (e.g., iPTM) with experimental binding affinity is a novel and interesting finding. This is timely, and it echos recent observations about the potential of AlphaFold-like models to rank binding affinities[1].\n\n[1] Hong X, Gao B, Jia Y, et al. How Good is AlphaFold3 at Ranking Drug Binding Affinities?[J]. bioRxiv, 2025: 2025.05. 27.656341.", "weaknesses": "**Major Concern**: \nThe use of PoseBusters is a commendable step for ensuring the physical and chemical plausibility of the generated structures. However, this validation does not—and cannot—confirm the biological correctness of the binding pose. Boltz-1x performs prediction from sequence, which is analogous to a \"blind docking\" experiment. This creates a significant risk that the model places a ligand in a physically plausible but biologically incorrect pocket, which would fundamentally mislead any downstream model. \n\nFor proteins with structures in PDB, I recommend performing structural alignments between their generated structures and known holo-structures to quantify pocket overlap and correctness. For the proteins without known structures—where the predictions are inherently more speculative as they fall outside Boltz's structural training domain—this increased uncertainty should be explicitly discussed. I recommend comparing the predicted pocket against annotated functional domains from UniProt or the known pockets of homologous proteins. Furthermore, some datasets constructed with template-docking methods (e.g., BindingNet v2[1]), which assume similar ligands bind in the same pocket and can give mostly correct pockets, should be more adequately discussed. \n\n**Minor Concern**:\nThe term \"assay\" appeared many times in this paper. However, in the bioactivity prediction field, an \"assay\" is often defined as a set of measurements for a single target conducted under a consistent experimental protocol (often corresponding to a unique ChEMBL assay ID). To avoid ambiguity, I recommend using the more precise term \"assay type\"(binding, functional, ADMET, ...), \"assay level\"(cell, target, ...), or \"measurement\"(IC50, Ki, Kd). However, I still strongly recommend providing the ChEMBL assay ID and organizing data in BindingDB by assays following BindingNet[1] and ActFound[2]. Data from different assays, even if they report the same \"assay type,\" can vary dramatically due to differences in experimental protocols (e.g., cell-based vs. biochemical), conditions (pH, temperature, cofactors), and instrumentation. Lumping these data together without distinction introduces severe batch effects, and there is also evidence for the benefit of considering \"assay\" in modeling [3].\n\n[1] Zhu H, Li X, Chen B, et al. Augmented BindingNet dataset for enhanced ligand binding pose predictions using deep learning[J]. npj Drug Discovery, 2025, 2(1): 1.\n[2] Feng B, Liu Z, Huang N, et al. A bioactivity foundation model using pairwise meta-learning[J]. Nature Machine Intelligence, 2024, 6(8): 962-974.\n[3] Feng B, Liu Z, Li H, et al. Hierarchical affinity landscape navigation through learning a shared pocket-ligand space[J]. Patterns, 2025, 6(10).", "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)