KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors
Authors: Benson Chen
Tomasz Danel
Gabriel H. S. Dreiman
Patrick J. McEnaney
Nikhil Jain
Kirill Novikov
Spurti Umesh Akki
Joshua L. Turnbull
Virja Atul Pandya
Boris P. Belotserkovskii
Jared Bryce Weaver
Ankita Biswas
Dat Nguyen
Kent Gorday
Mohammad Sultan
Nathaniel Stanley
Daniel M Whalen
Divya Kanichar
Christoph Klein
Emily Fox
R. Edward Watts
Venue: ICML 2025 poster (2025)
Keywords: DEL
small molecule
dataset
benchmark
drug discovery
Primary area: applications->chemistry_physics_and_earth_sciences
TL;DR: Dataset and benchmark paper for a 81 million small molecule DNA-encoded library to find hits for drug discovery

Abstract: DNA-Encoded Libraries (DELs) represent a transformative technology in drug discovery, facilitating the high-throughput exploration of vast chemical spaces. Despite their potential, the scarcity of publicly available DEL datasets presents a bottleneck for the advancement of machine learning methodologies in this domain. To address this gap, we introduce KinDEL, one of the largest publicly accessible DEL datasets and the first one that includes binding poses from molecular docking experiments. Focused on two kinases, Mitogen-Activated Protein Kinase 14 (MAPK14) and Discoidin Domain Receptor Tyrosine Kinase 1 (DDR1), KinDEL includes 81 million compounds, offering a rich resource for computational exploration. Additionally,  we provide comprehensive biophysical assay validation data, encompassing both on-DNA and off-DNA measurements, which we use to evaluate a suite of machine learning techniques, including novel structure-based probabilistic models. We hope that our benchmark, encompassing both 2D and 3D structures, will help advance the development of machine learning models for data-driven hit identification using DELs.

KinDEL: DNA-Edd Library Dataset for Kinase Inhibitors
Benson Chen Tomasz Danel"
Gabriel H. S. Dreiman' Patrick J. McEnaney' Nikhil Jain
Kirill Novikov
Spurti Umesh Akki
Joshua L. Turnbull'
Virja Atul Pandya Boris P. Belotserkovskii
Jared Bryce Weaver
Ankita Biswas"
Dat Nguyen'
Kent Gorday Mohammad Sultan Nathaniel Stanley'
Daniel M Whalen
Divya Kanichar'
Christoph Klein
Emily Fox! R. Edward Watts"
Abstract
DNA-Encoded Libraries (DELs) represent a trans- formative technology drug discovery, facilitat- ing the high-throughput exploration of vast chem- ical spaces. Despite their potential, the scarcity of publicly available DEL datasets presents bot- tleneck for the advancement of machine learning methodologies in this domain. To address this gap, we introduce KinDEL, one of the largest publicly accessible DEL datasets and the first one that includes binding poses from molecular docking experiments. Focused on two kinases, Eittogen-Activtted Protein Kinase 14 (MAPK14) and Discoidin Domain Receptor Tyrosine Kinase (DDR1), KinDEL includes 81 million com- pounds, offering a rich resource for computational exploration. Additionally, we provide comprehen- sive biophysical assay validation data, encompass- ing both on-DNA and off-DNA measurements, which we use to evaluate suite of machine learn- ing techniques, including novel structure-based probabilistic models. We hope that our bench- mark, encompassing both 2D and 3D structures, will help advance the development of machine learning models for data-driven hit identification using DELs.
Step
Step
Step C
MAM CAE
MAM CAn
OAD
Figure (Top) DELs are synthesized in a sequential man- ner; at every step, the DNA codon specifies the next building block (synthon) be attached. (Bottom) An example of fully synthesized DEL molecule, with the building blocks and codons colored-coded for visualizability. Typically, there is also linker that connects the molecule to the DNA, which here is 6-carbon chain.
synthesis of chemical building blocks, synthons, with each resulting molecule being assigned DNA barcode (see Figure 1). DELs are then used in selection experiments where they are mixed with proteins of interest bound to matrix. Multiple rounds of washing are conducted to re- move any weak binders, and the DNA tags of surviving molecules are sequenced as a measure of binding affinity. Data generated through these experiments are intrinsically noisy with various sources of bias arising from the DEL synthesis and selection processes, suggesting that modern machine learning methods may be needed to learn signal from the data. Unfortunately, there is still a lack of large, publicly available DEL datasets and benchmarking tasks to drive this important research area.
1. Introduction
DNA-Encoded Libraries (DEL) have emerged as powerful tool drug discovery, enabling highly efficient screens of small molecule libraries against therapeutically relevant tar- al., 2021; Kunig et al., 2021; Peterson & Liu, 2023). These massive libraries are efficiently constructed through combinatorial
gets (Yuen & Franzini, 2017; Gironda-Martiner
The growing interest in utilizing DEL data for modeling evidenced by the many recent efforts to advance this area (Iqbal et al., 2024; Blevins et al., 2024; Gu et al., 2024). One of the primary reasons for this interest is that selection experiments using DELs alleviate some of the data limita- tions typical the field; most chemistry problems in the machine learning domain lack consistent and sufficiently
'Insitro, South San Francisco, CA 94080, USA. Correspondence to: Benson Chen <benatorc@ caanattrr@gmail.coom gomi. Tomasz Danel <tomek@ @ insitro.com>..
Equual contribution
Proceedings of the ndnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors
high-quality labels. In particular, DELs can contain billions of compounds, and require fewer resources to run compared to more traditional high-throughput screens (Peterson & Liu, 2023). However, the process of DEL synthesis and selec- tion introduces various sources of bias that can add noise to the observed data. For instance, DEL experiments measure the affinity of molecules while they are attached to a DNA barcode (hence, on on-DNA binding). In contrast, during a real therapeutic campaign, drug-like molecules are tested with- out a DNA tag (off-DNA), meaning the DEL data is biased by the DNA and the molecule's s attachment to it. Moreover, uncertainties in reaction yields and biases in polymerase chain reaction (PCR) amplification add additional noise to the process. These difficulties have fueled an increasing enthusiasm within the community for developing structured computational models to more effectively interpret the data signals.
tasks demonstrate the efficacy of using DEL data in de- riving therapeutic insights. By releasing these benchmarks, we aim to facilitate the comparison of various modeling techniques currently applied to DELs. To seed these bench- marks, we survey computational methods from the liter- ature and build predictive models, validated through bio- physical data from compounds independently resynthesized both on- and off-DNA Since DEL data primarily captures on- on-DNA binding events, but our interest lies in off-DNA binding affinity, these additional data are crucial for as- sessing the models' generalizability to diverse biophysical data. Our studies show that models built on DEL data can effectively characterize both on- and off-DNA affinities, highlighting the usefulness of DEL data in drug discov- ery. We hope that KinDEL serves as a public resource that can facilitate the iterative refinement of chemical models by providing supervised data in densely sampled chemical spaces. Data and code for our benchmarks can be found at https: /t////gtttt/t//// cww http//iiitttooooo /kindel.
Despite these challenges, several efforts have demonstrated successful applications of machine learning to DEL data (MeCloskey et al., 2020). These early successes suggest that there still much to explore in this domain. Currently, most methods have focused purely on discriminative modeling, which plays crucial role in drug discovery campaigns by ranking prospective compounds in order to select potential hits. These prediction methods typically utilize established architectures, building upon both molecular fingerprints and more sophisticated graph convolutional networks (Duve- naud et al., 2015). More recently, efforts have been made to pose the problem in a probabilistic manner, directly in- corporating experimental uncertainty in the model structure (Chen et al., 2024). These models leverage the fact that, while individual data points may be noisy, it is generally expected that groups of molecules with the same synthons or substructures will show enrichment, or signal, when ana- lyzed collectively. Additionally, DEL data can be employed for generative modeling, providing weak supervision to nav- igate the complex chemical landscape, which is valuable for lead optimization steps of drug discovery.
2. Dataset
We first introduce high level summary of the dataset gen- eration, and then provide overview of the data that we publicly release. The data generation is divided into roughly three experimental processes, which are the synthesis of the DEL, the subsequent selection experiments against proteins of interest, and the biophysical assays to collect validation data. Specific experimental details can be found in Ap- pendix A.
2.1. Data Generation
DEL Synthesis The DEL is built as a trisynthon library with 378 synthons in the A position, 1128 synthons in the B position, and 191 synthons in the C position. The synthesis is sequential process, with each synthon specified by the DNA tag and added one at a time. Notably, each molecule does not have only a single encoding; instead, multiple en- codings of DNA map to the same final molecular structure. These redundant encodings help mitigate potential biases during the subsequent DNA sequencing step. Rather than counting the total number of amplified DNA associated with single molecule (which is subject to PCR noise), we count the number of different redundant encodings observed for each molecule. This library was designed to enhance scaf- fold uniqueness and chemical diversity, thereby exploring a broader region of chemical space. A simplified diagram of synthesis can be found in Figure 1.
To demonstrate the advantages of DEL data and promote development of the methods described above, we release Library) as li-
KinDEL (Kinase Inhibitor brary of 81 million small molecules tested against two kinase targets, MAPK14 14 and DDRI. Our dataset, distin- guished by its high consistency across experimental repli- cates (see Appendix B), provides large amount of super- vised data for the machine learning community to develop methods for solving small molecule chemistry problems in drug discovery.
DEL Selectio DEL Selection was performed by combining the DEL with the target proteins, which were immobilized on beads, as well as a negative control without any protein. Multiple rounds of washes removed any weak binders in the solution. The molecules were then extracted via elution,
In addition to the KinDEL dataset, we provide a set of benchmark tasks validated using biophysical assay data, which we also release publicly. A major challenge in driv- ing research in this area has been the dearth of benchmark
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KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors
MMM OAN
MNRO
Protein Capture
DEL Binding
MMM oAE
OAU
Input
AMR MAN DEL
ID
A
B
C
SMILES
Streptavidin
MAD MAn avRo AAR
OAE
2
OAE
Protein
3
OAB
Washing
MSA ARRO ASRO
Panning
Labels
ID Pre
MAPK14 DDRI
Matrix
1
2
AARO
Read Counts
Sequencing
Elution
3
(a) Panning experiment
(b) Data structure
Figure 2: KinDEL data acquisition process. (a) DEL selection experiments are conducted by combining the DEL with the protein target of interest immobilized onto Streptavidin beads. After multiple rounds of washing, the tight binders are eluted off of the protein, and their corresponding DNA are sequenced obtain the count data. (b) Molecules in the dataset represented separate synthons A, B, and C and the combined product. The labels for each compound include the pre-selection counts measured before panning and read counts for the targets and the matrix measured in triplicate.
and the subsequent samples were then amplified via PCR and sequenced to obtain the count data. Each selection was performed in triplicate with each protein. This process is visualized Figure 2.
bead) can be captured. In our dataset we provide three (3) replicates of data for each of the control and the protein target conditions. As mentioned earlier, raw DNA counts can be noisy due to PCR amplification bias (Aird al., 2011; Kebschull Zador, 2015), so use the sequence count information data, which measures the number of unique DNA sequences observed for each molecule.
Biophysical Assay Validation Additional biophysical data small subset of molecules collected both and off-DNA (see Appendix C for more details on how these molecules were selected), For on-DNA data, we use Flu- orescence Polarization (FP), which utilizes polarized light to quantify binding affinity by measuring the dissociation constant (K p). For off-DNA data, we use Surface Plasmon Resonance (SPR), which also relies on light measure the dissociation constant. The reason collect both types of data is because oN-DNA KD data reveals insights about the actual binding of the molecule in the DEL selection exper- iment, while ofN- Kn focuses on interactions of the molecules without DNA barcode, which the relevant setting for an actual drug candidate.
Pre-selection Data Additionally, we provide data about the sequenced library itself, called pre-selection data, which provides a rough estimate of the relative abundance each molecule prior to any experimental run. During the syn- thesis of the DNA-encoded library, not all molecules produced in equal amounts. Variability arise from differ- ences in synthesis efficiency, coupling yields, or synthesis errors. Additionally, some molecules may precipitate or adhere to storage containers, resulting in loss and lower ob- served counts. Due to the size of the library, it is too costly to sequence the library deeply enough for an extremely ac- curate pre-selection estimate. However, because there is always some amount of synthesis noise, for instance ing impure reactants or having incomplete reaction yields, achieving a precise measurement of the pre-selection data is not imperative. While this data is typically used to nor- malize counts, sequencing inaccuracies can lead to error propagation, especially for low-abundance molecules.
2.2. Data Overview
DEL Data KinDEL contains count data for more than 81M unique molecules used in selection experiments with two proteins MAPK14 and DDRI. Typically, DEL experiments with negative control without the protein, so that non-specific binding events (such binding directly to
3

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KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors
150
150
0.2
0.5 QED
0.8
700 Molecular Weight
300
500
5 ALogP
0
50
20
Symtho
100 200
Synthon B
Synthon
100
200
Syuthon B
(a) MAPK14
(b) DDRI
4 # Aromatic Rings
1
0
10 # Rotatable Bonds
15
100
200
Polar Surface Area
Figure The 3D cube visualization of the dataset, where each axis corresponds
different synthon in the DEL. Points in the plot are the most enriched compounds (mea- sured using Poisson enrichment). The linear patterns can be interpreted as enriched disynthons, i.e. combinations of two synthons that often bind to the protein target.
0.0
1.0
9 11
3 5
Fraction of sp Carbons # H-BBnd Acceptors
# H-Bond Donors
Figure The distributions of chemical properties in the KinDEL dataset. These selected properties are often used to assess the druglikeness of molecules. The light blue areas mark the 10th and 90th percentiles computed for all the FDA approved oral new chemical entities, as reported by Shultz (2018). QED: quantitative estimate of druglikeness (Bickerton et al., 2012).
and compare their performance in modeling binding affinity, In this benchmark, all models were trained using the top IM compounds with the highest counts from our KinDEL dataset. We publish the full library to enable construction of further benchmarks.
Held-out Test Set The observed count data in DEL exper- iments an approximation of the true on-DNA binding affinity (K)). The count data are influenced by multiple sources of noise (see Section 4). We ultimately wish to rank molecules by binding affinity, so we use compounds with measured KD (from biophysical assays) as a test set. Performance on these compounds assesses if the models correctly rank compounds by Kp. This can be viewed as measuring how well models can remove the noise inherent to DELs.
Biophysical Assay Data We have collected data from 30- 50 molecules using the aforementioned biophysical assays, FP and SPR, to validate our models. For molecules on- DNA, we have resynthesized molecules both from within and from outside our library. For off-DNA compounds, we have only resynthesized molecules that within the DEL itself. These molecules were primarily selected from the top hits predicted from models trained on the DEL data.
For both our targets, MAPK14 and DDR1, the selected compounds contained in the DEL library were resynthesized on- and off-DNA to create an in mn-ibrarr held-out test set. For hit finding, we would like to be able to predict off-DNA K KD- This is challenging because the DEL data comes from DNA bound molecules, and is biased by the DNA. The oN-DNA Kp more closely aligns with DEL data since the molecules in the training data are bound to the same DNA in the same way. A few additional compounds were added from outside the library (and tagged with DNA) to create an additional held-out test set that we refer to as "Extended". The Kp data from these biophysical assays are also released with our dataset. A UMAP visualization of the DEL including the in-library and external test set compounds is depicted in Figure 5.
Figure 3 illustrates various properties of the molecules in our dataset, comparing them those of typical drug-like molecules. From this, we can see that KinDEL well- posed within typical drug-like distributions according to an analysis by (Shultz, 2018). Notably, over 30% of the molecules in our library fall within the property ranges of already approved drugs, as outlined by Schultz. While certain synthon combinations may result in compounds that fall outside these preferred ranges, DEL molecules primarily serve to provide initial hits for drug discovery campaigns. These initial hits undergo iterative refinement during the hit-to-lead optimization process.
3. Benchmarking
3.1. Experimental Setup
Data Splits We split our datasets using three strategies, ensuring that all held-out compounds are placed in the test set and not used for training. The first type of data split is a random split, where randomly selected 10% of compounds
One primary use case of DEL data is building predictive models of binding affinity. To that end, we investigate com- monly used models in DEL literature as benchmark models

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KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors
library beld-out external
are trained with different losses, this is consistent way to compare model performance. Here, we use Spearman correlation, because we are primarily concerned with the ability of a model to rank order molecules by their binding affinity.
3.2. Benchmark Models
In this benchmark, we compare models commonly used for DEL data in the literature. These methods follow a similar paradigm, in which the models try to learn the protein bind- ing signal in the target data by subtracting out noise from the control data. Two baselines are included gauge the alignment in the actual data, between the DEL counts and validation KD values. The first baseline, count enrichment, computes an enrichment score by subtracting the average control counts from the average target counts. The second baseline, Poisson enrichment, computes a ratio of fitted Poisson distributions of the target and control data (Gerry et al., 2019).
Figure 5: UMAP visualization of KinDEL constructed using Tanimoto distances between compounds. The compounds selected for the held-out testing set are depicted as orange diamonds (in-library) and green triangles (external).
testing compound
Next, we compare ML models trained on the data pre- dict the aforementioned Poisson enrichment using mean- squared error (MSE) loss. These models include: random forest (RF) (Breiman, 2001), XGBoost (Chen & Guestrin, 2016), k-nearest neighbors (kNN) (Fix & Hodges, 1989), and deep neural network (DNN) using Morgan finger- prints (radius=2, length=2048) (Rogers & Hahn, 2010) from RDKit (Landrum, 2010) input features. We also tested two molecular graph models, Graph Isomrrphism Network (GIN) (Xu et al., 2018) 8) and Chemprop DMPNN (Heid et al., 2023). DEL-Compose (Chen et al., 2024) is a probabilistic model that uses Morgan fingerprints as input and predicts the parameters of a zero-inflated Poisson distribution to max- imize the likelihood of the observed count data. We train two variants of DEL-Compose, one with only full molecule structures (DEL-Compose (M)), and one with synthon struc- tures IDEL-Compose ())) The hyperparameters of all the models used in this study are presented in Appendix D.
Syation A Syndlen B
Synthoo A
Synihon
Synthur Synthon 8
random split
cluster split
disynthon split
Figure 6: Three data splitting strategies used to construct the testing set.
are placed in the validation set, and another randomly se- lected 10% are placed in the test set. The second type of data split is a disynthon split, where we sample disynthon structures (molecules with the same 2 synthons), and put all compounds containing these sampled structures in the same subset using the same 80- 80-10-10 10-10-100 ratio between the training, validation, and test sets. This data partitioning method is more challenging for the machine-lcarning models because some synthon combinations tend have a binding profile distinet from the individual synthons they consist of (see Figure 4). The third approach is a cluster split based compound similarity. The details about the clustering algo- rithm can be found in Appendix E. The types of data splits are illustrated in Figure 6. Each dataset is split five times for each splitting strategy, and the reported performance of the models is aggregated over five training runs.
The architectures of the neural network models follow the implementation the original publications. The DNN archi- tecture contains multiple linear layers with ReLU activation, batch normalization, and dropouts after each layer except for the last one (see Appendix D) All neural networks were trained using the Adam optimizer until convergence with early stopping when the validation loss does not improve for more than 5 epochs.
3.3. Benchmark Results
Evaluation Metries Models trained DEL data typi- cally predict an enrichment score, which can be regarded as a measure of binding affinity. To evaluate different base- lines, we compare how well each method's predicted enrich- ment scores correlate with experimental KD values for the molecules in the held-out test set. Since different models
Tables I and 2 show the performance of various models on MAPK 14 and DDR1, respectively. The number of data points (n) in each held-out test set is also reported in the tables. The Counts and Poisson enrichment baselines serve estimate of the alignment between DEL screening
5

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KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors
Table I: Model performance evaluation for MAPK14. The test loss column contains values of the loss function computed on test split. The performance for the on- and off-DNA "In-Library" is the negative Spearman correlation between model predictions and experimental KD D for compounds resynthesized from the DEL The performance for on-DNA "Extended" set includes additional compounds resynthesized on-DNA but not in the original DEL.
In-Library
Extended
on-DNA
off-DNA Spearman's
on-DNA Spearman's p 41
Spearman's p
Split
Model
Test Loss L
30
33
Counts
0.778
0.353
Poisson
0.737
0.166
RF
0.064 + 0.003
0.694 + 0.030
0.370 + 0.111
0.453 + 0.028
XGBoost
0.056 + 0.002
0.477 + 0.009
0.345 = 0.036
0.196:100 0.074
MSE
random
KNN
0.072 + 0.002
0.649 1 0.041
0.466 = 0.103
1 0.464 0.040
DNN
0.139 + 0,010
0.582 0.062
0.514 = 0.071
0.351 + 0.058
GIN
0.062 0.004
0.511 0.038
= 0.492 0.139
0.174
Chemprop
0.121 + 0.008
0.693 0.039
0.504 = 0.093
0.462
DEL-Compose(A))
NLL
3,017 + 0.005
0.448 1 0.054
0.756 = 0.011
0.569 1 0.048
DEL- Composee (5)
3.192 + 0.167
0.420 0.050
0.760 = 0.018
RF
0.063 + 0.015
0.697 0.033
0.313 = 0.071
0.472 + 0.048
XGBoost
0.059 + 0.013
0,486 0.032
0.378 = 0.091
0.176 = 0.069
MSE
cluster
KNN
0.080 + 0.018
0
0.435 = 0.094
0.421 = 0.032
DNN
0.065 1 0.011
0.565 1 0.089
0.592 = 0.065
0.284 1 0.114
GIN
0.072 + 0.015
0.411 0.209
0.369 = 0.054
0.171
Chemprop
0.131 + 0.047
0.713 0.013
0.532 = 0.070
DEL-Compose(Ar)
MSE NLL
3.038 1 0.053
0.440 1 0.045
0.730 = 0.009
1
DEL-Compose (5)
3.339 + 0.114
0.369 + 0.050
0.766 = 0.012
RF
0.154 1 0.016
0.157 0.138
0.505 = 0.062
0.302 1 0.096
XGBoost
0.148 + 0.015
0.377 + 0.054
0.482 = 0.045
0.212 = 0.126
disynthon
KNN
0.165 + 0.014
0.402 0.074
0.266 = 0.078
0.367 + 0.043
DNN
0.160 0.017
0.275 0.135
0.429 = 0.118
0.184 0.146
GIN
0.153 + 0.011
0.090 0.084
0.483 = 0.151
-0.080 + 0.071
Chemprop
0,216 + 0.007
+
0.506 = 0.093
0.228 + 0.121
(
NLL
3.177 1 0.028
0
0.716 = 0.052
0.421 1 0.054
DEL (S))oo)o
3.351 + 0.040
0.128 + 0.049
0.748 = 0.024
results and experimental KD computed directly from the sequence count data, measuring how well the DEL data itself predicts the Kp in the follow-up assays. For DDR1, 1, we see that DEL-Compose, which views the data from a probabilistic perspective, is the most performant model in all but the "Extended on- "n-D"AA set. For MAPKI 14, relatively simple models (RF and kNN) perform best for the in-library on-DNA validation while DEL-Compose performs better off-DNA. Recall that RF- is the task more reflective of the setting for selecting actual drug candidates. Interestingly, we find that DEL-Compose rank orders the validation com- pounds off-DNA better than using enrichment metrics of the DEL data itself (Counts, Poisson). This is true for both targets, and on all three splits of the data. As mentioned ear- lier, DEL data is only indirectly correlated to off-DNA data, so this demonstrates that these structure-based models may have regularization properties that can denoise the DEL data. Most models perform similarly in the cluster split compared to the random split, indicating that the cluster split created based on fingerprint cluster not too much more challeng- ing. However, we observe that almost all models perform
worse on the disynthon split compared to the random split. In particular, this change in performance is quite signifi- cant for the MAPK14 data, which might suggest that these models are overfitting to certain features. The disynthon split is a more challenging task, since we remove structures entirely from the training data, and the models have to infer based on chemical structures (out-of-distribune Overall, these results demonstrate that models trained on DEL data can be used for hit selection, since models can predict enrichment scores that correlate well with on- and off-DNA biophysical data.
inference).
4. Discussion
Data Applicability and Limitations Our data and bench- marking tasks were designed to evaluate the ability pre- dict compounds within the DEL. Although we incorporated challenging splits of the data, such as disynthon splits, we have not evaluated the usefulness of this particular data for truly extended held-out set with molecules from outside of the
sets. While we do include an
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KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors
Table 2: Model performance evaluation for DDRI. The test loss column contains values of the loss function computed on test split. The performance for the on- and off-DNA "In-Library" is the negative Spearman correlation between model predictions and experimental KD for compounds resynthesized from the DEL The performance for on-DNA "Extended" set includes additional compounds resynthesized on-DNA but not in the original DEL.
In-Library
Extended on-DNA
on-DNA
off-DNA Spearman's P
Spearman's p
Spearman's p
Split
Model
Test Loss L
39
49
n 54
Counts
0.695
0.355
Poisson
0.779
0.441
RF
0.685 + 0.011
0.578 + 0.034
0.267 + 0.022
0.608 + 0.021
XGBoost
0.519 0.011
0.553 + 0.032
0.252 = 0.031
0.587
MSE
random
KNN
0.748 + 0.010
0.59
0.316 = 0.026
1.508
DNN
1,261 + 0.057
0.703
0.335 = 0.009
GIN
#
0
0.283 = 0.028
0.039
Chemprop
1.391 + 0.043
0.729 + 0.017
0.335 = 0.004
DEL-Compose(A))
NLL
2.873 1 0.003
0.731 1 0.016
0.509 = 0.024
0.646 0.024
DEL- CompCoo (5)
2.918 + 0.057
0.689 + 0.048
0.483 = 0.044
RF
0.641 + 0.190
0.586 + 0.025
0.273 = 0.017
0.155
XGBoost
0.589 + 0.164
0.569 + 0.024
0.262 = 0.009
0.599 0.013
MSE
cluster
KNN
0.887 = 0.189
0.581 1 0.061
0.329 = 0.063
0.489 0.049
DNN
0.519 + 0.150
0.708 1 0.015
0.330 = 0.022
0.673 0.014
GIN
0.633 + 0.142
0.524 0.081
0.137 = 0.047
0.599 + 0.047
Chemprop
1.507 + 0.363
0.732 0.028
0.326 = 0.014
0.690
DEL-Compose(A))
NLL MSE NLL
2.891 + 0.079
0.737 1 0.041
0.467 = 0.025
0.540
DEL-Compose (5)
2,993 + 0.065
0.686 + 0.027
0.482 = 0.019
RF
1,151 + 0.151
0.481 0.120 0.120
0.330 = 0.081
0.557 0.082
XGBoost
0,989 + 0.131
0.523 + 0.071
0.241 = 0.031
0,572 + 0.046 0.046
disynthon
KNN
1.109 + 0.088
0.663 1 0.043
0.363 = 0.038
0.523 + 0.036
DNN
0.977 + 0.104
0.572 0.063
0.265 = 0.051
0.598 0.055
GIN
0.966 + 0.090
0.410 0.020
0.070 = 0.031
0.023 0.546 = 0.023
Chemprop
1.690 + 0.192
0,558 + 0.061
0,310 = 0.038
0.579 0.037
(
3.184 + 0.025
0.663 1 0.022
0.463 = 0.023
0.492 - 0.049
DEI DEL-Compose
3.1 + 0.041
0.563 + 0.084
0.429 = 0.069
DEL, this extended set is limited in size. We envision that models trained on this data be used to make predic- tions for molecules from purchasable catalogues, but such applications will require further exploration, especially be- ing cognizant of problems in this space such as domain of applicability (Weaver & Gleeson, 2008).
achieved by running the experiment with known inhibitor doped in at high concentrations. Because data lacks this condition, it can be difficult to discem non-specific binding modes. We also recognize that one limitation of the dataset is that the two targets we provide are kinases. In order to make this dataset more broadly applicable for modeling DEL data, we additionally release our data on another well studied target, Bovine Carbonic Anhydrase (BCA), using the same library.
One limitation of DEL data is that it only measures on- DNA binding events, and on- and off-DNA binding are only loosely correlated (Hackler et al., 2019). While we have shown in our experiments that machine learning models can have nice regularization properties and learn some correla- tions to off-NNA binding, DEL data needs to be combined with additional 3-D structural data to fully understand these different binding modalities. To that end, we also publicly release docked 3-D poses of our library molecules to the target proteins to aid future model development by the com- munity (see Appendix F). Additionally, DEL screens are sometimes run with an additional experimental condition to distinguish potential allosteric binders from orthosteric binders (Gironda- EGrronda-Martinez et al., 2021). This usually
Challenges and Future Directions DEL data is powerful in that it specifically densely samples particular chemical spaces, which can be leveraged to learn more powerful rep- resentations. However, DEL data suffers from experimental noise. In particular, there are unobserved factors such as synthesis noise that makes it difficult to separate out sig- nal from noise in the data (Zhu et al., 2021). Additionally, since our observations are sequencing read counts rather than actual binding affinity, the measurements also suffer from PCR bias (Aird et al., 2011). While we have presented several benchmark methods that try to learn a denoised

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KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors
enrichment from structure-based models, how best do this is still open question in the field, and we hope that our dataset release will enable the development of more denoising methods.
oxidase and members of the Carbonic Anhydrase family, In both cases, the DELs were synthesized with specific chemotypes known to bind to their targets. As a result, evaluation in each is primarily limited to comparing rela- tive enrichment for compounds containing known binders. These papers serve as excellent case-studies but with their targeted library construction and limited library size ~ 100k (Gerry al., 2019) and ~7M (Hou et al., 2023), they are unlikely to serve as generalizable benchmarks for DEL ML. methods.
Furthermore, our benchmark primarily focuses on build- ing purely predictive models, which encapsulates most of the published work in this field currently. However, KinDEL holds significant potential for use in generative frameworks. Many generative modeling approaches in the moall-molecule space lack sufficient supervised data to learn interesting sampling distributions. However, this is exactly the data that DEL. provides, densely sampling around partic- ular synthons or di di-synthon structures. Therefore, we hope that DEL data combined with provided docking poses can be used train or fine-tune generative models on specific protein targets.
The AIRCHECK database facilitates the public release of large-scale DEL datasets, notably including a 3-billion- molecule library screened against WDR91 (Wellnitz et al., 2024). However, chemical structures are masked with finger- prints in these commercial-sine libraries, which inherently limits the range of applicable modeling approaches.
5. Related Work
5.2. Computational Methods on DEL Data
Computational efforts on DEL data have evolved over time. One of the major concerns with DEL data is its intrinstic noisiness. Firstly, the synthesis of a DEL is optimized for scale, and not precision, which results in uncertainty in the composition of the library. For instance, DEL synthesis will result in forming partial or truncated products (Gironda- Martínez et al., 2021; Binder et al., 2022), which has some causal effect on the observed data that cannot easily be mea- sured. Additionally, the final data measurement is obtained by sequencing PCR-amplified DNA barcodes; however, the PCR process itself does not uniformly sample from the sur- viving members of the selection experiment (Aird et al., 2011).
5.1. Current datasets
To the best of the authors' knowledge, only a limited num- ber of DEL datasets have been released publicly. While KinDEL is not the largest library tested, our dataset is evaluated on two distinct targets in the Kinase family and contains comprehensive and consistently replicated raw perimental data (see Appendix B) in addition to orthogonal, non-DEL based binding affinity data for validation.
Iqbal et al. (2024) released three DEL datasets tested against two targets, Casein Kinase 1a/5. Their libraries span a range of chemical size and diversity, and they demonstrated the efficacy of suite of machine learning methods to model binding affinities. Like our work, they experimentally val- idated some of their machine learning derived molecule predictions with biophysical assay data. However, they only tested off-DNA binding affinities, whereas we have synthe- sized our compounds both on- and off-DNA. Having both kinds of data is important because on- on-DNA data bridges the gap between DEL data and off-DNA data. Addition- ally, they have not investigated probabilistic models in their benchmarking tasks, which we find to be empirically useful.
Computational methodology for DEL data is still nascent area, largely due a lack of publicly available data. Current works typically directly apply fingerprint and graph neural network approaches that are popular within property predic- tion models. Due to the noisiness intrinsic to the data, many methods bin the labeled data into binary classification to avoid overfitting on the raw data. For instance, McCloskey et al. (2020) trained classification models on disynthons us- ing variants of typical graph neural networks. This approach was applied by Ahmad et al. (2023) to find first-in-class WD Repeat Domain 91 ligands, using molecules from buyable catalogues. Torng et al. (2023) follows up on this work and uses Weave GCN for hit identification of CA-IX. However, the true binding process is observed on a discrete scale, so more recent works have focused on building probabilis- tic models of binding. Lim et al. (2022) proposed new uncertainty- loss function for training regression mod- els, and demonstrated the efficacy of their method using various graph neural network models. (Shmilovich et al., 2023) takes this process one step further and incorporates 3-D docked poses to leverage the scale of DEL data by us-
Another recent dataset release is from Leash Bio, who re- leased their data as a Kaggle competition (Blevins et al., 2024). Their dataset has single DEL screened against on three different proteins, but their data has been preprocessed from raw count to binary label. This process is often used to denoise DEL data, but it removes information from the data. We show through our experiments that it is possi- ble to learn over discrete count data through probabilistic approaches.
Gerry et al. (2019) and Hou et al. (2023) have released libraries tested against well-studied targets Horseradish Per-
8

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KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors
ing multiple-instance learning learn over poses. Other works exploit compositionality of DELs inductive biases of the models. (Binder et al., 2022) tries to explicitly model the partial products, while (Chen et al., 2024) computes hierarchical representation of DEL molecules and incorpo- rates a probabilistic loss. (Koziarski et al., 2024) explores generative models of DEL molecules using GelswNets. Many of these developments are relatively new, and we hope that KinDEL will enable the further development of these methodologies in the field.
Domínguez, M., Eastwood, P., Esteve, C.,, González, J., Lumeras, W., Orellana, A., al. Novel triazolopy- ridylbenzamides potent and selective p38a inhibitors. Bioorganic & medicinal chemistry letters, 22(10):3431- 3436, 2012.
Aird, D., Ross, M. G., Chen, W.-S., Danielsson, M., Fennell, T., Russ, C., Jaffe, D. B., Nusbaum, C., and Gnirke, A. Analyzing and minimizing per amplification bias in illumina sequencing libraries. Genome biology, 12:1-14, 2011.
6. Conclusion
Bemis, G. W. and Murcko, M. A. The properties of
known drugs, 1. molecular frameworks. Journal of Medicinal Chemistry, 39(15)22887-288 10.1021//m9900222.. URL https::/dde //t::///oioooo/ /ttp:////o...oo...///// org/10. 1021/ PMID: 8709122.
DELs have emerged as a high-throughput technology that enables screenings of large combinatorial small molecule libraries. However, DEL data has many sources of intrinsic noise stemming from synthesis and selection experiments, necessitating the right machine learning tools to extract the correct signal in the data. We introduce KinDEL as a 81M molecule dataset with selection data from two tar- gets, MAPK14 14 and DDRI, in order to highlight the abil- ity to model DEL data to find potent binders. Our DEL is built with chemical diversity in mind, and many of the molecules in the library have properties within the range of approved drugs. We additionally release biophysical data for molecules synthesized both on- and off- to validate our models trained on DEL data. We hope that our public data release and benchmarking will engender more interest in DEL data as an important chemical modality for machine leamning research in the future.
1996. doi:
Bickerton, G. R., Paolini, G. V., Besnard, J., Muresan, S., and Hopkins, A. L. Quantifying the chemical beauty of drugs. Nature chemistry, 4(2):90-98, 2012.
Binder, P., Lawler, M., Grady, L.., Carlson, N., Leelananda, S., Belyanskaya, S., Franklin, J., Tilmans, N., and Palacci, H. Partial product aware machine learning on DNA- encoded libraries. arXiv preprint arXiv:2205.08020 2022.
Blevins, A., Quigley, I. K., Halverson, B. J., Wilkin- son, N., Levin, R. S., Pulapaka, A., Reade, W., and Howard, A. Neurips 2024 predict new medicines with BELKA, 2024. URL https: /tt:://agggg /ttg//aaglleeec com/ www.oonnnnssaaaa...naae.e
Impact Statement
Breiman, L. Random forests. 45(1):5 2001. doi: 10. 1023/A: 1013//:10109933444324
The broader impact of this work includes the potential to accelerate the identification of novel therapies, ultimately benefiting public health. However, as with many datasets in the chemical and biological sciences, there a poten- tial risk of misuse, such as designing harmful substances. While there are inherent societal implications with advanc- ing drug discovery, we do not anticipate any specific ethical concerns that must be highlighted beyond the established consequences of advancing machine learning techniques in this domain.
Butina, D. Unsupervised data base clustering based on daylight's fingerprint and tanimoto similarity: A fast and automated way to cluster small and large data sets. Jour- nal of Chemical Information and Computer Sciences, 39(4):747-750, 1999. doi: 10.1021/ci9803381. URL https: / http//oiiiooooo orgiiorrg/100 /00
Chen, B., Sultan, M. M., and Karaletsos, T. Composi- tional deep probabilistic models of DNA-encoded li- braries. Journal of Chemical Information and Modeling, 64(4):1123-113) 2024.
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KinDEL: ENA-ncoded Library Dataset for Kinase Inhibitors
A. Experimental Protocols
A.1. DEL Synthesis
Library Design The design of DNA-encoded libraries (DELs) often grapples with limited diversity and availability of bifunctional building blocks compared to their monofunctional counterparts. In order to expand structural diversity of the first two synthons of three-step linear library, we implemented hybrid design that combines trifunctional and monofunctional building blocks within one step of synthesis, to form bifunctional synthon, by using DEL synthesis on solid supports. This strategy significantly expands the chemical space and diversity of our DELs, offering a robust pathway to discovering novel compounds with enhanced biological activity.
Library Build The DEL is built as a trisynthon library, comprised of 378 synthons in the A position, 1 1128 synthons in the position (the terminal, capping step), and 191 synthons in the C position. This resulted in a DNA encoded library comprised of roughly 81 million unique members. The first two steps are done either by acylation with N-protected (Boc or Fmoc) amino acid, followed by deprotection, or by immobilization of the DNA to a solid support (DEAE sepharose resin) followed by series of chemical transformations: acylation with trifunctional building block (Boc-amino acid with protected side chain: Fmoc-amine, ester or ketone), deprotection (if necessary) of the side chain protecting group, reaction (amide coupling, reductive amination/alkkllttoon with the monofunctional building block (amine, aldehyde or acid), and lastly eluting the DNA off the DEAE sepharose and cleavage of the Boc group. In the final step, the downstream amino groups were reacted with monofunctional acids or aldehydes. Each of the steps described herein is encoded by the attached DNA strand allowing for ready deconvolution of the sequence of chemical steps taken for any given member.
A.2. DEL Selection
The DEL selection was performed using an Agilent Bravo for all handling steps. To immobilize the protein nanomole of the protein of interest (Avi-tagged MAPKI4 14 or DDRI). were incubated through gentle aspiration through streptavidin-coated beads housed within PhyTips (Cat. H-TV-92-20-05). This was performed in technical triplicate for each protein. Following immobilization, any unbound proteins were washed away using Buffer A (45 mM HEPES, 45 mM Tris-HCI, 150 mM NaCI, 5 mM MgCI2, pH 8.1).
Subsequently, the immobilized proteins were incubated through gentle aspiration and dispension over a period of 30 minutes with the Naive DEL library (at 500K copies of each member) in solution of Buffer A supplemented with 0.1 mg/ml sheared salmon sperm DNA to minimize non non-specific binding. To eliminate noise and unbound library compounds, the Phynexus tips were washed six independent times with Buffer B (45 mM HEPES, 45 mM Tris-HCI, 425 mM NaCI, mM MgCl2, pH 8.1, 0.03% Tween).
Post- Pashing procedure. Any compounds remaining (the binders) were eluted by 5x aspiration through the tip of hot water at C The material eluted is termed 'Elution Round I'. Following this the used Phytips were disposed, and fresh streptavidin Phy were used to capture a fresh nanomole of avi-tagged protein of interest, and the process described above repeated, but, the Naive DEL Library is instead the 90% of the eluent from Round (Elution Round 1) as the source of library. This procedure is repeated a final time with the Elution Round 2 sample to generate an Elution Round sample.
From the 10% of eluates conserved from round 2 and the round 3 eluate were taken 5 JL of sample that was subsequently PCR amplified in 50 pl. reactions using New England Biol Labs 2x Q5 mastermix (Cat. #M0492S) and custom designed barcode primers to differentiate both the round, target and experiment. Following this μL of the amplified material is transferred to a 25 μL PCR reaction with Q5 and custom P5 and P7 primers to install the relevant sequencing primers. The PCR mixture is purified using a QIAquick PCR Purification Kit (Cat. #281 104), this sample is then further purified to remove any DNA products of incorrect size using a PippenHT. The purified samples are submitted to next-generatie sequencing utilizing the Novaseq platform with a 2x150 BP S4 kit. The sequencing depth (number of read counts/sequence counts) is 514.5M/65.8M for MAPKI4, 296.5M/49.8 8M for DDR1, and 440.7M/52M for BCA in all three replicates.
A.3. Biophysical Assays
1.3.1. MOLECULAR BIOLOGY
hMAPK14-1 (1-360, WT) gene was cloned into PET-28a expression vector (Novagen) by polymerase chain reaction (PCR). An N-terminal His-TEV-Avi tag was added by using the forward primer 5'- and the reverse primer 5'-
CTTAGGAAGGGGAACCCCCCCCATTTCCCCCC
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KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors
The forward primer included the restriction site Ncol. The hMAPK (1-360, WT) gene was cloned using the forward primer
TTCGTGGCCTTTCAATTTCCCC
The reverse primer included the restriction site Xhol. This generated plasmid pET28a-HHs Caee---iHTTT--VAE -is---ii-ii-----Ae TEV-avi-hMAPP1441 (1-360, WT).
and the reverse primer 5'
1--PK14-2 (1-360, WT) gene was cloned into pETT-28a expression vector (Novagen) by PCR. An N-terminal His-TEV tag was added by using the forward primer SCTTTACAAAGGAATTTCCCCCCCCCACACCC 3' and the reverse primer ":CCCCTGGAAATTACAGGTTTCC"' the restriction site Ncol. The hMAPK14 (1-360, WT) gene was cloned using the forward primer and the reverse primer 5'- The reverse primer included the restriction site Xhol. This generated plasmid PETT28- -EV-MMAA-- (1-360, WT).
The forward primer included
5'
hDDR1-1 (593-913, A730-735) gene was cloned into PFastBac1 expression vector by PCR. An N-terminal His-TEV tag was added by using the forward primer
3' and the reverse primer "-AACCTGGGAATTTAAGGGTTTT: AAC.AAACCCCCCCCGGTTTTTCCCCCCCCCC
The forward primer included the re- striction site EcoRI. The hDDRI-1 (593-913, A 730-735) gene was cloned using the forward primer and the reverse primer 5'- The reverse primer included the restriction site Noul. This generated plasmid pFastBac --eteacc--Hi-iieee --------EE--DDR---II [593-913 (A730-735), WT].
5'
hDDRI-2 (593-913, A730-735) gene was cloned into pFastBacl expression vector by PCR. An N-terminal His-TEV-Avi The forward primer included the re- striction site ECORI. The hDDR1-2 (593-913, A730-735) gene was cloned using the forward primer and the reverse primer 5"- The reverse primer included the [593-913 (A730-735), WTJ.
tag was added by using the forward primer 3' and the reverse primer S-TTCCTGCCATTCAATTTTCTSS
restriction site Notl. This generated
BCA2-1 (1-260, WT) gene was cloned into PET28a expression vector by PCR. An N-terminal His- TEV-Avi tag was added by using the forward primer S:CTTDAGGAAGGAAAGAMACCCGGGGGG The forward primer included the
3' and the reverse primer 5.TTCGTGCCATTCCANTTTCCC. restriction site Ncol. The BCA2-1 (1-260, WT) gene was cloned using the forward primer 5'- and the reverse primer The reverse primer included the
5' CTAGGAATTTAAACCAGGGAAAAAACCCTTTC restriction site Xhol. This generated plasmid pFT28a- -irr288--Hs--in Hii-----------A Siea-------EE--i----A-AAA TEV--VV--CAA((12200,
All cloning was performed using the Beyotime Seamless Cloning kit (D7010M), All plasmids were amplified using DH5a cells followed by DNA extraction. Insertion of the genes were verified by sequencing.
A.3.2. PROTEIN PRODUCTION
МАPK14
MAPK14 plasmid [either pET28a-His- Mils-TEV-ai--MMAPKI- (1-360, WT) or HIS.TRA--HSS-EEEEMMPPP...A.. (1-360, WT) was transformed into BL21-Goldd (DE3) competent cells (Agilent, 230132) and plated on LBBagar/kanamyeiin (50 pg/mL) medium then left to grow at 37 Fresh colonies from transformed BL21-Gold cells were picked and used to inoculate 100 mL of LB medium (10 g tryptone, 10g g NaCI, 5 g yeast extract per liter water) supplemented with (50 pg/mL. kanamyein and cultured overnight at 250 rpm 37 The overnight culture was added to 2 LB/kanamyein ((50 pg/mL)) and grown at 250 rpm C until OD600 reached 0.600. Isopropyl B-D-1 ADDD--hhiogllactooyraosss
(IPTG) then added (final concentration 0.3 mM) and the culture was left to continue to grow at C6ºC, 250 rpm overnight.
Cells were harvested the following day at 8,000 rpm at 4 and stored at -20ºC. Cell pellets were resuspended in lysis buffer (50 mM Tris, 500 mM NaCI, mM TCEP, 10% glycerol, pH 8.0 and 0..5 pT. Benzonase) and lysed using a high pressure cell homogenizer. Lysate was centrifuged at 13,000 rpm for 30 minutes at 4 twice, to remove cell debris. Lysate supernatant was then applied incubated for 1 hr at 4 Resin was then loaded into a column and the column was washed with wash buffer (50 mM Tris, 500 mM NaCI, 1 mM TCEP, 10% glycerol, pH 8.0) and eluted with elution buffer (50 mM Tris, 500 mM NaCl, 1 mM
mL of Ni-Resin (which had been pre-equilibrated with lysis buffer) and the lysate/Ni mixture was
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KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors
TCEP, 10% glycerol, pH 8.0 and 250 mM imidazole). Eluted protein was cleaved with TEV protease a 1:10 (w/w) ratio and dialyzed against dialysis buffer (50 mM Tris, 100 mM NaCI, mM TCEP, 7.5 mM MgC12, pH 8.0) at 4 ovemigght.
Protein was loaded onto a Ni-resin column, pre-equilibrated with lysis buffer and the cleaved protein was collected in the flow-through (cleaved-tag tag left on the column). In the case of Avi-tagged protein (MAPKI4-1), the dialyzed protein was incubated at C for 4 hrs with ATP (1 mM), Biotin (0.5 mM), and BirA (200 nM), prior to loading onto Ni-resin to remove cleaved hexahistidine tags.
Protein was collected and concentrated with a 10 kDa Millipore Amicon Ultra-15 centrifugal filter unit. Concentrated protein was loaded onto a Superdex 200 column pre-equilibrated with SEC buffer. Protein was collected according to UV-vis signal and follow-up SDS-PAGE verification. Protein fractions were pooled and concentrated with a 10 kDa Millipore Amicon U1tra-15 centrifugal filter unit and exchanged into a final buffer containing 10 mM HEPES, 200 mM NaCI, mM TCEP at pH 7.5. A final purified protein yield of 19 mg/L cell culture was obtained.
DDR1
DDRI plasmid (either HissBBee---s--TEE---------D
[593-913 (4730-735), WT] or -
(593-913 (A730-735), WTJ) was transformed into DHIOBac competent cells (Agilent) and plated on LB/agar plates containing 50 pg/mL kanamycin, 7 ug/mLL gentamicin, 10 ug/mL tetracycline, 100 ug/mL XaymmLX-ggal X-gal and 40 ug/mL IPTG then left to grow at C for 48 hours. Fresh colonies from transformed DH10Bac competent cells were picked and used to inoculate 5 mL of LB medium (10 tryptone. 10g NaC1, 5 g yeast extract per liter water) supplemented with 50 pg/mL kanamycin, 7 ug/mL gentamicin, 10g//mL tetracycline and cultured overnight at 37CC. An aliquot of each cell culture was verified to contain the recombinant bacmid by PCR analysis.
Transfection. In 6-well plate, S19 cells were grown in 2 mL cultures cell density of 2 x 106 cells/mL in SF9001II medium at 27%C. The culture medium was then exchanged with fresh SF900II medium followed by inoculation with the recombinant bacmid:lipid (Cellfectin II reagent) complexes. The bacmid infected cells were incubated at C7 for 5 days. The cell culture medium was collected as P1 virus and cell pellet was used for western blot analysis.
P2 baculovirus generation and scale-up protein expression. St9 cells were grown in a 50 mL. culture medium to a cell density of 2 x 106 cells/mL followed by infection with P1 virus in the ratio 1:200. Incubate the PI infected S19 cells at 27 for days. The cell culture medium was collected as P2 virus and the cell pellet from mL cell culture was used for western blot analysis. For scale-up expression, Sf9 cells were grown in a 12 L culture medium to cell density of 2 x 106 cells/mL followed by infection with P2 virus in the ratio 1: 1:200. For N-Avi tagged hDDR1-2, BirA was co-expressed (infection ratio 1:500, biotin 40 μM) with the P2 infected S19 cells. The P2 infected S19 cells were incubated at 27 for 3 days.
Cells were harvested after 3 days at 8,000 rpm at 4 and stored at -20ºC. Cell pellets were resuspended in lysis buffer (50 mM HEPES, 300 mM NaC1, 2 mM TCEP, 10 mM MgCl2, 10% saccharose, cocktail, 100U/mL. Benzonase, pH 8.0) and Protease Inhibitor Cocktail Tablet was added until a final concentration of 1 tablet/L. Cells were sonicated to lyse in repeating periods of seconds on, 3 seconds off for 10 minutes. This cycle was then repeated for an additional 10 minutes. color change of cell lysate was used to help indicate if cell lysis was sufficient, if no color change from pre-lysed cells was observed, cell lysis was allowed to continue. Cell lysate was centrifuged at 13,000 rpm for 30 minutes at 4 twice, to remove cell debris. Lysate supernatant was then applied to 6 mL of Ni-Resin (which had been pre-equilibrated with lysis buffer) and the lysate/Ni resin mixture was incubated for 1 hr at 4 Resin was then loaded into a column and the column was washed with wash buffer (50 mM HEPES, 300 mM NaCI, 2 mM TCEP, 10 mM MgCl,, pH 8.0) and eluted with elution buffer (50 mM HEPES, 300 mM NaCI, 2 mM TCEP, 10mM MgCl,, pH 8.0, 50 mM imidazole). Eluted protein was cleaved with TEV protease at a 1:10 (w/w) ratio and dialyzed against dialysis buffer (50 mM HEPES, 300 mM NaC1, 2 mM TCEP, 10 mM MgCl2, pH 8.0) at 49C overnight.
Protein was loaded onto Ni-resin column. pre-equilibrated with lysis buffer and the cleaved protein collected in the flow-through (cleaved-tag left on the column). Protein was collected and concentrated with 30 kDa Millipore Amicon Ultra-15 centrifugal filter unit. Concentrated protein was loaded onto a Superdex 200 column pre-equilibrated with SEC buffer. Protein was collected according to UV-vis signal and follow-up SDS -ASE verification. Protein fractions were pooled and concentrated with a 30 kDa Millipore Amicon Ultra-15 centrifugal filter unit and exchanged into a final buffer containing 20 mM HEPES, 200 mM NaCI, mM TCEP, 5% glycerol at pH 7.5. A final purified protein yield of 1.8 mg/L. cell culture was obtained.
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KinDEL: ENA-nneeed Library Dataset for Kinase Inhibitors
BCA2
The BCA plasmid, (Agilent, 230132) and plated on LB/agar/annmmyiin (50 pg/mL) medium then left to grow at 37 Fresh colonies from transformed BL21-Gold cells were picked and used to inoculate 100 mL of LB medium (10 g tryptone, 10 NaCI, 5 yeast extract per liter water) supplemented with 50 pg/ml. kanamycin and cultured overnight at 220 rpm 37 The overnight culture was added to 10 L LB/kanamyein (50 pg/mL) and grown at 180 rpm 37 until OD600 reached 0.600. Isopropyl G-D-Iahhogglacooyyranside (IPTG) was then added (final concentration 0.3 mM) and the culture was left to continue to grow at 169C, 160 rpm overnight.
WT), was transformed into BL21-Gold (DE3) competent cells
Cells were harvested the following day at 8,000 rpm at 4 and stored at -20 Cell pellets were resuspended in lysis buffer (50 mM Tris, 500 mM NaCI, mM TCEP, 1 mM PMSF, 10% glycerol, pH 8.0 and 100 U/ml. Benzonase) and lysed using a high pressure cell homogenizer. Lysate was centrifuged at 13,000 rpm for 30 minutes at 4 twice, to remove cell debris. Lysate supernatant was then applied to mL of Ni-Resin (which had been pre-equilibrated with lysis buffer) and the lysate/Ni mixture was incubated for hr at C Resin was then loaded into a column and the column was washed with wash buffer (50 mM Tris, 500 mM NaC1, mM TCEP, 10% glycerol. pH 8.0) and eluted with elution buffer (50 mM Tris, 500 mM NaCI, mM TCEP, 10% glycerol, pH 8.0 and 250 mM imidazole). Eluted protein was cleaved with TEV protease at 1:10 (w/w) ratio and dialyzed against dialysis buffer (50 mM Tris, 100 mM NaCI, mM TCEP, 7.5 mM MgC12, 5% glycerol, pH 8.0) at C.C overnight.
The dialyzed protein was incubated at C for 4 hrs with ATP (1 mM), Biotin (0.5 mM), and BirA (200 nM), prior to loading onto Ni-resin to remove cleaved hexahistidine tags. Protein was loaded onto a Ni-resin column, pre-equilibrtted with lysis buffer and the cleaved protein was collected in the flow-through (cleaved-tag left on the column).
Protein was collected and concentrated with a 30 kDa Millipore Amicon Ultra-15 centrifugal filter unit. Concentrated protein was loaded onto Superdex 75 column pre-equilibrated with SEC buffer. Protein collected according to UV-vis signal and follow-up SDS-PAGE verification. Protein fractions were pooled and concentrated with a 30 kDa Millipore Amicon Ultra-15 centrifugal filter unit and exchanged into a final buffer containing 25 mM HEPES, 200 mM NaCI, mM TCEP at pH 7.5. A final purified protein yield of 8.38 mg/L. cell culture was obtained.
BCA used for fluorescence polarization assay was obtained from Sigma-Aldrich (Product No. C2522).
A.3.3. BIOPHYSICAL. METHODS
Fluorescence Polarization
Annealing of DNA tagged DEL molecules. DEL small molecule hits are attached to the DNA oligo, Za (GCAGGCG- GAGACCTGCAGTCTT) Fluorescein (Integrated DNA Technologies, /3FluorT/) tagged complementary DNA oligo Za' (CGGACTCCAGGTTTTCCCCTTTTTOOOO was annealed to Za-tagged DEL compounds using a thermocycler (Bio-Rad, 1851148).
Assay setup. In a 384 well black small volume microplate (Greiner Bio-One, 784900), the annealed compounds were dispensed using Echo650 (Labcyte) at a constant final concentration of 4 nM in 137 mM NaCI, 2.7 mM KCI, 9.8 mM Phosphate buffer, 0.01% Tween-20. MAPK14-2, DDRI-1, and BCA were each serially diluted 1:1 starting from top dose of pM in 137 mM NaCI, 2.7 mM KCI, 9.8 mM Phosphate buffer, 0.01% Tween-20 for total 16-point dilution and were transferred to the 384 well black small volume microplate containing the annealed compounds using Agilent Bravo G5563A The final assay concentration was 2 nM the annealed compound and 25 μM top dose of the protein. The final assay volume was uL. The serially diluted protein was incubated with the annealed DEL compounds and then the fluorescence polarization was measured using PerkinElmer En Vision 2105 in milli-P (mP), where mP 1000 (S - GP) GP) (S and P are background subtracted fluorescence count rates, and G is instrument and assay dependent factor). The dose-response curves (mP vs. [Protein]) were fit in GraphPad Prism using the equation, Y Bmax x (/(KD + X) + NS x X + Background, where Y is mP, X is [Protein]. Bmar is the maximum specific binding in the same units as Y, KD is the equilibrium dissociation constant, in the same units as X, NS is the slope of nonspecific binding in Y units divided by X units, Background is the amount of nonspecific binding with no added radioligand.
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KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors
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Figure 7: Correlation between experimental replicates. Binding each target and the control was measured three replicates, which are included in the published dataset.
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KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors
Surface Plasmon Resonance
Compounds were tested using Surface Plasmon Resonance (SPR) using Biacore T200 and Biacore S200 (Cytiva Life Sciences). N-terminally biotinylated DDRI [393-913 (A730-735),, MAPK14 (1-360), and BCA2-1 (1-260, WT) were immobilized to a streptavidin coated SPR chip (Series S SA, Cytiva, 29699621), I). Approximately 4400 RU of hDDR1-2, 4900 RU of MAPK 14-1, and 3300 RU of BCA2-1 were immobilized for dose response assays using IxHBS-P+ (10 mM HEPES, 150 mM NaCI, 0.05% v/v Surfactant P20; pH 7.4). IxHBS-P+ was prepared by mixing 10xHBS-P+ (Cytiva, BR100671) with HPLC-grade water (Fisher Scientific, W51).
Compounds were diluted in the SPR running buffer which consisted of IxHBS-P+ supplemented with 5% DMSO (VWR, 76177-938). Multi-cycle kinetics was used to determine compound affinities. Compounds were injected in series of increasing concentrations with 30 /min flow rate, a 60 8 contact time and a 300 dissociation time. Compound sensorgrams were processed and fit using Biacore Evaluation software, with DMSO correction applied during sensorgram processing. Binding and dissociation curves for each compound were globally fit to obtain an on- and off-rate kinetic constant (k, and ka, respectively) which were used to determine the overall binding constant (Kp) for each compound.
B. Experimental Replicability
DEL experiments suffer from the substantial noise that stems from the large scale of combinatorial libraries screened simultaneously within single tube. Key sources of this noise include inconsistent control over the quantities of each compound in the mixtre,, synthesis-related challenges such as the introduction of side products and low synthetic yields, as well as errors during DNA sequencing. Figure illustrates the strong correlation between experimental replicates in our DEL experiments, underscoring the reproducibility of the panning procedures we conducted.
C. Validation Set Selection
We selected validation compounds to cover range of KD values and chemical diversity, allowing us to better assess the models' ability to rank molecules across diverse binding affinities. To achieve this, we used an ensemble of models described in Section 3, and then clustered the molecules based on chemical similarity via Butina clustering using Morgan fingerprints. This constituted the molecules the "in-library" set. Figure shows the distributions of the pk values of the selected compounds. We also surveyed literature for tool compounds, which we also re-synthesized on-DNA. This made up the additional molecules found in the "extended" set.
6
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Figure Distributions of pKn values in the in-library held-out sets.
D. Model Hyperparameters
This section describes the hyperparameters used to train the models presented in this study. Random Forest and XGBoost used 100 decision trees trained with the squared error criterion, and the depth of decision trees was not restricted, The k-Nearest Neighbors model used 5 nearest neighbors.
The final architecture of the DNN model consisted linear layers with the ReLU activation functions except for the last one. Batch norm and dropout layers (with the probability of zeroing an element equal to 20%) were applied after each layer before the activation layers. The hidden dimension size was set to 512 for all layers.
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KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors
The GIN model has 5 GIN convolutional layers with hidden dimension size equal to 256. The graph convolutional layers followed by the global average pooling layer and two linear layers with ReLU activation layer between them. The first linear layer reduces the dimensionality from 256 to 128 before the second layer produces the model prediction.
Chemprop uses three layers of bond message passing with the hidden dimension of 300. Then, the information is aggregated using a global average pooling layer, and one linear layer used to make predictions. The model is trained using the default Chemprop optimization procedure, which employs the Adam optimizer with Noam learning rate scheduler and 2 epochs of warmup.
The DEL-Compose model was used in two modes of molecule encoding: synthon-based encoding (denoted DEL- Compose (5)) and full molecule encoding (denoted ( The full molecule encoding uses four linear layers with ReLU activation functions to learn the encoding of the molecule based on its Morgan fingerprint. The synthon- based mode embeds each synthon separately using the same 4-layer MLP. Next, combinations of synthon embeddings (AB, BC, AC, ABC) are further processed by 2-layer MLPs, and finally all embeddings are aggregated using a 4-head attention pooling. The output distribution was set to zero-inflated Poisson distribution. The learning rate used to train DEL-Compose was 5e-5, and the batch size was 64.
E. Dataset Splitting
It is often difficult ensure that information from the test set does not leak into train. One popular method is calculating Bemis-Murcko scaffolds (Bemis & Murcko, 1996) for each compound in a dataset and assigning compounds to split by scaffold. Unfortunately, this method often fails for DEL data. Below we calculate Bemis-Murcko scaffolds for the top million compounds in the MAPK14 dataset using rdkit (Landrum, 2010). There are ~300,000 unique scaffolds calculated for these compounds, implying many compounds have a unique scaffold (see Fig. 9a). Additionally the common scaffolds often trivial (see Fig. 9b). The most common scaffold is simple benzene, and the next five most common also extremely common molecular building blocks. Given these shortcomings, we do not use scaffold split.
An alternative method is similarity split based on clustering with fingerprints. A classic method for this is Butina Clustering (Butina, 1999). Unfortunately. it does not scale well to large datasets. We have developed an internal method that scales to large datasets. Our two step method first uses UMAP (MeInnes et al., 2018) to reduce 1024 ECFP4 fingerprints (Rogers GPU
Hahn, 2010) to length 10 float vectors. Then HDBSCAN (Melnnes al., 2017) is used to cluster compounds, implementation from NVIDIA (cuml) this in few hours on single Tesla T4. Clusters assigned splits using waterfall method, each step assigning the largest remaining cluster the smallest current split. The following settings were used:
UMAP (n c00poneennnt "aatrii-"jacccard"" neiighbors=30, min_dist=0.0 0, n_epochs=1000)
HDBSCAN (min (min_cluster_sizeell min_samples=None, metric=/euclidian
prediction prodiction_dat--Truee cluster cluster_seiectiene r method='eohm!
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KinDEL: DNA-Encodee Library Dataset for Kinase Inhibitors
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(b) The top six most frequent scaffolds are common structures that not pharmacologically "interesting".
Figure 9: Munik-Murrcko scaffolds in KinDEL. The high number unique scaffolds and the simplicity of the most common scaffolds indicate that scaffold-based split challenging enough for our benchmark.
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KinDEL: Eno-endoded Library Dataset for Kinase Inhibitors
(a) DDRI
(b) MAPK14
Figure 10: Example ligands from the KinDEL datasets docked our selected kinases; (a) example pose of library member docked DFG-out conformation of DDRI (PDB: 6FEX), forming extensive hinge interactions. Beta-1 and -2 hidden for clarity: (b) example pose of library member docked to DFG-out conformation of MAPKI4 (PDB: 3KQ7), bridging the activation loop and C-alpha glutamate.
F. Molecular Docking Procedure
Tautomer and protonation state selection performed using Epik Classic (Shelley et al., 2007) from Schrödinger Suite 2024-4 at pH 7.4. For each library member, random subset of stereoisomers was enumerated using rdkit 2023.09.2 (up to 32), then conformers per stereoisomer were generated using ETKDGv3 (Wang et al., 2020). Up to 8 of the lowest-energy conformers under the UFF force field (Rappé et al., 1992) (max per stereoisomer) were retained, for compounds with UFF parameters for all atoms (otherwise, up to random conformers). Docking was performed using the Vina scoring function (Trott& & Olson, 2010) in Uni-Dock 1.1.2 (Yu et al., 2023) with exhausti venness 128 and max_step 20, saving the top three poses per receptor. All of these 234k ligand states were docked against all six receptor models, using fixed 20 Å docking box centered on the orthosteric pocket.
One receptor model represents the major conformation of DDRI in available experimental structures, the DFG-out, C- helix-in PDB: 6FEX (Richter et al., 2018). Five receptor models were chosen to represent most experimental structures of MAPK14, with a variety activation loop and P-loop conformations, PDB: 3KQ7, 3S3I, 5WJJ, 5XYY, and 6SFI (Cheng al., 2009; Aiguadé al., 2012; Kaieda et al., 2018; Wang et al., 2017; Rohm et al., 2019). All receptors were prepared using the Protein Preparation Wizard from Schrödinger Suite 2023-4, capping termini with neutral ends, and aligned into common frame.
Docked poses are provided in SDF format with "molecule hash" and "receptor" properties. Example poses are shown in Figure 10, where the hinge binding motif that is characteristic for kinases can be observed.
20


Reviews:
[{"review_id": "6NvZSFruxS", "reviewer": "Reviewer_2jpD", "summary": "This paper introduces a dataset of DNA encoded libraries for two kinases, MAPK14 and DDR1, with 81 million compounds. This dataset will be of use in drug discovery applications and modeling of related biological processes.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the Reviewer's time and effort in evaluating our paper and are pleased with the overall positive feedback. The only concern raised pertains to our choice of venue for publication. We would like to address this by outlining our reasons for selecting this machine-learning conference over a journal.\n\nFirstly, we believe that presenting our dataset and benchmark at such a conference maximizes its impact by directly reaching its target audience—machine learning researchers. The lack of high-quality DEL datasets is a bottleneck for developing advanced machine learning models. By contributing this work, we aim to advance research in analyzing the outputs of innovative compound screening technologies.\n\nFurthermore, this year ICML has specifically called for application-driven submissions. The Call For Papers seeks “Application-Driven Machine Learning (innovative techniques, problems, and **datasets** that are of interest to the machine learning community and driven by the needs of end-users in applications such as healthcare, physical sciences, biosciences)”. We believe our paper aligns well with this focus, as it provides a valuable new dataset for an important area of science that currently suffers from a lack of high-quality data.\n\nThank you once more for your feedback. Please let us know if you have any further questions.", "claims_and_evidence": "The claims of the paper are well supported by the tables shown in the article.", "methods_and_evaluation_criteria": "The evaluation and benchmark of the data in terms of Kd predictions is sensible and the results look reasonable.", "theoretical_claims": "There are no proofs.", "experimental_designs_or_analyses": "I read through the experimental design and analysis and see no obvious issues.", "relation_to_broader_scientific_literature": "This article introduces a useful dataset that contains high chemical diversity, and will be useful as a reference for the development of new methods.", "essential_references_not_discussed": "I don't have any references to add.", "other_strengths_and_weaknesses": "This paper does not really fit within the scope of the main conference track. It would be better suited to a workshop, or a journal that is better suited to evaluate the experimental assays included in this study. There are no model or algorithm developments of significance.", "other_comments_or_suggestions": "None", "questions_for_authors": "None", "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": "F7nnRQPqyQ", "reviewer": "Reviewer_wZnX", "summary": "The KinDEL paper provides a new dataset of laboratory measurements related to DNA-encoded libraries (DEL) together with baseline models that analyze these data.  The experiments used a library of 81 million small molecules interacting with two kinases: MAPK14 and DDR1.  The authors further extended the dataset with additional on-DNA repeat measurements of the top hits, binding affinity measurements for selected molecules off-DNA molecules, and predicted 3D structures of the interactions of these off-DNA molecules with the targets.  This dataset opens up an opportunity for important work related to denoising DEL experiments and for incorporating the DEL experimental results in downstream applications.", "questions": "", "limitations": "", "rating": 5, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for dedicating your time to review our paper and for your positive feedback and valuable suggestions. We will include the number of data points for the testing sets in the main text as recommended. Furthermore, we agree that evaluating the generalization of the models on external datasets, including compounds with activity against other kinases and decoys, is a compelling research direction. We see this as a promising avenue for future work. Once again, we appreciate your thorough review and insightful feedback! Please let us know if you have any further questions.", "claims_and_evidence": "The lack of public datasets related to DEL experiments is a known issue that has plagued the public development of denoising algorithms for DEL datasets. The huge quantity of data generated from these experiments is ideally suited for downstream applications that incorporate machine learning.  This is the first such dataset to link the raw DEL measurements with on-DNA repeats and proposed 3D structures.  These additional off-DNA measurements are invaluable for testing the generality of the small molecule models.  The chosen targets are well-studied proteins that have additional public datasets available, so one could extend this work in the future to test the generality and the limitations  of DEL datasets.  I found no problematic claims in the submission, though I haven't inspected the dataset itself carefully beyond the analyses presented herein.", "methods_and_evaluation_criteria": "Everything in this paper makes sense.  The authors are certainly very familiar with the problem domain and provide strong baselines and well-documented datasets.  The division of the training datasets in 3 different splits and the documentation of baseline models on all categories follow the best current ideas in this domain.\n\nA totally minor, mostly aesthetic point: the numbers of molecules for each of the on-DNA, off-DNA, and extended on-DNA datasets are only mentioned in the top of the tables (presumably written as n=30, 33, 41 mean in Table 1, and similar in Table 2).  The authors might consider listing these numbers more prominently or point to the entries in the table from somewhere in the text.", "theoretical_claims": "There were no major or novel theoretical claims in this work.  The ideas around possible combination of 3D structures with DEL datasets have been previously published.  The choice of baseline models make sense and the most advanced of them was also previously published last year (Chen et al, 2024).  The observation of improved behavior for the reasonable baselines on off-DNA data compared to the count-based analyses is also meaningful and is the main reason for the development of non-trivial analysis methods for datasets from DEL selection experiments.", "experimental_designs_or_analyses": "The experiments were sound and valid.  Depending on the library composition, the use of Avi tags and streptavidin beads might be suboptimal for immobilization during a DEL selection experiment. However, in this specific case, the additional measurements without targets, the fluorescence measurements (where streptavidin binding is an optimal choise), and the validation of the baseline models outside the DEL setup strongly suggest that the dataset is clean.", "relation_to_broader_scientific_literature": "This work adds to a rather limited set of public datasets of DEL experiments.  The authors did a good job of reviewing them in section 5.1.", "essential_references_not_discussed": "A detailed understanding of this paper requires understanding of the broader scientific literature related to DNA-encoded library selection experiments, and a reasonable understanding of the industrial process of drug discovery, some of which is unfortunately not well documented and differs in different companies.\n\nThe use of such data by academics and non-experts in the future is of course possible, which makes this particular contribution important enough to warrant consideration in this conference.", "other_strengths_and_weaknesses": "The paper is clear and concise.  The dataset is valuable.  It would have been useful to test the generalization of the models on other known molecules for these targets, as well as to evaluate the ability/suitability of these models to score decoys or discriminate molecules for these specific kinases vs other kinases.  However, such extensions are best left as follow-up work to this contribution and would have needlessly extended the length and scope of this work.", "other_comments_or_suggestions": "No additional suggestions.", "questions_for_authors": "No questions to the 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": "7zcLBVBe19", "reviewer": "Reviewer_Yxwq", "summary": "The paper introduces a novel dataset containing DEL screen data for two different proteins, and benchmarks the performance of several supervised ML methods in the context of learning from the provided data.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your careful review of our work and your valuable feedback. \n\n**Dataset description**:\nWe appreciate your specific comments on making our paper more accessible to machine learning researchers. We will revise Figure 2 by removing unnecessary technical details that do not contribute to the understanding of the data format. For instance, the biotinylation of proteins is detailed in the appendix and can be omitted from Figure 2 to avoid potential confusion. Additionally, we will add a new subplot that outlines the high-level data acquisition process and data format. This subplot will clarify the inputs and labels (pre- and post-selection count data) used in our models. We will also make sure to define all new terms that might be confusing for the machine-learning audience.\n\n**Biophysical Assay Validation**:\nWe will relocate the appendix reference concerning the selection of validation compounds to an earlier section of the text for clarity. The limited number of validation points is due to the high cost associated with these experiments, as each molecule requires resynthesis, either on-DNA or off-DNA, along with separate biophysical assays for each.\n\n**Pre-selection Data**:\nDuring the synthesis of the DNA-encoded library, not all molecules are produced in equal amounts. Variability can arise from differences in synthesis efficiency, coupling yields, or synthesis errors. Additionally, some molecules may precipitate or adhere to storage containers, resulting in loss and lower observed counts. While this data is typically used to normalize counts, there are important caveats. Sequencing inaccuracies can lead to error propagation, especially for low-abundance molecules. DEL-Compose is a model that addresses these issues by incorporating pre-selection counts as inputs, which serve as a multiplier when modeling the zero-inflated Poisson distribution in the loss function. We will add this discussion in the appendix.\n\n**Range of Kd values in the held-out test data**:\nWe will provide histograms of Kd values in Appendix C. Our approach involves selecting both binders and non-binders with varying Kd values to evaluate our model's ability to accurately rank these molecules using Spearman’s correlation. Although we propose a regression problem where molecules are ranked from most to least promising, the Kd values can also be categorized into two groups for testing classification models, as the dataset includes both binders and non-binders.\n\n**Only 1M top molecules are used for training**:\nWe observe that the baseline models struggle with sparse DEL data and require modifications to the loss function or additional inductive biases to handle label sparsity. To simplify learning, we train on the top 1M compounds. This introduces some bias relative to the evaluation set, but since we aim to rank order the molecules rather than make binary predictions, we believe our model can effectively rank molecules in validation sets. Nonetheless, we provide access to the full dataset for researchers to explore better results with more training points.\n\n**Ranking molecules by binding affinity**:\nAll the evaluated models predict continuous values, employing regression, and we use Spearman's correlation as the evaluation metric. Unlike Pearson's correlation, which identifies linear relationships, Spearman’s correlation effectively assesses ranking quality. We will add a brief discussion on the ranking issue and our rationale behind selecting this evaluation metric. In short, these models are often used to score available molecules to maximize their binding affinity. It is challenging to establish a firm threshold to define a binder because priorities change depending on the target and stage of drug development. Measuring model performance via ranking ensures that good models retrieve the best options for any given target and screening pool combination.\n\n**Other data splits**:\nRegarding the splitting of KinDEL, we explored several methods and determined that the disynthon split is optimal. Although we considered splitting datasets by single synthon positions, this approach presented two challenges. Firstly, only one part of each molecule is unique in the testing data, while the model encounters all possible disynthons at the remaining two positions. Secondly, some synthons may feature motifs essential for binding, such as kinase hinge-binding motifs, which could end up entirely in the testing data. We believe that the disynthon approach strikes a sensible balance. Please see Figure 4, where the visible line patterns relate to enriched disynthons, and the absence of clear planes indicates the lack of enriched (mono)synthons.\n\n**AIRCHECK datasets**:\nThank you very much for this suggestion. We will reference this resource in the related work.\n\nThank you once again for your invaluable review and suggestions. Please let us know if you have any further questions.", "claims_and_evidence": "Yes.", "methods_and_evaluation_criteria": "Yes.", "theoretical_claims": "N/A.", "experimental_designs_or_analyses": "Yes.", "relation_to_broader_scientific_literature": "New dataset.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "Strengths of the paper include the importance of the problem (DELs seem to be a useful screening technique in practice, and have received relatively little attention from ML community), the rigorousness of the conducted experiments (I appreciate e.g. testing different splitting conditions, and the choice of benchmarked ML method, which to my knowledge are fairly good coverage of SotA), and realistic discussion of limitations of the data.\n\nIn my opinion, the paper has two main weaknesses:\n1. I suspect it will be difficult to follow by people with ML background (it definitely was for me at times, and I have some experience with DELs - some things only became clear on 2nd pass, because they rely on information from later sections). To give a few examples (not exhaustive):\n    - In introduction: “Multiple rounds of washing are conducted to remove any weak binders, and the DNA tags of surviving molecules are sequenced as a measure of binding affinity” - it’s not clear at this point what is the result of this sequencing, and how is it a measure of binding affinity (as opposed to be a binary binding/non-binding indication). Counting is clarified in section 2, but this caused confusion until then.\n    - Figure 2 uses the term library capture which wasn’t immediately obvious to me, similarly it mentions biotinylation of protein which doesn’t seem to be described at all.\n    - “Biophysical Assay Validation”: it isn’t clear how this subset of data was selected (in the next section the Appendix is eventually referenced, but again it leaves the reader hanging for some reason until then). The number of samples seems very small, given the discussed noisiness of data, is this a common practice to use so few data points for validation?\n    - “Pre-selection Data”: it isn’t clear what causes differences in abundance of molecules. Also, from the ML point of view, it’s also not clear how this information is typically used - should it involve count normalization?\n    - I would personally love to see some high-level overview of the data from the ML perspective, perhaps as a graphical abstract-type figure: summarized in a single place number of molecules, distribution of counts (with a note that it correlates with binding affinity), number of targets, information about estimated noise (baseline correlations?), information about test set, perhaps also splits. There is a lot of detail in the paper separating this information, and finding the (in my mind) important pieces takes some effort.\n\n2. Experimental evaluation:\n    - The description of held-out test sets is very vague in terms of value range - would it be possible to demonstrate Kd’s in more detail than saying it’s a “range of Kd values” (and doing so in the appendix)? This is fairly meaningless statement, and it impacts the interpretation of the results - do ML methods discriminate binders and non-binders? Is it ranking within binders?\n    - “all models were trained using the top 1M compounds with the highest counts” - this seems like a very confusing design choice, could the authors elaborate on this? It goes back to the previous point: if the test set contains only binders, it seems like cheating to train only on the data with highest counts. Perhaps there are good reasons for this (e.g. non-binders being distinguishable by things like docking), but in that case discussion would be warranted. \n    - Also going back to the previous points, “We ultimately wish to rank molecules by binding affinity” - again, it isn’t clear to me that’s the case, and that the goal is not discriminating binders / non-binders - discussion would be great.", "other_comments_or_suggestions": "Minor remarks:\n- Would it be possible to have a split in which different data partitions contain completely separate synthons? Would it make sense to do so?\n- One source of DEL data that I think would be worth mentioning is https://www.aircheck.ai/; I am not sure if it has associated paper though.", "questions_for_authors": "N/A.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper presents KinDEL, a DNA-encoded library (DEL) dataset for two kinases (MAPK14 and DDR1), comprising 81 million compound-target interactions with associated on-DNA repeats, off-DNA binding affinities, and predicted 3D structures. It includes baseline models and multiple evaluation splits. 

All reviewers acknowledge the dataset's value. Reviewer wZnX strongly supports acceptance, noting the dataset's novelty and utility for modeling DEL experiments. Reviewer Yxwq highlights the rigorous evaluation and the importance of DELs as a largely untapped area for ML, raising minor concerns about clarity, which the authors addressed well in the rebuttal. Reviewer 2jpD initially questioned venue fit but ultimately recognized the contribution’s merit and raised their score to a weak accept.

The authors make a convincing case for ICML as the appropriate venue, citing the conference’s call for application-driven ML in biosciences. The rebuttal directly addresses reviewer concerns and proposes clear manuscript improvements.

This is a well-executed, impactful contribution that introduces a high-quality benchmark dataset with clear relevance to the ML community.
Decision: Accept (poster)