Deciphering DEL pocket patterns through contrastive learning

Domain: chemistry
Published: 2026-02-16
Editorial outcome: major_revision

Abstract
DNA-encoded libraries (DELs) facilitate high-throughput screening of trillions of molecules against protein targets through split-pool synthesis and DNA tagging. Despite their potential, only a few DEL-derived compounds have advanced to clinical trials or reached the market. A better understanding of the defining characteristics of target proteins, particularly those with binding pockets suitable for DEL screening, is critical to improving success rates. However, existing approaches remain limited in assessing pocket flexibility and functional similarity. Here, we present ErePOC, a pocket representation model based on contrastive learning with ESM-2 embeddings to address these challenges. ErePOC captures both structural and functional features of binding pockets, enabling identification of shared characteristics among DEL targets. By integrating analyses of low-dimensional physicochemical properties and high-dimensional ErePOC embeddings, we provide a comprehensive view of DEL target space. With 98% precision in downstream classification tasks, ErePOC demonstrates high performance in pocket representation, which is then applied to predict human proteins suitable for DEL screening, with enrichment uncovered across 18 functional categories. This work establishes a framework for enhancing DEL-based drug discovery through more effective target selection and pocket similarity analysis.

Introduction
DNA-encoded libraries (DELs) represent a powerful screening technology for discovering small molecules with high affinity for target proteins1,2. Constructed through split-pool synthesis, DELs generate vast combinatorial libraries, often containing billions to trillions of compounds, each uniquely labeled with a DNA barcode. These DNA-tagged compounds are then screened based on their binding affinities to specific target proteins, facilitating high-throughput identification of potential drug candidates.

DEL technology has contributed to the discovery of numerous hit compounds in target-based drug discovery, with notable successes including inhibitors for SARS-CoV-2 3CLpro3, soluble epoxide hydrolase4, Autotaxin5, and receptor-interacting protein kinase 16.

Despite the high-throughput capabilities and economic advantages of the DEL technique, the number of DEL-derived molecules advancing to clinical trials or reaching the market remains relatively low, partly due to the relatively recent adoption of DEL as a mainstream approach for hit identification, as well as persistent challenges such as uncertainties in processing DEL data, difficulties in optimizing DEL hits, and a general lack of understanding on target druggability with DEL molecules7. To overcome these barriers, efforts to integrate artificial intelligence (AI) with DEL screening have emerged, with most focusing on how to select more promising hits from the highly noisy screening data8. Notably, DEL molecules are characterized by common features constrained by solution chemistry and the structural requirements for attaching DNA tags, which may result in interaction patterns with target protein pockets. A perspective on the characteristics of protein pockets that bind DEL molecules could provide insights into DEL, potentially enhancing its efficiency and success rates in drug discovery campaigns.

Protein language models (PLMs) have emerged as powerful tools in biological research, with wide applications in protein structure prediction9, property prediction10, function annotation11,12, and protein design and engineering13. Despite these advances, large-scale language models specifically designed for binding pockets—the fundamental functional units in drug design—are still limited. Existing methods for pocket representation, such as MASIF14, primarily rely on learning chemical and geometric features on protein surfaces. In certain machine learning (ML) models for drug–target interaction (DTI) prediction, pocket structures are explicitly encoded15–18. For example, UniMol15 utilizes self-supervised masked atom prediction to learn the representations of pocket structures, while PocketAnchor16 represents pockets using anchor points sampled in space for downstream pocket detection and binding affinity prediction tasks.

In contrastive learning, ML models are trained to distinguish between similar and dissimilar pairs of data, with the aim often being to learn a generalizable feature representation. Combining such a self-supervised representation learning technique with a pre-trained large PLM, such as ESM-2, enables zero-shot or few-shot learning by leveraging the evolutionary information encoded in the PLMs19. This approach has been effectively utilized in DTI prediction by methods such as DrugLAMP17 and PocketDTA18. However, functional classification models for binding pockets remain largely absent.

Key challenges include the lack of comprehensive pocket databases and the inherent structural flexibility of binding pockets, which pose significant difficulties for structure-based models, limiting their effectiveness for functional annotation and classification. Research has also shown that binding pockets for the same ligand (e.g., ATP) can exhibit notable geometric differences20, and that global structural similarity in proteins does not always correspond to local similarities in pocket structures21. These observations underscore the limitations of current pocket representation methods, particularly in distinguishing functionally similar pockets. Recent work has highlighted that refined pocket representations can directly enable the biological discoveries22.

To address these challenges, a more tailored, function-driven approach to pocket modeling is needed to advance both the understanding of binding pockets and drug discovery.

In this work, to better represent protein pockets and assess their functional similarity, we introduce ErePOC, a pocket representation model based on contrastive learning. In this approach, ESM-2 embeddings are processed through a feedforward neural network (NN) to generate function-aware representations by minimizing a contrastive loss function that increases the similarity between pockets bound to similar ligands while maximizing the dissimilarity between those bound to dissimilar ligands. ErePOC enables the visualization of pocket feature distributions across both experimentally determined and predicted protein spaces, revealing distinct pocket patterns shared by targets binding the same class of ligand. Building upon these capabilities, we focus on exploring the common features of DEL target pockets. By leveraging ErePOC’s ability to model these pockets, we screen the human proteome to identify targets containing pockets similar to those successfully engaged by DEL molecules, calculate corresponding enrichment scores, and uncover potential targets suitable for DEL screening. This work not only introduces an approach to understanding and improving DEL-based drug discovery but also enhances target identification and pocket analysis, which are crucial for biological discovery in general.

Results
This study aims to identify shared characteristics of protein targets suitable for DEL screening, with a particular emphasis on binding pockets as the central unit of analysis. We integrated data from several sources, including BioLiP223 and AlphaFill24, which contain experimental and predicted ligand–protein complex structures, respectively. We also curated two datasets containing complex structures with ligand being either DEL molecules or FDA-approved drugs (FDA-AD). We systematically examined the binding pocket characteristics across the DEL, FDA-AD, and BioLiP2 datasets, focusing on sequence features, physicochemical properties, and binding interactions. The findings are structured as follows: We begin with a detailed analysis of DEL pocket patterns, then introduce the training and validation of the ErePOC model for representing protein pockets. This is followed by an exploration of pocket landscape clustering, where we compare experimentally determined and computationally predicted structures. Finally, we predict human protein classes most likely enriched in DEL screening and evaluate their functional roles and structural similarities at both global and local levels.

Sequence analysis on pocket datasets
We compared pocket sizes across various structures by analyzing the distribution of amino acid residues within the pockets. These structures are sourced from four categories: (i) the BioLiP223 database, from which we selected entries annotated with regular ligands (i.e., biologically relevant small molecules) and their associated pockets; (ii) the AlphaFill dataset24, which contains computationally predicted ligand–protein complex structures; (iii) a DEL dataset curated from ligands reported to be identified by DEL screening (Supplementary Information Table S1); and (iv) an FDA-AD dataset containing FDA-approved drugs with experimentally determined complex structures (Table S2). They include 326,416, 293,019, 128, and 340 pockets, respectively. For BioLiP2, pockets were defined using experimentally annotated binding residues provided by the web server. Meanwhile, pockets for the AlphaFill, DEL, and FDA-AD datasets were generated by including all amino acid residues within 5 Å of the bound ligand. To assess the impact of this inconsistency, we additionally redefined BioLiP2 pockets using the same distance-based criterion and repeated all analyses under this unified definition. The key findings remained consistent across definitions, suggesting that our conclusions are reasonably robust to differences in pocket definition.

As shown in Fig. 1A, the average number of residues in BioLiP2, AlphaFill, DEL, and FDA-AD pockets was 12.5, 12.5, 28.1, and 16.1, respectively. The larger number of residues surrounding DEL and FDA-AD ligands likely reflects their greater molecular size and chemical complexity, often containing halogen atoms and other bulky functional groups that demand more spatially extended binding environments. The average molecular weights of DEL and FDA-AD ligands in this study are 560.5 and 310.9 Da, respectively (Fig. S2). In contrast, regular ligands and their pockets have co-evolved for an optimized fit tuned to biological needs rather than maximal binding. Synthetic drug molecules are often optimized for potency and selectivity through medicinal chemistry efforts, which typically result in larger and more chemically complex scaffolds compared to regular ligands25,26. They often target protein pockets that are more spacious, flexible, and dynamic, accommodating a broader range of interactions.

We analyzed amino acid frequencies in binding pockets across the BioLiP2, DEL, and FDA-AD datasets. To highlight compositional differences, we calculated the enrichment ratio of each amino acid relative to its abundance in the PDB. As shown in Fig. 1B, methionine, tyrosine, tryptophan, and phenylalanine were the four most significantly enriched amino acids in the DEL dataset. These four were also the most enriched in FDA-AD, appearing more than twice as frequently in drug-binding pockets compared to proteins in general. These bulky side chains potentially provide unique pocket geometries for specific molecular binding, as well as anchors for hydrophobic and aromatic interactions. Compared to BioLiP2, hydrophobic amino acids, including methionine and leucine, were significantly enriched in DEL and FDA-AD. In contrast, cysteine showed significantly lower enrichment in drug-binding pockets. We note that our analysis excluded covalent drug molecules, which primarily react with cysteine’s thiol group. Overall, our analysis reveals that DEL and FDA-AD pockets share similar amino acid composition patterns, distinguishing them from pockets that bind regular ligands.

Analysis of pocket physicochemical properties and their interactions with ligands
We used Fpocket27 to analyze the biochemical and biophysical properties of pockets across the DEL, FDA-AD, and BioLiP2 datasets. Six Fpocket descriptors were grouped into three clusters to compare pocket size, hydrophobicity, and polarity (Fig. 2A–F). Pocket size was assessed using volume and the number of alpha spheres. The average volumes of pockets were 3301.2 Å³ for DEL, 2534.1 Å³ for FDA-AD, and 2739.5 Å³ for BioLiP2. DEL pockets were approximately 1.3 times larger than FDA-AD pockets and nearly 1.2 times of BioLiP2 pockets. DEL pockets also contained more alpha spheres (164.3) than FDA-AD (118.8), making them 1.4 times larger in this size metric. The average alpha sphere densities were 11.0 Å for DEL, 10.0 Å for FDA-AD, and 10.5 Å for BioLiP2, suggesting that DEL molecules generally bind to more open and exposed pockets.

DEL and FDA-AD pockets are more hydrophobic than BioLiP2 pockets, as indicated by their higher proportions of apolar alpha spheres and larger mean local hydrophobic densities. Interestingly, while DEL and FDA-AD pockets exhibit similar distributions of local hydrophobic densities, their apolar sphere proportions differ, averaging 53.9% for FDA-AD, 50.8% for DEL, and 46.2% for BioLiP2. For polar interactions, the distributions were similar. BioLiP2 had the highest proportion of polar atoms in pockets (38.6%), followed by DEL (37.3%) and FDA-AD (36.0%).

We further analyzed the interactions between pocket residues and ligands using the Arpeggio method28 (Fig. 2G–I). Hydrophobic interactions accounted for 50.7% in DEL, 42.9% in FDA-AD, and 32.5% in BioLiP2. Hydrogen bond interactions were 3.8% for DEL, 6.7% for FDA-AD, and 9.7% for BioLiP2, while polar interactions constituted 6.0%, 11.7%, and 14.5%, respectively. Ionic interactions were slightly more prevalent in DEL pockets (1.3%) than in FDA-AD pockets (0.7%), while BioLiP2 pockets exhibited a higher fraction of ionic interactions (3.9%). This pattern may reflect the early, pre-optimization status of DEL compounds compared to approved drugs.

Overall, these results indicate that DEL hits preferentially bind to larger, more hydrophobic pockets. The expansive contact area in these pockets enhances binding through shape complementarity, thereby favoring hydrophobic interactions. These characteristics suggest potential avenues for optimization toward drug-like molecules, for example, by balancing polar interactions to improve binding specificity. Statistical analyses were performed by computing Cliff’s δ effect sizes for key pocket and pocket–ligand interaction features (Fig. S3). These analyses confirmed that DEL pockets were significantly larger than FDA-AD targets (volume δ=0.405; number of alpha spheres δ=0.409; alpha sphere density δ=0.395; p<3.6e-11) and BioLiP2 pockets (volume δ=0.302; number of alpha spheres δ=0.321; alpha sphere density δ=0.201; p<3.4e-09), and exhibited a balanced polar–apolar composition, with physicochemical properties intermediate between FDA-AD and BioLiP2 (Fig. S3A). Analysis of pocket–ligand interactions confirmed that DEL pockets were dominated by hydrophobic contacts (δ=0.122 vs FDA-AD, δ=0.378 vs BioLiP2), whereas hydrogen bonds (δ=−0.150 vs FDA-AD, δ=−0.392 vs BioLiP2) and polar interactions (δ=−0.207 vs FDA-AD, δ=−0.459 vs BioLiP2) were reduced (Fig. S3B). These characteristics indicate that DEL binding is primarily driven by hydrophobic effects, stabilized by a minimal yet functionally critical set of polar anchors.

Principal component analysis (PCA) of features further confirmed these patterns, showing that DEL pockets occupy a distinct region in PCA space (Fig. S4). PC1 primarily reflected chemical composition, including apolar/polar atom ratios and interaction types, while PC2 was dominated by structural size descriptors, together accounting for approximately 75% of the variance. The pocket analysis was also consistent with molecular property differences obtained using Molecular Operating Environment (MOE)29 (Fig. S2). DEL molecules exhibited lower water solubility (LogS=−6.49) compared to approved drugs (−3.05) and higher hydrophobicity (cLogP=3.42 vs. 1.44). While DEL pockets shared the overall druggability characteristics of FDA-AD targets, they displayed a distinct physicochemical bias, favoring larger, more hydrophobic pockets with a different polar–apolar balance. We note that no single feature or simple combination could distinguish DEL from FDA-AD or general protein pockets, possibly due to the broad variability of pocket structures. This observation highlights the need for developing a more informative representation of pockets.

ErePOC: an Enhanced representation of POCkets by contrastive learning
We developed ErePOC, a contrastive learning-based model trained on 326,416 pocket–ligand pairs from the BioLiP2 dataset, to generate latent space representations of binding pockets (Fig. 3). For each pair, pocket residues were embedded using 1280-dimensional feature vectors from ESM-2, and pocket-level features were obtained by average pooling of residues forming the binding pocket. This approach ensures that the spatial profile captures structural information intrinsic to the pocket. To quantify pocket similarity, each 1280-dimensional pocket embedding was projected into a 256-dimensional latent space through a two-layer perceptron. Pocket similarity was computed as the cosine similarity between these 256-dimensional pocket representations, while ligand similarity was evaluated using the cosine similarity of their 2048-dimensional molecular fingerprints. Unlike conventional DTI models, ErePOC employs Kullback-Leibler (KL) divergence as the loss function to align ligand distributions with the pocket landscape, encouraging pockets that bind chemically similar ligands to be positioned closer in the latent space.

Ultimately, ErePOC learns a compact 256-dimensional representation for each pocket, effectively capturing both fine-grained similarities and key distinctions among binding sites. By aligning pockets according to the chemical similarity of their ligands through contrastive learning, this approach results in an enhanced, function-aware representation of binding pocket features. The resulting latent space provides deeper insights into pocket–ligand interactions, enhancing the classification and analysis of binding pockets.

We evaluated our model using zero-shot learning tasks to compare the performance of representations derived from ESM-2 embeddings and those learned by ErePOC. We considered a classification task involving seven types of pockets, each corresponding to a unique ligand type—ADP (9531 pockets), COA (1900), FAD (6367), HEM (13,312), NAD (5354), NADP (3997), and SAM (1228)—totaling approximately 43,000 binding pockets curated from BioLiP2. Figure 4A, B show the clustering of these seven binding pocket types using t-SNE30 based on ESM-2 and ErePOC representations, respectively. The results clearly demonstrate that the contrastive learning framework generates well-separated clusters for different ligand/pocket types, effectively capturing both functional and ligand-specific features of the binding pockets. In contrast, the ESM-2-based model, which lacks functional annotations specific to pockets, shows limited separation between pocket types. This comparison highlights the superior performance of contrastive learning in producing more refined and informative representations for functional pocket classification.

To assess the robustness of our model, we conducted an ablation study in which two types of binding pockets were completely excluded from the BioLiP2 training dataset prior to contrastive learning. We then evaluated the model’s performance in classifying these excluded pocket types. Figure S5 presents t-SNE visualizations for various exclusion scenarios: ADP and FAD, HEM and ADP, ADP and NAD, and HEM and SAM pockets. The results show that strong classification performance was maintained even for pockets excluded from training. The contrastive learning framework effectively distinguishes the removed ligand types, highlighting its ability to generalize and accurately classify pockets based on their functional and ligand-binding features. This robustness suggests that the model leverages annotated ligand information during training to make reliable predictions, even when specific ligand types are absent from the training set. This underscores the effectiveness of ErePOC in capturing and generalizing key binding pocket characteristics.

The approach was further validated by analyzing ATP-, FAD-, and HEM-binding pockets from both the BioLiP2 dataset (experimentally determined structures) and the AlphaFill dataset (which imputes ligands into AF2-predicted structures). t-SNE clustering based on ESM-2 features (Fig. S6A) showed limited separation between pocket types. In contrast, the ErePOC representation revealed substantial overlap among pockets binding the same ligands across both datasets, demonstrating ErePOC’s ability to capture structural similarities between experimental and predicted protein-ligand complexes.

Additionally, 500 randomly selected pockets from the BioLiP2 dataset were used to compute the correlation coefficient (shown in Fig. S7). Pearson correlation analysis demonstrated a robust correlation of 0.96 between ligand Tanimoto similarity and pocket cosine similarity derived from the ErePOC vector, highlighting ErePOC’s aptitude for capturing meaningful pocket–ligand interactions. Furthermore, five pockets were randomly chosen from each of the seven ligand-binding types to compute pairwise cosine similarities. The heatmap in Fig. S8 compares the similarity results using the ESM-2 embeddings, ErePOC vectors, and t-SNE 2D projections following ErePOC transformation. The cosine similarity computed from ErePOC representations effectively distinguished between various pocket types, whereas ESM-2 demonstrated limited discriminatory power. Moreover, cosine similarity based on t-SNE-reduced representations also performed poorly in assessing pocket relationships. In summary, ErePOC proves highly proficient in identifying pockets that bind ligands with analogous structural features.

We designed another downstream classification task involving the prediction of the seven ligand-binding pocket types using few-shot learning with ESM-2 and ErePOC representations. For independent testing, 10% of the targets were held out, ensuring a comprehensive evaluation of the model’s performance. Given the relatively straightforward nature of this classification task, all models achieved strong results. In this few-shot learning setup, we tested four models: ErePOC-NN, ErePOC-SVM, ESM-2-NN, and ESM-2-SVM. The ErePOC-NN and ErePOC-SVM models used pocket representations derived from contrastive learning as input features, paired with either an NN or a support vector machine (SVM) classifier, respectively. Similarly, ESM-2-NN and ESM-2-SVM relied on direct embeddings from ESM-2, utilizing NN and SVM classifiers.

Figure S9 compares the performance of these models on the testing dataset. ESM-2-NN achieved the highest overall accuracy (0.989), followed closely by ErePOC-NN (0.986) in classifying the seven ligand-bound pocket types. We note that the MaSIF-ligand model, trained using the MaSIF representation, achieved an accuracy of 0.74 for the same task, although the result was obtained on a different test set14. Interestingly, when assessing the performance of the SVM models with an RBF kernel, ESM-2-SVM’s accuracy dropped significantly to 0.811, while ErePOC-SVM maintained a high accuracy of 0.985. This notable difference underscores the superiority of contrastive learning in generating robust representations for functional pocket classification. It also highlights ErePOC’s capability to generalize to diverse or previously unseen pockets, whereas ESM-2’s pre-trained features appear less effective for this specific task without further fine-tuning. We note that for ErePOC model training and downstream analyses, the original BioLiP2 binding residue annotations were used to preserve experimentally validated contacts. Training and analyses using the alternative 5Å pocket definition yielded highly similar results, as demonstrated in Figs. S8–S10.

Clustering and characterization of DEL pockets in experimental and predicted protein landscapes
The primary goal of this study is to explore the distribution of drug-related and lead-binding pockets across the entire protein space. Using the ErePOC representation, we projected the FDA-AD and DEL datasets onto the comprehensive pocket landscape generated using the BioLiP2 dataset (Fig. 4C, D) and the AlphaFill dataset (Fig. S11). t-SNE visualization illustrates that experimentally resolved metabolite pockets are organized into distinct localized regions (Fig. 4C), demonstrating ErePOC’s ability to differentiate functional pockets. Furthermore, pockets bound to approved drug molecules (FDA-AD, shown in purple in Fig. 4D) are widely distributed throughout the protein space, rather than being confined to specific clusters, highlighting their versatility. Figure 4D also highlights the similar distribution patterns of DEL pockets (dark gray) and FDA-AD pockets, which scatter across the latent space. This spatial concordance aligns with previous Fpocket analyses, which emphasized similarities in their physicochemical properties. Together, these results suggest that DEL screening can access the majority of known druggable targets, while exhibiting a measurable preference for pockets with features conducive to DEL hit generation, such as larger volume and higher hydrophobicity.

The ErePOC representation partitions the BioLiP2 pocket space into distinct patterns, offering critical insights into the global pocket landscape. For example, pockets bound to native ligands such as SAM or HEM are notably absent from the DEL and FDA-AD chemical spaces, suggesting that these tightly bound, cofactor-associated pockets may be less suitable for conventional DEL screening. To further explore the “DEL-able” regions of the pocket landscape, we identified the five nearest neighbors for each DEL target in the BioLiP2 dataset based on cosine similarity (Fig. S12). A total of 361 pockets in BioLiP2, referred to as DEL neighbors, exhibited cosine similarity scores greater than 0.8. The physicochemical properties of these DEL neighbors, as computed using Fpocket, are summarized in Fig. S13. The average pocket volume for the DEL neighbors changed from 1612.84 to 2038.69 Å³, corresponding to an approximate 26.4% increase relative to the average volume of natural pockets in the BioLiP2 dataset. The average number of alpha spheres for DEL neighbors changed from 69.35 to 92.35, reflecting a 33.2% increase and indicating higher structural complexity. Furthermore, the mean local hydrophobic density for DEL neighbors increased from 14.98 to 21.71, representing a 44.9% enhancement, emphasizing their pronounced hydrophobic nature.

In terms of interaction profiles, hydrophobic interactions in DEL neighbors changed from 30.1 to 42.0%, reflecting a 36.7% increase compared to BioLiP2 pockets. Hydrogen bond interactions, however, decreased from 9.9 to 7.0%. Polar interactions in DEL neighbors increased from 9.8 to 14.8%, showing a 51.0% rise in polarity relative to the BioLiP2 pockets. In total, these DEL neighbors exhibit larger volumes and greater spatial expansiveness than typical natural pockets in BioLiP2, with physicochemical properties that closely resemble those of both DEL and FDA-AD pockets. The similarities of DEL neighbors make them promising candidates for DEL screening. Furthermore, the ligand–pocket interaction profiles of DEL neighbors, DEL pockets, and FDA-AD pockets were found to be comparable, as illustrated in Fig. S13G-I, reinforcing the similarity in their functional characteristics.

Identification and characterization of human protein pockets for DEL screening
We screened AlphaFold2-predicted human proteins (23,391) to predict binding pockets most likely suitable for DEL screening31. First, Fpocket was used to identify pockets in these human proteins. For each pocket, the average pLDDT of its constituent residues was calculated as the pocket pLDDT score. Pockets with a volume smaller than 800 Å³ or a pLDDT score below 0.7 were excluded, yielding a total of 182,424 human protein pockets. The 800 Å³ threshold was selected based on prior studies suggesting 500 Å³ as a minimal druggable pocket volume32, together with our observation that DEL‑binding pockets are substantially larger. These pockets were then encoded using ErePOC embeddings, and their cosine similarity to 128 known DEL pockets was computed, from which the highest similarity was assigned to each pocket. Ultimately, 4774 pockets with a cosine similarity greater than 0.8 were identified. After removing duplicates based on UniProt ID, 2739 unique human proteins were predicted to contain DEL-compatible pockets. The overall prediction workflow is illustrated in Fig. 5A.

Among the predicted human proteins, 17.9% were classified as transferases, 11.6% as hydrolases, and 9.4% each for DNA-binding proteins and oxidoreductases. In comparison, the 128 DEL target proteins were predominantly transferases (27.1%), followed by hydrolases (17.4%) and receptors (9.7%). The FDA-AD dataset, which encompasses 31 protein families, showed a similarly broad distribution, with transferases (20.8%), hydrolases (18.1%), oxidoreductases (14.8%), DNA-binding proteins (7.3%), and receptors (6.9%) as the most frequent classes. Enrichment scores for each protein class were calculated using a hypergeometric test, and Fig. 5C depicts the distribution of enrichment scores for proteins with p values < 0.05. Notably, several classes, including oxidoreductases, multifunctional enzymes, transferases, chromatin regulators, lyases, and isomerases, exhibited enrichment scores ranging from 1.36 to 6.24 in both the DEL target set and the predicted human protein dataset. Interestingly, transferases, hydrolases, and oxidoreductases are consistently enriched across all three datasets (DEL, FDA-AD, and predicted DEL-compatible pockets), underscoring their central roles as preferred families for DEL screening.

Among known DEL targets, three multifunctional enzymes—AASS (aminoadipate-semialdehyde synthase, PDB: 5L78), PSMA (proteasome subunit alpha, PDB: 1Z8L), and sEH (soluble epoxide hydrolase, PDB: 1ZD4)—resulted in the strongest enrichment. These enzymes catalyze two or more distinct biochemical reactions, which explains their dual classification in our analysis (e.g., sEH is counted as both a hydrolase and an oxidoreductase). The significant enrichment of multifunctional enzymes (p<0.05) suggests that DEL molecules may preferentially target proteins with complex structural architectures and flexible pockets that support multiple catalytic activities.

Figure S14 presents a t-SNE visualization of the distribution of DEL pockets within human proteins, alongside human protein pockets with cosine similarity > 0.8. Consistent with findings from the BioLiP2 and AlphaFill datasets, DEL pockets exhibit a broad and diverse distribution. Notably, human protein pockets closely resembling DEL pockets cluster into three distinct groups. However, a substantial number of DEL pockets lack highly similar counterparts among human proteins. This disparity may arise from the limitations of AlphaFold2 in predicting accurate protein structures or the potential inaccuracies of Fpocket in identifying binding pockets, both of which could impact the detection of DEL-like pockets across the human proteome.

Global and local structure comparison for human DEL targets
Figure 6 presents case studies comparing global and local pocket structures for protein classes enriched in both predicted and known DEL targets. Using ErePOC embeddings, the cosine similarity scores of binding pockets within each class were calculated. Pockets with high cosine similarity and UniProt IDs assigned to the same protein class were grouped for comparative analysis of their global and local structural features. Global structural similarity was assessed using TM-align33, which computes the TM score measuring the 3D similarity of overall protein structures, while pocket-level similarity was evaluated using PPS-align34, which reports the pocket similarity (PS) score measuring the 3D similarity of pockets. Both scores range from 0 to 1, with higher values indicating a more similar topology. Specifically, a PS score greater than 0.46 indicates that the pocket has a similar structure34. Illustrated in Fig. 6A–D are four representative cases, each from the oxidoreductase, multifunctional enzyme, transferase, and hydrolase classes, that exhibit high similarity in latent representation space despite dissimilarity in both global protein and local pocket structures. These cases indicate that contrastive learning may capture potential functional or physicochemical relationships between binding pockets that are not fully explained by global protein fold or local geometric similarity. Comparable observations have been reported in earlier studies, where pockets binding the same ligand (e.g., ATP) exhibit considerable geometric diversity20, and functional associations are detectable across distinct structural folds35. Although further experimental evidence will be required to substantiate such relationships in our predictions, these findings suggest that embedding-based similarity could provide information complementary to conventional structure alignment methods and may offer hypotheses for future exploration.

In contrast to the above examples, we also identified instances with consistently high similarity in the latent pocket representation space and in global and local structures. Two such examples—one from the lyase class and another from the chromatin regulator class—are illustrated in Fig. 6E, F, exhibiting strong structural similarity at both levels, with TM scores exceeding 0.9 and PS scores above 0.5. We note that some divergence in pocket residues may arise due to the sensitivity of pocket definition, yet the latent space representation appears robust to such variation. Figure 6G illustrates a case involving a known DEL target (FKBP) and the predicted protein target P26885 (FKBP2), which shares a TM score of 0.85, indicating strong global structural similarity. However, their pocket similarity score is only 0.24, likely due to the flexible and extended nature of the binding pocket, which severely limits the effectiveness of rigid-body 3D alignment of local structures. Despite this, ErePOC identified a high cosine similarity of 0.81 in the pocket latent space, reasonably suggesting that FKBP2 should also be a target accessible to DEL molecules.

Our analysis is not restricted to the functional classes annotated in UniProt. For example, ErePOC identified potential ligand-binding similarity between the RNA-binding protein NOP56 (UniProt: Q8TAS1) and the SAM-dependent methyltransferase TrmD (PDB: 1UA2), despite their distinct canonical biological roles. A moderate TM score (0.75) suggests a shared Rossmann-like fold, while a moderate PS score (0.43) may arise from conserved cofactor-binding architectures (e.g., SAM/RNA interaction interfaces). This observation implies that DEL-derived chemotypes targeting TrmD’s catalytic pocket could potentially engage other RNA-modifying enzymes with analogous structural features.

These findings highlight that while global and pocket-level structural similarities can indicate spatial configurations, they do not fully characterize binding pocket functionality. The inherent flexibility of binding pockets and the conformational selection or induced fit that occurs during ligand binding further complicates their structural representation. ErePOC’s robustness stems from its integration of ESM-2 embeddings, which incorporate evolutionary information at the amino acid level to generate high-dimensional representations of physicochemical properties. Moreover, contrastive learning enhances the specific functional characterization, offering resilience to structural distortions induced by ligand binding. These underscore ErePOC’s utility in providing a comprehensive and functionally relevant representation of binding pockets.

As a further validation, we designed a large-scale in silico DEL screening experiment against 14 selected human protein targets to compare their capability to bind DEL-like compounds (Fig. S16). Six of these targets, each drawn from a different DEL-enriched functional family identified in our earlier analysis, contained a pocket exhibiting high ErePOC cosine similarity (>0.8) to known DEL pockets. These include a chromatin regulator (O15294), a hydrolase (P03951), an isomerase (P26885), a lyase (P43166), a multifunctional enzyme (P14060), and an RNA-binding protein (Q8TAS1). As a comparison group, six additional targets were selected from DEL-neutral protein families, which did not show enrichment but nevertheless contained pockets with high ErePOC similarity (>0.8). These include a signal transduction inhibitor (O14508), a mitogen (Q9H706), an elongation factor (P43897), an actin capping protein (P47756), a hypotensive agent (P68871), and a cyclin (Q5T5M9). In addition, two closely related proteins, MAT2A (P31153) and MAT2B (Q9NZL9), were included as a family-level case study. The catalytic MAT2A contains a small, polar pocket with a maximum ErePOC similarity of only 0.66 to known DEL pockets. In contrast, the regulatory MAT2B, though non-catalytic, harbors a larger and more hydrophobic pocket with a much higher similarity (0.93), providing an illustrative example of differential DEL compatibility within a single protein family.

Docking calculations were performed using a published virtual DEL compound library (approximately 2.8 million molecules) sampled from 15 sublibraries of the three-cycle HitGen Open DEL library36. No DNA tags were added, so they represent off-bead synthesized molecules. Motivated by previous studies showing that more favorable (i.e., more negative) docking scores correlate with higher experimental hit rates, and that larger libraries improve discovery efficiency37,38, we evaluated whether ErePOC-identified pockets exhibit similarly favorable trends for DEL compounds. Standardized docking Z-score distributions, calculated using the global mean and standard deviation of docking scores across all compounds, are shown in Fig. 7A. Targets from DEL-enriched families exhibited stronger predicted binding, with more negative mean Z‑scores (−2.18 vs −1.07) and docking scores (−7.45 vs −6.15 kcal mol⁻¹) compared to those from DEL‑neutral families. The difference was also statistically significant when examining the top 1% scoring compounds per target: DEL-enriched targets yielded mean top 1% docking scores ranging from −8.93 to −11.96 kcal mol⁻¹ (Z=−1.54 to −3.73), whereas DEL-neutral targets ranged from −5.49 to −9.73 kcal mol⁻¹ (Z=+0.95 to −2.12) (Fig. 7B). Monte Carlo resampling over 1000 iterations (Fig. 7C) confirmed the robustness of these trends across relevant cutoffs (−10 to −8 kcal mol⁻¹), supporting a significant difference in the predicted ability to bind DEL molecules as measure by docking scores. These differences were also evident in the distribution of docking scores (Fig. 7D and Supplementary Fig. S17).

Methods
Construction of ligand–pocket complexes datasets
Our study incorporates the following datasets (summarized in Table S1), which include both experimentally determined and computationally predicted structures of ligand–protein complexes, as well as datasets related to DELs and FDA-approved drugs. The first dataset consists of high-resolution experimental structures sourced from BioLiP2 (updated as of March 9, 2024), a semi-manually curated database focused on biologically relevant ligand–protein binding interactions. We extracted only regular ligands, excluding metals, peptides, DNA, and RNA, resulting in a final dataset of approximately 326,416 ligand–protein complexes. We specifically chose BioLiP2 rather than using all PDB entries with ligands because BioLiP2 systematically filters out crystallization artifacts and non-functional ligands, thereby ensuring greater biological relevance and reliability.

In BioLiP2, binding sites are originally defined based on functional residue annotations. Specifically, a protein residue is labeled as a binding residue if at least one of its atoms is within a distance equal to the sum of the van der Waals radii plus 0.5 Å of any atom of the bound ligand, and if there are at least two such inter-atomic contacts, the residue is considered part of the binding site. To ensure methodological consistency across datasets, we additionally defined binding pockets using a simple geometric criterion, including all protein atoms within 5 Å of any non-hydrogen atom of the bound ligand. All analyses were repeated using this definition, and the results remained consistent across both definitions (Figs. S8–S10 and S14–S15).

Additionally, we curated predicted ligand–protein complex structures from the AlphaFill database. AlphaFill transfers ligands, cofactors, and metal ions to AlphaFold-predicted models based on sequence and structural similarities. However, due to potential clashes between the transplanted ligands and the predicted receptor structures, the quality and resolution of the predicted complexes were relatively low. To improve the structural quality of AlphaFill entries, we implemented a steric clash detection procedure using an in-house Python script. Steric clashes were defined as any pair of non-hydrogen atoms separated by less than 1.2 Å. For each pocket and ligand, we calculated a clash score as the fraction of atom pairs that fell below this threshold. Protein-ligand clashes were quantified as the number of atom pairs between the pocket and ligand with inter-atomic distances shorter than 1.2 Å. Structures were discarded if the pocket clash score was ≥0.005, or the ligand clash score was ≥0.01, or if the number of protein-ligand clashes was ≥5. This procedure removed approximately 11% of the AlphaFill dataset, yielding 293,019 relatively high-quality ligand–protein complexes for downstream analysis.

Our third dataset focused on FDA-approved drugs, collected from the DrugBank database. We gathered information on 2863 FDA-approved drugs and used their SMILES representations to search the Protein Data Bank (PDB) for corresponding drug–protein complexes. In total, 5286 potential drug–protein complex structures were identified. Ligands in these structures were marked using the “HET” keyword, and Tanimoto similarity based on Morgan fingerprints (radius=2) was calculated using RDKit (version 2024.03.2) between these ligands and the FDA drug list. A ligand was considered an FDA-approved drug only if its Tanimoto similarity equaled 1.0, ensuring exact chemical identity. Pockets were defined as amino acid residues within 5 Å of these ligands, resulting in a final dataset of 340 unique crystal structures, each corresponding to a distinct FDA-approved (FDA-AD) drug–protein pair. Multiple targets of the same drug were retained, but redundant crystal structures of the same drug–target combination were removed.

The fourth dataset was specifically developed for DEL compounds. This dataset was compiled from the DELOpen platform [https://www.delopen.org/] by WuXi AppTec, with the latest update in April 2023. We analyzed target proteins and their binding residues using public references, and corresponding structures were downloaded from the PDB. For each target protein, only the DEL hit with the best binding affinity was retained. The 3D conformation of each ligand was generated using energy minimization in the MOE. Induced fit docking was then performed in MOE to predict the binding poses of the DEL hits with the target proteins. If the experimental structure of a DEL hit–target protein complex was available, it was included in the dataset instead. This dataset ultimately consisted of 128 unique DEL hit–target protein structures. The corresponding SMILES strings of these ligands are provided in Supplementary Table S2. To ensure methodological consistency, we note that redundancy was handled differently across datasets depending on their intended use. Specifically, the BioLiP2 and AlphaFill datasets intentionally retain multiple structures bound to the same ligand, thereby exposing the model to diverse conformational states and binding environments. In contrast, for the FDA-AD and DEL datasets, we applied stricter curation: the FDA-AD set was reduced to unique FDA drug–protein pairs (removing redundant crystal structures for the same drug target), while the DEL set was limited to one highest-affinity hit per target protein.

Finally, we downloaded 23,391 predicted human protein structures from the AlphaFold Protein Structure Database (Homo sapiens species). Each of these proteins was processed using the Fpocket tool to predict potential binding pockets. To analyze the enrichment of amino acids in our benchmark datasets, we referenced the ratios of the 20 types of amino acids that are enriched in the PDB database. These ratios were obtained from NIMBioS. (https://legacy.nimbios.org//~gross/bioed/webmodules/aminoacid.htm).

Pocket physicochemical characterization and shape similarity analysis
We used the Fpocket 3.027 tool to extract pocket descriptors from experimental structures in BioLiP2, FDA-AD, and the DEL datasets. These descriptors include volume, number of alpha spheres, proportion of apolar alpha spheres, mean local hydrophobic density, density of alpha spheres, and proportion of polar atoms. These properties were calculated to provide insights into the physical characteristics of the binding pockets. Specifically, volume reflects the size of the cavity, with larger values indicating larger pockets. The number of alpha spheres and the proportion of apolar alpha spheres capture the geometric and hydrophobic features of the pocket, with a higher proportion of apolar spheres suggesting a more hydrophobic environment. The mean local hydrophobic density serves as a descriptor for the overall hydrophobicity of the pocket, with higher values indicating greater hydrophobicity. The density of alpha spheres provides an indication of the packing density of the pocket, while the proportion of polar atoms helps assess the hydrophilicity of the binding site.

Pocket–ligand interaction analysis using Arpeggio
We analyzed pocket–ligand interactions using the Arpeggio28 tool, which identifies contacts at the pocket–ligand interface. These contacts were extracted from the “.bs_contacts file” file generated by Arpeggio. The interaction types included in our analysis were van der Waals, hydrogen bonds, weak hydrogen bonds, halogen bonds, ionic interactions, aromatic interactions, hydrophobic interactions, carbonyl interactions, polar interactions (contacts between polar atoms that do not meet the geometric criteria for hydrogen bonds), and weak polar interactions. For each ligand–pocket complex, we summed the number of interactions of each type between the ligand and pocket atoms. The proportion of each interaction type was calculated by dividing its count by the total number of interactions, allowing us to compare the relative contributions of different interaction types to the overall binding profile.

Statistical analysis and PCA projection
Nine pocket and interaction features were extracted using Fpocket and Arpeggio. The distributions of these features in the FDA-AD, DEL, and BioLiP2 datasets were first assessed for normality. As most features deviated from normality and the sample sizes were unequal, non-parametric statistical methods were employed. Specifically, the Kruskal–Wallis H test was used to examine overall differences across datasets, followed by pairwise Mann–Whitney U tests when significant, with Cliff’s δ calculated to quantify effect sizes. All analyses were performed in Python using the SciPy library, and statistical significance was reported as p values. To explore differences in the pocket feature space, BioLiP2 features were standardized (mean=0, variance=1) and subjected to PCA analysis. DEL and FDA-AD data were then projected onto the BioLiP2 PCA space, and scatter plots were generated to visually compare the distributions of the three datasets.

Evaluation of molecular properties and ligand similarity
We evaluated the molecular properties of ligands from the BioLiP2 dataset, DEL hits, and FDA-approved drugs. The properties analyzed included the number of hydrophobic atoms (a_hyd), the number of hydrogen atoms (a_nH), the log octanol/water partition coefficient (logP), the log solubility in water (logS), and the topological polar surface area (TPSA). These properties were plotted to visualize their distribution across BioLiP2 ligands, FDA-approved drugs, and DEL hits. Ligand similarity was calculated using extended connectivity fingerprints (ECFP4), generated with the RDKit (version 2024.03.2) package49, to compare the molecular structures and assess their similarities across the datasets.

ErePOC model architecture
Pocket featurization. The input to the ErePOC model is the full-length amino acid sequence of the target protein. Each amino acid in the sequence is transformed into an embedding vector using the ESM-2 model. The embedding vector E 2 Rd t has a default dimension of d t=1280 as defined by ESM-2. The binding pocket is represented by a vector P 2 Rd t, which is computed as the mean of the embedding vectors for the amino acids located within the binding pocket. This mean pooling process generates a feature vector that effectively captures the overall characteristics of the binding pocket.

Mapping into a common latent space. To compare pockets in a shared latent space, we first map the pocket embeddings P 1 2 Rd t and P 2 2 Rd t into a common latent space. This is achieved by applying a fully connected layer with GELU (Gaussian Error Linear Unit) activation, which transforms the pocket embeddings into latent vectors T* 1 2 Rh and T* 2 2 Rh. The transformation is parameterized by weight matrices W 2 Rh*d t, and bias vectors b 2 Rh, described by the following equation:

T*=GELU(WP+b) (1)

Once we obtain the latent embeddings T* and T*, their cosine similarity is computed to measure the distance between the two pockets:

CosineSimilarity(T* 1, T* 2) = (T* 1 · T* 2) / (||T* 1||2 · ||T* 2||2) (2)

Although the pockets themselves are not directly annotated in our dataset, each pocket is associated with a specific binding ligand. To differentiate between pockets, we use the KL divergence as the loss function. The idea is that pockets whose ligands are similar—whether in structure, function, or chemical properties—should be represented closer together in the latent space. The loss function is defined as:

loss = P ij log (P ij / Q ij) (3)

Here, Q ij represents the similarity between the ligands bound to pockets i and j, computed using the Morgan fingerprint, which encodes each molecule into a fixed-dimensional embedding M 2 Rd m, where d m=2048. This formulation ensures that the model distinguishes between pockets by leveraging the similarity of their associated ligands, while guiding the pockets to be represented closely in the latent space if their ligands are similar.

Implementation. Model training was performed using the Adam optimizer with an initial learning rate set to 0.001. Early stopping with a patience of 100 epochs was applied, saving the model with the lowest validation loss. The maximum number of epochs was set to 2000. Each training epoch consisted of 4096 batches with 128 samples per batch, while validation used 256 batches of 128 samples. The model architecture included a 2-layer NN, with intermediate dimension d=512 and output dimension d=256. Each layer applied batch normalization, followed by dropout (rate=0.1), and GELU activation. The model was implemented in PyTorch version 2.3.1 and trained on a machine equipped with NVIDIA L40 GPUs.

Validation through binding pocket classification. To assess the performance of ErePOC, we constructed a dataset of approximately 43,000 protein binding pockets, each labeled with one of seven types of bound ligands: ADP, COA, FAD, HEM, NAD, NADP, and SAM. We then designed a downstream classification task to evaluate the ability of our model to distinguish between these different binding pockets. For a comprehensive evaluation, we compared our model against three baseline models using zero-shot learning: ErePOC-NN, ESM-2-NN, ErePOC-SVM, and ESM-2-SVM. In ErePOC-NN and ErePOC-SVM, the pocket representations derived from contrastive learning were used as input features, with classifiers being NN or SVM, respectively. Similarly, in ESM-2-NN and ESM-2-SVM, the features directly extracted from the ESM-2 model were used, with NN and SVM classifiers employed for the classification task. The dataset was split 9:1 into training and test sets. Model performance was evaluated using precision, recall, and F1-score:

Recall = TP / (TP + FN) (4)

Precision = TP / (TP + FP) (5)

F1-score = 2 × (Precision × Recall) / (Precision + Recall) (6)

Hypergeometric test for enrichment analysis
In this study, we applied a hypergeometric test50 to assess the enrichment of human proteins whose binding pockets exhibit high cosine similarity (greater than 0.8) to pockets known to bind compounds in DELs. The goal was to determine whether specific protein families or functional categories are significantly enriched within the subset of human proteins containing DEL-suitable pockets. For each human protein in the predicted dataset, we retrieved functional annotations using UniProt IDs and categorized them according to molecular functions and protein families, as defined in the UniProt database. This classification allowed us to categorize the human proteins into specific functional groups, such as enzyme families or cellular processes. We then calculated the enrichment score (ES) for each protein class, comparing its representation in the subset of DEL-suitable human proteins to its representation in the overall human proteome. Specifically, the enrichment score for a given protein class i was defined as:

Enrichment score (ES) i = (N i (M) / N M) / (N i (total) / N total) (7)

where N i (M) is the number of proteins of the i-th classification in the subset M, which includes the human proteins with pockets that exhibit cosine similarity > 0.8 to known DEL pockets. N i (total) is the total number of proteins of the i-th classification in the entire human protein dataset. N M and N total are the total numbers of proteins in subset M and the entire dataset, respectively. An enrichment score greater than 1 indicates that the given protein class is overrepresented in the DEL-suitable subset. To evaluate statistical significance, we computed the p value using the hypergeometric distribution:

p(observed) = (N i (total) choose N i (M)) * ((N total - N i (total)) choose (N M - N i (M))) / (N total choose N M) (8)

Virtual DEL screening by docking
A virtual DEL comprising approximately 2.8 million small molecules was obtained from a publically available dataset36. This dataset was originally developed to benchmark DEL-based hit discovery strategies and represents a chemically diverse and synthetically tractable compound space suitable for DEL screening. All compounds were downloaded in SMILES format and preprocessed using OpenBabel (v3.1.1)51. Three-dimensional conformations were generated using OpenBabel’s OBBuilder module, with hydrogen atoms added to each molecule. Energy minimization was then performed using the MMFF94 force field. After preprocessing, a total of 2,778,654 unique DEL molecules were retained for virtual screening.

Docking was performed using AutoDock Vina (Version 2.0)52. Receptor and ligand structures were converted from PDB or MOL2 formats to PDBQT with MGLTools (v1.5.7). Receptor conformations were kept rigid, while ligand torsions were treated as flexible. The docking grid was defined as a 15 Å cubic box centered on the predicted DEL-compatible pockets with ErePOC. The default Vina scoring functions and search parameters were applied, with the exhaustiveness set to 8. For each ligand, the top five docking poses were generated, and the pose with the lowest binding energy was selected for subsequent analysis.

Docking scores for each compound were normalized using Z-score transformation:

Z i = (S i - μ all) / σ all (9)

where S i is the docking score of compound i, and μ all and σ all are the mean and standard deviation of docking scores. The enrichment factor (EF) at a given docking score cutoff c was defined as:

EF(c) = (N pos (c) / N pos,total) / (N neg (c) / N neg,total) (10)

where N(c) and N total denote the number of compounds with docking scores ≤ c and the total number of compounds, respectively. The subscripts pos and neg refer to the DEL-enriched and DEL-neutral target sets.

To evaluate the statistical significance and robustness of the observed differences between DEL-enriched and DEL-neutral targets, the top 1% of docking scores per target were extracted and converted to Z-scores, and the distributions of these top 1% Z-scores were compared between groups using a one-sided Mann–Whitney U test. In parallel, a Monte Carlo resampling procedure was applied to the same subsets, in which 200 compounds were randomly sampled from the top 1% compounds per target over 1000 iterations. For each iteration, the mean ± standard deviation across iterations was used to quantify the variability and stability of the EF values.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Noteworthy results:\nThe authors have developed a method, ErePOC, for creating ligand binding pocket representations and assess their\nfunctional similarity. They incorporated their embeddings into the ESM-2 contrastive learning method. They applied their\nrepresentation method to a set of experimentally determined protein-ligand structures in the BioLiP2 database. The BioLiP2\ndatabase is a semi-curated set of proteins bound to native ligands. They demonstrated the ability of their method to\naccurately group pockets that bound the same ligand. They compared this method to performance of ESM-2 embeddings\nwhich were not able to distinguish between binding pocket classes. They applied their method to a set of experimentally\nderived protein-ligand structures from the OpenDEL database. The OpenDEL database contains experimental protein\nstructures complexed to DEL hits. The resulting DEL pocket representations were applied to a filtered set of structures from\nthe AlphaFold2 database of predicted protein structures that contained binding pockets identified using F-pocket. They\nidentified proteins from the AlphaFold database that have binding pockets similar to those known to bind DEL molecules. They\npropose that this method can be used to identify targets suitable for DEL screening, virtual screening, used to identify\nbinding pockets for a particular ligand type, and optimize binding pockets for a specific ligand.\nSignificance:\nThe method they have developed for binding pocket representations outperforms ESM-2 in its ability to group similar binding\npockets. Generalizability based on test set performance was equally good for ErePOC-NN, ErePOC-SVM and ESM-2-NN\nmodels. While the analyses comparing the properties of pockets calculated using F-pocket are interesting, they do not\nrepresent a significant advancement to the field.\nAdditional experiments:\nThey identify pockets that they expect to bind DEL molecules but do not provide any experimental validation that their\npredictions are accurate. I suggest that the authors choose representative proteins from each of the 7 highly enriched DEL\nprotein classes (Fig. 5) and perform a DEL screen. As a control pick representatives from 7 protein classes that were not\nhighly enriched. If their predictions are correct, they should observe a significantly higher number of enriched DEL molecules\nin the positive set relative to the negative set.\nFlaw in data analysis and interpretation:\nOn page 5, line 118, they describe how they defined binding pockets from the different datasets. For the BioLiP2 dataset,\nthey used functional annotations provided by the database curation. For the FDA, DEL and AlphaFill datasets, they define\nthe binding pocket by distance from the bound ligand. They don’t explain why they used different criteria for defining the\nbinding pockets.\nPage 6, line 124 states “Drug candidates, being larger and chemically more complex than natural ligands, are specifically\ndesigned to exhibit stronger affinity.” The authors do not cite a reference or provide an analysis to demonstrate that the\nstatement is true. They should remove that statement or provide evidence.\nPage 6, line 131 states that “DEL pockets were larger than FDA-AD ones, which aligns with the general understanding that\nDEL molecules are larger, with average molecular weights of 1298.5 Da and 218.3 Da in our datasets, respectively.” When\nevaluating the graphs in Figure S2, the molecular weights of the DEL molecules are higher than those of the FDA-AD\nmolecules but the average MW of the DEL molecules appears to be much lower based on the distribution shown. The\nmajority of the DEL molecules are in the 300-700 Da MW range. This is also inconsistent with the data analysis shown in\nFig. 2A & B indicating that the volume of binding pockets is similar between the FDA-AD and DEL datasets.\nPage 7, line 170 states “Overall, these results indicate that the binding of DEL hit molecules relies more on pocket shape\ncomplementarity and can be further optimized towards drug molecules.” I don’t follow how they made that conclusion based\non the analysis and results described in section 2.2 and shown in Fig. 2.\nPage 11, line 275 “The ErePOC representation partitions the BioLiP2 pocket space into distinct patterns, offering critical\ninsights into the global pocket landscape. For example, pockets bound to native ligands such as SAM or HEM appear less\nsuitable as drug targets - particularly in the context of DEL screening. In contrast, pockets bound to ADP seem more flexible\nto accommodate designed molecule.” These interpretations appear to be based on the analyses shown in Fig. C and D.\nWhile it is true that the chemical space corresponding to SAM or HEM bound structures lacks representatives from the DEL\nand FDA datasets, the chemical space corresponding to ADP bound structures has very few representatives from the DEL\nand FDA datasets.\nBased on the description of database curation described in section 4.1, it isn’t clear to me if datasets were consistently\nconstructed. The BioLiP2 dataset has multiple structures that are bound to the same ligand. The description of the process\nused for the AlphaFill structures given on page 16 lines 444-450 doesn’t indicate if the 330,434 are all unique or have\nredundant/similar ligands. The set of FDA molecules is described on page 17, line 458 as being 630 unique crystal\nstructures. Does that mean they are 630 unique ligand-protein combinations? Page 17 line 462 “For each target protein, only\nthe DEL hit with the best binding affinity was retained.” resulting in 128 unique structures.\nComments:\nPage 2, line 15 states that “few DEL-derived compounds have advanced to clinical trials or reached the market.” I disagree\nwith that assertion. I would refer the authors to a nice review written by David Liu in which he performed a comprehensive\nanalysis of DEL derived molecules that have entered the clinic. Nat Rev Drug Discov. 2023 Jun 16;22(9):699–722.\ndoi:10.1038/s41573-023-00713-6. While the total number of DEL molecules that have reached the clinic is comparatively\nlow relative to molecules identified by high-throughput screening, DEL has only recently been adopted as a standard\napproach for hit identification.\nThe authors included structures from three databases of experimentally derived structures, BioLiP2, FDA and OpenDEL,\nand two databases of predicted structures, AlphaFill and AlphaFold. It would be interesting to know why they didn’t include\nin their comparisons the human protein structures from the Protein Data Bank with small molecule ligands not included in\nthe BioLiP2, FDA and OpenDEL databases.\nSection 2.5 described the identification of pockets from the human proteome that are suitable for DEL screening. The set of\nAlphaFold2 predicted human protein structures was filtered prior to analysis with ErePOC2. One of the filters applied was to\nremove proteins that had pocket volumes less than 800 A3. On page 7, line 151 they indicate that the average volume of a\nDEL molecule binding pocket is 2723.3 A3. They should explain why they decided to use 800 A3 as their volume cutoff.\nFigs. 2A and S10A are lacking units for the X-axis.\nFigs. 2C and S10C the legend below the graph has a typo: “polor” should be “polar”\nFig. S3 should include a legend indicating the corresponding binding sites and colors as was done in Fig. 4.", "reviewer_2": "The authors describe a method to represent the properties of protein pockets that captures both structural and functional features, and use this method to investigate DEL target space.\nThe paper is well written, but we have a number of comments around the methodology.\nComments:\nThe DEL dataset contains 128 entries. It would be useful if the authors included the associated SMILES strings for the\nligands and a depiction of their structures (as supplementary information).\n“We hypothesize that DEL targets exhibit unique pocket characteristics that can be utilized in drug design decision-making,\nsuch as target prioritization and hit identification.” Does this study shed any light on these unique pocket characteristics? Or\nindeed is the conclusion that the pocket characteristics are similar to general druggable pocket?\nP6, L123: “DEL and FDA-AD pockets were notably larger than those in BioLiP2 and AlphaFill, likely due to differences in\nligand characteristics.” I do not believe that thisshows that the pockets are larger, merely that the ligands are larger, and\nhence the number of residues within 5A. The pocket size is accurately assessed in Section 2.2.\nP7: Across the various properties, is the difference between DEL and FDA-AD statistically significant? Is there any evidence\nthat the targets for which DEL has been run differ from the set of all druggable targets?\nThe FDA-AD compounds are the result of a lead optimisation process that would have eliminated functional groups that are\nproblematic for metabolism. The DEL compounds are investigational compounds and their structures are expected to be\nless optimal. This might explain why ionic interactions were found in the DEL dataset. If the authors had included the\nassociated ligand structures for the DEL dataset, this would be easier to assess.\nThe phrase “DEL pockets” seems a bit misleading. These are just pockets where DEL ligands have been reported.\nPresumably, small molecules have also been reported for these pockets, or is this not the case?\nP 7, L174: “DEL pockets possess unique features in their physicochemical properties, distinguishing them from general\nprotein pockets.” The results shown seem to indicate that their properties are similar to those for FDA-AD.\nFig 5: If prediction of human protein targets suitable for DEL screening had been done instead with the Drug-AD pockets\nwould there have been any difference in the results? If so, this would be interesting to report. What I am trying to figure out is\nwhether the ‘DEL-able’ pocket space really is different than general druggable pocket space. On P11, it is stated that\n“Together, these results suggest that DEL screening can readily access most of the known druggable target space.” which\nwould indicate that there is little difference.\nP. 13: “These examples highlight how contrastive learning imparts the capacity to detect higher-level functional or\nphysicochemical similarities that are not solely reflected by either global or local 3D structural alignment.” Are these not false\npositives, or is there experimental evidence that these bind very similar ligands?\nP17: “A ligand was considered an FDA-approved drug if its Tanimoto similarity exceeded 0.7.” Unless the similarity is 1.0,\nthe structure is different. It may be a tautomer, in which case you could use the InChI to check for identity. But otherwise,\nthese will not be the same molecule, and I believe that it is incorrect to state that these are FDA-approved drugs. They\nsimply have high similarity to FDA-approved drugs.\nP5, line 112: \"i) the 112 BioLiP2(22) database, which provides experimentally determined binding structures of natural\nligands and their associated pockets.\"\nThis sentence is confusing, as BioLip2 is not limited to natural ligands. If authors used a subset of BioLip2, it should be\nclearly stated here. Method section, line 438 mentions the filter for “regular ligands”, but this is not clear in the previous\nsections.\nP5, line 118: \"They include 326,416, 330,434, 128, and 630 pockets, respectively\"\nThe study's comparative analysis is challenged by the data imbalance, with the BioLip2 and AlphaFill datasets being orders\nof magnitude larger than the primary DEL dataset (n=128). Could the authors comment on this and whether it may effect the\nstatistical power of the conclusion?\nP5, 118-123: For the BioLiP2 dataset, pockets are defined functionally based on \"experimentally annotated binding\nresidues,\" which typically includes a small set of specific interacting residues. For all other datasets, pockets are defined\ngeometrically using a universal 5A˚ distance cutoff from the ligand which creates a more general, inclusive cavity. This\ninconsistency introduces a systematic bias in the average number of residues observed in Figure 1A.\nP7, line 164: “​​We further analysed the interactions between pocket residues and ligands using the Arpeggio method(25)\n(Figure 2 a-c). Hydrophobic interactions accounted for 50.7% in DEL, 49.5% in FDA-AD, and 30.1% in BioLiP2. Hydrogen\nbond interactions were 3.8% for DEL, 4.8% for FDA-AD, and 9.9% for BioLiP2, while polar interactions constituted 6.0%,\n7.8%, and 14.8%, respectively. Ionic interactions can be found in the DEL dataset (1.50%) but were almost absent in FDA-\nAD (0.15%).”\nThe authors report \"Hydrogen bond interactions\" (e.g., 9.9% in BioLiP2) and \"polar interactions\" (14.8% in BioLiP2) as two\ndistinct categories. This is confusing because a hydrogen bond is a specific and strong type of polar interaction. What does\nthe \"polar interactions\" category actually contain if it excludes hydrogen bonds?\nMoreover, the authors state that ionic interactions are more common in the DEL set (1.50%) than the FDA-AD set (0.15%).\nGiven the tiny size of the DEL dataset (n=128), could this 1.5% be an artifact arising from just a few charged ligands in their\nsmall sample? Also, the corresponding value for the massive BioLiP2 dataset should be reported?\nP16, line 446: The low-quality dataset from AlphaFill is a critical issue that is not adequately addressed. The authors' only\nacknowledgement of this problem is a single sentence stating that the \"quality and resolution of the predicted complexes\nwere relatively low\" due to \"potential clashes.\" It appears that the authors used all 330,434 structures without any quality-\nbased filtering. Surely this brings into question the results from the AlphaFill analysis as the defined pockets may be artifacts\nor the wrong size.\nP16, line 454-457: “Ligands in these structures were marked using the 'HET' keyword, and Tanimoto similarity based on\nmolecular fingerprints was calculated 455 between these ligands and the FDA drug list. A ligand was considered an FDA-\napproved drug if its 456 Tanimoto similarity exceeded 0.7.”\nWhat fingerprint (FP) was used when doing the Tanimoto calculation? Without knowing this, the score thresholds are\nmeaningless as interpretation varies from one FP to another. In any case, 0.7 seems very low, and it may find similar\ncompounds but not the exact molecule.\nP17, line 458: “...resulting in a final dataset of 630 unique crystal structures of drug-pocket complexes.” Although authors\nmention unique crystal structures, they fail to address the massive overrepresentation of certain drug-target families in the\nProtein Data Bank (PDB) (e.g., kinases, HIV protease, and heat shock proteins). Dozens or even hundreds of these\nPDBs could contain the exact same drug binding to the exact same protein pocket. The results will reflect the specific properties of\na few over-studied protein families rather than the general characteristics of FDA-approved drug pockets.\nI note that the code is on GitHub and includes instructions on setting it up with Conda. Unfortunately, my institution does not\npermit access to Conda due to its new license and so I have not proceeded further.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "We would like to thank all reviewers for their careful readings and comments. We are very glad\nto work with them to improve the manuscript.\nResponse to Reviewer #1\n1. The Reviewer commented:\nNoteworthy results:\nThe authors have developed a method, ErePOC, for creating ligand binding pocket\nrepresentations and assess their functional similarity. They incorporated their embeddings\ninto the ESM-2 contrastive learning method. They applied their representation method to a\nset of experimentally determined protein-ligand structures in the BioLiP2 database. The\nBioLiP2 database is a semi-curated set of proteins bound to native ligands. They\ndemonstrated the ability of their method to accurately group pockets that bound the same\nligand. They compared this method to performance of ESM-2 embeddings which were not\nable to distinguish between binding pocket classes. They applied their method to a set of\nexperimentally derived protein-ligand structures from the OpenDEL database. The\nOpenDEL database contains experimental protein structures complexed to DEL hits. The\nresulting DEL pocket representations were applied to a filtered set of structures from the\nAlphaFold2 database of predicted protein structures that contained binding pockets\nidentified using F-pocket. They identified proteins from the AlphaFold database that have\nbinding pockets similar to those known to bind DEL molecules. They propose that this\nmethod can be used to identify targets suitable for DEL screening, virtual screening, used to\nidentify binding pockets for a particular ligand type, and optimize binding pockets for a\nspecific ligand.\nSignificance:\nThe method they have developed for binding pocket representations outperforms ESM-2 in\nits ability to group similar binding pockets. Generalizability based on test set performance\nwas equally good for ErePOC-NN, ErePOC-SVM and ESM-2-NN models. While the\nanalyses comparing the properties of pockets calculated using F-pocket are interesting, they\ndo not represent a significant advancement to the field.\nAuthor reply:\nWe thank the reviewer for her or his constructive assessment and for recognizing the importance\nof pocket representation. The central contribution of this work is the development of ErePOC,\na contrastive learning framework that generates ligand-aware, functional embeddings of protein\npockets. Inspired by the specific challenge of assessing target suitability for DEL screening,\nErePOC enables scalable comparison of pocket similarity across large structural datasets —\nand serves more broadly as a general tool for functionally informed pocket analysis.\nWhile DELs have revolutionized early-phase drug discovery by enabling the screening of\nbillions of compounds, the lack of systematic, structure-based methods for evaluating DEL-\ntarget compatibility remains a major bottleneck. Our study bridges this gap by combining\nprotein language models with ligand similarity–driven training, resulting in an approach that\noutperforms raw ESM-2 features and structure-based alignments in grouping functionally\nrelated pockets. The ErePOC framework allows us to expand the landscape of predicted DEL-\nsuitable targets across the human proteome and uncover physicochemical and functional\npatterns that are not accessible by traditional descriptors alone.\nThe Fpocket-based analyses are not presented as a standalone contribution but to provide\nphysicochemical context for describing pocket properties. These analyses serve both to\nmotivate the development of a latent-space representation and to offer interpretability and\ncross-referencing for the results learned by ErePOC. To avoid overemphasizing these\ndescriptive analyses, we have added the following sentence in the first paragraph of\n“Conclusion and Discussion” section to clarify this point.\n“These insights motivate key innovation of this study: developing ErePOC to learn high-\ndimensional pocket representations beyond descriptive features, and applying it to\ncomputationally expand the landscape of DEL-suitable targets across the human proteome.”\nWe have also added the following sentences at the end of Section 2.2 “Analysis of pocket\nphysicochemical properties and their interactions with ligands”:\n“We note that no single feature or simple combination could distinguish DEL from FDA-AD or\ngeneral protein pockets, possibly due to the broad variability of pocket structures. This\nobservation highlights the need for developing a more informative representation of pockets.”\n2. The Reviewer commented:\nAdditional experiments:\nThey identify pockets that they expect to bind DEL molecules but do not provide any\nexperimental validation that their predictions are accurate. I suggest that the authors choose\nrepresentative proteins from each of the 7 highly enriched DEL protein classes (Fig. 5) and\nperform a DEL screen. As a control pick representatives from 7 protein classes that were not\nhighly enriched. If their predictions are correct, they should observe a significantly higher\nnumber of enriched DEL molecules in the positive set relative to the negative set.\nAuthor reply:\nWe greatly appreciate the reviewer’s insightful suggestion. We fully agree that performing DEL\nscreening on representative targets would provide valuable validation and further insights into\nthe DEL compatibility of different protein classes. However, conducting such large-scale DEL\nexperiments for 14 targets from a variety of protein classes would require substantial resources,\nincluding protein expression and purification, DEL screening, sequencing, and downstream\nanalysis, which lie well beyond the capabilities of most, if not all, academic laboratories, and\nthe scope of this work. The primary goal of our work is to develop a computational framework\nthat can systematically identify protein targets potentially more suitable for DEL screening,\nhelping to prioritize targets and reduce experimental burden in early-stage drug discovery.\nAs a computational substitute, we designed a virtual DEL screening workflow for 14\nrepresentative protein targets to emulate experimental testing. Specifically, we leveraged a DEL\ncompound library containing approximately 2.8 million DEL compounds and performed\nstructure-based docking calculations against predicted DEL-suitable targets and matched\ncontrol targets from non-enriched protein classes.\nAlthough docking cannot fully recapitulate experimental outcomes, previous studies have\nshown that large-scale docking trends often correlate with hit discovery rates. Consistent with\nthis, we observed that DEL-enriched targets yielded significantly more favorable docking\nscores, higher enrichment of top-ranking compounds, and more stable Monte Carlo enrichment\nprofiles compared to DEL-neutral controls. Inspired by the suggestion, we also discussed a few\nparticular cases, such as MAT2A and MAT2B. These results, now included in the revised\nmanuscript (Section 2.6, Figures 7 and S16-S17), provide computational evidence that supports\nthe predictive utility of ErePOC and align with the validation envisioned in the reviewer’s\nsuggestion.\n“As a further validation, we designed a large-scale in silico DEL screening experiment against 14\nselected human protein targets to compare their capability to bind DEL-like compounds (Figure S16).\nSix of these targets, each drawn from a different DEL-enriched functional family identified in our earlier\nanalysis, contained a pocket exhibiting high ErePOC cosine similarity (> 0.8) to known DEL pockets.\nThese include a chromatin regulator (O15294), a hydrolase (P03951), an isomerase (P26885), a lyase\n(P43166), a multifunctional enzyme (P14060), and an RNA-binding protein (Q8TAS1). As a comparison\ngroup, six additional targets were selected from DEL-neutral protein families, which did not show\nenrichment but nevertheless contained pockets with high ErePOC similarity (> 0.8). These include a\nsignal transduction inhibitor (O14508), a mitogen (Q9H706), an elongation factor (P43897), an actin\ncapping protein (P47756), a hypotensive agent (P68871), and a cyclin (Q5T5M9). In addition, two\nclosely related proteins, MAT2A (P31153) and MAT2B (Q9NZL9), were included as a family-level case\nstudy. The catalytic MAT2A contains a small, polar pocket with a maximum ErePOC similarity of only\n0.66 to known DEL pockets. In contrast, the regulatory MAT2B, though non-catalytic, harbors a larger\nand more hydrophobic pocket with a much higher similarity (0.93), providing an illustrative example of\ndifferential DEL compatibility within a single protein family.\nDocking calculations were performed using a published virtual DEL compound library (approximately\n2.8 million molecules) sampled from 15 sublibraries of the three-cycle HitGen OpenDEL library36. No\nDNA tags were added so they represent off-bead synthesized molecules. Motivated by previous studies\nshowing that more favorable (i.e., more negative) docking scores correlate with higher experimental hit\nrates, and that larger libraries improve discovery efficiency37, 38, we evaluated whether ErePOC-\nidentified pockets exhibit similarly favorable trends for DEL compounds. Standardized docking Z-score\ndistributions, calculated using the global mean and standard deviation of docking scores across all\ncompounds, are shown in Figure 7A. Targets from DEL-enriched families exhibited stronger predicted\nbinding, with more negative mean Z‑scores (–2.18 vs –1.07) and docking scores (–7.45 vs –\n6.15 kcal mol⁻¹) compared to those from DEL‑neutral families. The difference was also statistically\nsignificant when examining the top 1% scoring compounds per target: DEL-enriched targets yielded\nmean top 1% docking scores ranging from –8.93 to –11.96 kcal mol⁻¹ (Z = –1.54 to –3.73), whereas DEL-\nneutral targets ranged from –5.49 to –9.73 kcal mol⁻¹ (Z = +0.95 to –2.12) (Figure 7B). Monte Carlo\nresampling over 1,000 iterations (Figure 7C) confirmed the robustness of these trends across relevant\ncutoffs (–10 to –8 kcal mol⁻¹), supporting a significant difference in the predicted ability to bind DEL\nmolecules as measure by docking scores. These differences were also evident in the distribution of\ndocking scores (Figure 7D and Supplementary Figure S17).” (Results in Section 2.6)\nFigure 7. Docking score comparison and enrichment analysis of DEL-enriched and DEL-\nneutral targets. A. Smoothed cumulative distribution functions (CDFs) of standardized docking\nZ-scores, illustrating a shift toward more favorable binding scores for DEL-enriched targets. B.\nMann–Whitney U test comparing top 1% docking Z-scores between DEL-enriched and DEL-\nneutral groups. Stars represent the mean Z-score of the top 1% compounds for each target. C.\nMonte Carlo–based estimation of enrichment factor (EF) comparing DEL-enriched and DEL-\nneutral targets through 1000 iterations of random resampling from the top 1% compounds,\ndemonstrating the statistical robustness of enrichment trends. D. Kernel density estimation\n(KDE) plots showing the distribution of docking scores for DEL-enriched (blue) and DEL-neutral\n(red) targets.\nFigure S16. Selected human proteins and their pockets for in silico DEL screening. This\nfigure shows the 14 human protein targets selected for large-scale virtual screening on a DEL\ncompound library, grouped into DEL-enriched (left panels) and DEL-neutral (right panels)\ncategories based on computational predictions. The MAT2B and MAT2A (bottom panels) are\nincluded as an example for different predicted DEL compatibility within a single protein family.\nThese classifications support the experimental design used to evaluate ErePOC-predicted DEL\nsuitability.\nFigure S17. Docking score distributions for the 14 human protein targets.\n3. The Reviewer commented:\nFlaw in data analysis and interpretation:\nOn page 5, line 118, they describe how they defined binding pockets from the different\ndatasets. For the BioLiP2 dataset, they used functional annotations provided by the database\ncuration. For the FDA, DEL and AlphaFill datasets, they define the binding pocket by\ndistance from the bound ligand. They don’t explain why they used different criteria for\ndefining the binding pockets.\nAuthor reply:\nWe sincerely thank the reviewer for raising this important concern. In our study, binding pockets\nin the FDA-AD, DEL, and AlphaFill datasets were consistently defined using a 5 Å distance\ncutoff from ligand atoms. The BioLiP2 dataset is annotated with binding pockets, so we\noriginally used the curated functional annotations provided by the database. To ensure\nmethodological consistency and assess robustness, we additionally redefined BioLiP2 pockets\nusing the same 5 Å geometric criterion applied to the other datasets. This unified definition was\nused to re-analyze all datasets and consistent results were obtained between these two BioLiP2\npocket definition.\nIn the revised manuscript (Results, Section 2.1), we now clarify both definition:\n“For BioLiP2, pockets were defined using experimentally annotated binding residues provided\nby the webserver. Meanwhile, pockets for the AlphaFill, DEL, and FDA-AD datasets were\ngenerated by including all amino acid residues within 5 Å of the bound ligand. To assess the\nimpact of this inconsistency, we additionally redefined BioLiP2 pockets using the same\ndistance-based criterion and repeated all analyses under this unified definition. The key\nfindings remained consistent across definitions, suggesting that our conclusions are reasonably\nrobust to differences in pocket definition.”\nWe have also added the following details in the Methods section (4.1):\n“In BioLiP2, binding sites are originally defined based on functional residue annotations.\nSpecifically, a protein residue is labeled as a binding residue if at least one of its atoms is within\na distance equal to the sum of the van der Waals radii plus 0.5 Å of any atom of the bound\nligand, and if there are at least two such inter-atomic contacts, the residue is considered part\nof the binding site. To ensure methodological consistency across datasets, we additionally\ndefined binding pockets using a simple geometric criterion, including all protein atoms within\n5 Å of any non-hydrogen atom of the bound ligand. All analyses were repeated using this\ndefinition, and the results remained consistent across both definitions (Figs. S8–S10 and S14-\nS15).”\nWe re-ran all analyses using the redefined pockets. Under this unified definition, the average\npocket sizes are: BioLiP2: 12.5 residues, AlphaFill: 12.5, DEL: 28.1, and FDA-AD: 16.1. As\nexpected, DEL and FDA-AD pockets remain larger than BioLiP2 and AlphaFill, reflecting the\ngenerally larger and more chemically complex nature of synthetic drug molecules and DEL hits\ncompared to natural ligands. While minor quantitative changes were observed (e.g., slightly\nlower enrichment of charged residues and slightly higher hydrophobic residue proportions in\nBioLiP2), the overall trends and conclusions remain unchanged: DEL pockets are larger and\nmore hydrophobic, exhibiting weaker polar and hydrogen-bond interactions than BioLiP2\npockets. We have also tested the robustness of ErePOC against these two slightly different\npocket definitions, and found very consistent results. Accordingly, we have added the following\nsentences to the Section 2.3:\n“We note that for ErePOC model training and downstream analyses, the original BioLiP2\nbinding residue annotations were used to preserve experimentally validated contacts. Training\nand analyses using the alternative 5 Å pocket definition yielded highly similar results, as\ndemonstrated in Figure S8-S10.”\nFigure S8. Cosine similarity comparison across different binding pockets under two\nBioLiP2 pocket definitions. A. Cosine similarity results based on the original BioLiP2\nannotation–defined pockets, including ESM-2 vectors, t-SNE 2D representations after ErePOC\ntransformation, and ErePOC embeddings; B. Corresponding results obtained using the unified\n5 Å pocket definition. The overall similarity distributions remain consistent across the two\ndefinitions, confirming the robustness of ErePOC embeddings with respect to pocket boundary\nchoice.\nFigure S9. Comparative performance of four models under two BioLiP2 pocket\ndefinitions. A. Predictive performance of ErePOC-NN, ErePOC-SVM, ESM2-NN, and ESM2-\nSVM using BioLiP2 annotation–defined pockets; B. Corresponding results using the unified 5\nÅ pocket definition.\nFigure S10. t-SNE visualization of ErePOC and ESM-2 representations for BioLiP2\ndataset using the 5 Å pocket definition. A–B. Visualization of the seven types of ligand-\nbinding pocket landscapes using ESM-2 (A) and ErePOC (B) representations, respectively. C.\nPocket landscape using ErePOC representations. D. Comparison of pocket landscapes for the\nFDA-AD and DEL datasets against BioLiP2 using ErePOC representations. Results are\nconsistent with those obtained using curated BioLiP2 binding annotations (Figure 4), confirming\nthe robustness of the ErePOC embedding with respect to pocket definition.\n4. The Reviewer commented:\nPage 6, line 124 states “Drug candidates, being larger and chemically more complex than\nnatural ligands, are specifically designed to exhibit stronger affinity.” The authors do not\ncite a reference or provide an analysis to demonstrate that the statement is true. They should\nremove that statement or provide evidence.\nAuthor reply:\nWe thank the reviewer for this helpful comment. We have cited the following two references to\nsupport the statement and revised the sentence for improved clarity:\nLeeson PD, Springthorpe B. The influence of drug-like concepts on decision-making in\nmedicinal chemistry. Nat Rev Drug Discov 6, 881-890 (2007).\nBickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL. Quantifying the chemical\nbeauty of drugs. Nat Chem 4, 90-98 (2012).\nThe revised sentence now reads:\n“Synthetic drug molecules are often optimized for potency and selectivity through medicinal\nchemistry efforts, which typically results in larger and more chemically complex scaffolds\ncompared to regular ligands25, 26. They often target protein pockets that are more spacious,\nflexible, and dynamic, accommodating a broader range of interactions.” (Section 2.1)\n5. The Reviewer commented:\nPage 6, line 131 states that “DEL pockets were larger than FDA-AD ones, which aligns with\nthe general understanding that DEL molecules are larger, with average molecular weights\nof 1298.5 Da and 218.3 Da in our datasets, respectively.” When evaluating the graphs in\nFigure S2, the molecular weights of the DEL molecules are higher than those of the FDA-\nAD molecules but the average MW of the DEL molecules appears to be much lower based\non the distribution shown. The majority of the DEL molecules are in the 300-700 Da MW\nrange. This is also inconsistent with the data analysis shown in Fig. 2A & B indicating that\nthe volume of binding pockets is similar between the FDA-AD and DEL datasets.\nAuthor reply：\nWe thank the reviewer for carefully identifying this mistake. The originally reported molecular\nweights were incorrect due to a data processing error. We have now corrected the average\nmolecular weights to 560.5 Da for DEL molecules and 310.9 Da for FDA-AD molecules in the\nrevised manuscript. The text on page 6, line 131 has been updated as follows.\n“The average molecular weights of DEL and FDA-AD ligands in this study are 560.5 and 310.9\nDa, respectively (Figure S2).” (Section 2.1)\n6. The Reviewer commented:\nPage 7, line 170 states “Overall, these results indicate that the binding of DEL hit molecules\nrelies more on pocket shape complementarity and can be further optimized towards drug\nmolecules.” I don’t follow how they made that conclusion based on the analysis and results\ndescribed in section 2.2 and shown in Fig. 2.\nAuthor reply:\nWe thank the reviewer for this helpful comment. In the revised manuscript, we have clarified\nthe reasoning and reworded the statement to avoid misinterpretation.\nOur conclusion is based on two main observations:\n DEL pockets tend to be larger than BioLiP2 pockets and modestly larger than FDA-\nAD pockets based on pocket volume and alpha sphere analyses (Figure S3A, Cliff’s δ),\nsuggesting a more expansive surface area for ligand–protein interactions, which is a\ngeometric prerequisite for shape complementarity–driven binding.\n Interaction analysis (Figure S3B) shows that hydrophobic contacts dominate in DEL\npockets, while hydrogen bonds and polar interactions are less frequent. This profile is\nconsistent with a binding mode that relies more on nonspecific shape and hydrophobic\ncomplementarity rather than defined polar interactions.\nTo ensure the interpretation remains appropriately cautious and logically supported, we have\nrevised the text in the manuscript as follows:\n“Overall, these results indicate that DEL hits preferentially bind to larger, more hydrophobic\npockets. The expansive contact area in these pockets enhances binding through shape\ncomplementarity, thereby favoring hydrophobic interactions. These characteristics suggest\npotential avenues for optimization toward drug-like molecules, for example by balancing polar\ninteractions to improve binding specificity.” (Section 2.2)\n7. The Reviewer commented:\nPage 11, line 275 “The ErePOC representation partitions the BioLiP2 pocket space into\ndistinct patterns, offering critical insights into the global pocket landscape. For example,\npockets bound to native ligands such as SAM or HEM appear less suitable as drug targets -\nparticularly in the context of DEL screening. In contrast, pockets bound to ADP seem more\nflexible to accommodate designed molecule.” These interpretations appear to be based on\nthe analyses shown in Fig. C and D. While it is true that the chemical space corresponding\nto SAM or HEM bound structures lacks representatives from the DEL and FDA datasets, the\nchemical space corresponding to ADP bound structures has very few representatives from\nthe DEL and FDA datasets.\nAuthor reply:\nWe thank the reviewer for his or her careful reading and thoughtful reasoning. We agree that\nthe interpretation regarding ADP-bound pockets may have been overstated, given the limited\nnumber of DEL and FDA-AD representatives in that region of the pocket space. We have thus\nremoved this specific interpretation. We have retained the discussion of SAM/HEM-bound\npockets, emphasizing that their absence from DEL and FDA-AD chemical spaces suggests that\nthese tightly bound, cofactor-associated pockets may be less suitable for conventional DEL\nscreening. The revised text now reads as follows:\n“The ErePOC representation partitions the BioLiP2 pocket space into distinct patterns,\noffering critical insights into the global pocket landscape. For example, pockets bound to native\nligands such as SAM or HEM are notably absent from the DEL and FDA-AD chemical spaces,\nsuggesting that these tightly bound, cofactor-associated pockets may be less suitable for\nconventional DEL screening. To further explore the “DEL-able” regions of the pocket\nlandscape, we identified the five nearest neighbors for each DEL target in the BioLiP2 dataset\nbased on cosine similarity (Figure S12).” (Section 2.4)\n8. The Reviewer commented:\nBased on the description of database curation described in section 4.1, it isn’t clear to me if\ndatasets were consistently constructed. The BioLiP2 dataset has multiple structures that are\nbound to the same ligand. The description of the process used for the AlphaFill structures\ngiven on page 16 lines 444-450 doesn’t indicate if the 330,434 are all unique or have\nredundant/similar ligands. The set of FDA molecules is described on page 17, line 458 as\nbeing 630 unique crystal structures. Does that mean they are 630 unique ligand-protein\ncombinations? Page 17 line 462 “For each target protein, only the DEL hit with the best\nbinding affinity was retained.” resulting in 128 unique structures.\nAuthor reply:\nWe thank the reviewer for this helpful comment regarding dataset consistency and construction.\nWe have revised Section 4.1 to clarify these issues. Specifically:\n BioLiP2 dataset: Multiple structures bound to the same ligand are retained intentionally,\nallowing ErePOC to learn from diverse conformational states of the same ligand across\ndifferent proteins and crystallization conditions. This enhances functional relevance and\nimproves generalizability.\n AlphaFill dataset: Similarly, AlphaFill contains redundant ligand–protein complexes.\nRedundancy was maintained to expose the model to a broad spectrum of protein contexts\nand ligand environments. After filtering for steric clashes, 293,019 high-confidence\ncomplexes were retained.\n FDA-AD dataset: Unique crystal structures were defined as unique FDA drug–protein\npairs. Redundant structures for the same drug–target pair were removed, but multiple\ntargets of the same drug are retained.\n DEL dataset: A one-to-one mapping was applied by retaining only the highest-affinity\nDEL hit per target, resulting in 128 unique DEL–target structures.\nThese clarifications have been added to Table S1 and incorporated into Section 4.1 of the\nrevised manuscript, which now reads as follows:\n“To ensure methodological consistency, we note that redundancy was handled differently across\ndatasets depending on their intended use. Specifically, the BioLiP2 and AlphaFill datasets\nintentionally retain multiple structures bound to the same ligand, thereby exposing the model\nto diverse conformational states and binding environments. In contrast, for the FDA-AD and\nDEL datasets we applied stricter curation: the FDA-AD set was reduced to unique FDA drug–\nprotein pairs (removing redundant crystal structures for the same drug–target), while the DEL\nset was limited to one highest-affinity hit per target protein.”\n9. The Reviewer commented:\nComments:\nPage 2, line 15 states that “few DEL-derived compounds have advanced to clinical trials or\nreached the market.” I disagree with that assertion. I would refer the authors to a nice review\nwritten by David Liu in which he performed a comprehensive analysis of DEL derived\nmolecules that have entered the clinic. Nat Rev Drug Discov. 2023 Jun 16;22(9):699–722.\ndoi:10.1038/s41573-023-00713-6. While the total number of DEL molecules that have\nreached the clinic is comparatively low relative to molecules identified by high-throughput\nscreening, DEL has only recently been adopted as a standard approach for hit identification.\nAuthor reply:\nWe thank the reviewer for this helpful comment and for referring us to the review by David Liu\net al. (Nat Rev Drug Discov. 2023, cited as Ref. 7). We agree that our original phrasing did not\nfully capture the clinical progress achieved with DEL-derived compounds. To address this, we\nhave revised the abstract and introduction to more accurately reflect the current status while\nnoting that DEL is a relatively recent approach for hit identification:\n“DNA-encoded libraries (DELs) facilitate high-throughput screening of trillions of molecules\nagainst protein targets through split-pool synthesis and DNA tagging. Despite their potential,\nonly a few DEL-derived compounds have advanced to clinical trials or reached the market. A\nbetter understanding of the defining characteristics of target proteins, particularly those with\nbinding pockets suitable for DEL screening, is critical to improving success rates.” (Abstract)\n“Despite the high-throughput capabilities and economic advantages of the DEL technique, the\nnumber of DEL-derived molecules advancing to clinical trials or reaching the market remains\nrelatively low, partly due to the relatively recent adoption of DEL as a mainstream approach\nfor hit identification, as well as persistent challenges such as uncertainties in processing DEL\ndata, difficulties in optimizing DEL hits, and a general lack of understanding on target\ndruggability with DEL molecules7.” (Introduction)\n10. The Reviewer commented:\nThe authors included structures from three databases of experimentally derived structures,\nBioLiP2, FDA and OpenDEL, and two databases of predicted structures, AlphaFill and\nAlphaFold. It would be interesting to know why they didn’t include in their comparisons the\nhuman protein structures from the Protein Data Bank with small molecule ligands not\nincluded in the BioLiP2, FDA and OpenDEL databases.\nAuthor reply：\nWe thank the reviewer for this comment. While the PDB contains many protein–ligand\ncomplexes, a substantial fraction of ligands are either crystallization artifacts or lack biological\nrelevance. BioLiP2 is a curated subset of the PDB that retains only biologically meaningful\ninteractions with functional annotation. To ensure reliability and avoid noise, we therefore\nbased our analyses on BioLiP2, along with the FDA-AD and the DEL datasets we prepared.\nWe have clarified this point in Methods Section 4.1 as follows:\n“We specifically chose BioLiP2 rather than using all PDB entries with ligands because BioLiP2\nsystematically filters out crystallization artifacts and non-functional ligands, thereby ensuring\ngreater biological relevance and reliability.”\n11. The Reviewer commented:\nSection 2.5 described the identification of pockets from the human proteome that are suitable\nfor DEL screening. The set of AlphaFold2 predicted human protein structures was filtered\nprior to analysis with ErePOC2. One of the filters applied was to remove proteins that had\npocket volumes less than 800 A3. On page 7, line 151 they indicate that the average volume\nof a DEL molecule binding pocket is 2723.3 A3. They should explain why they decided to use\nAuthor reply：\nWe thank the reviewer for this helpful comment. Previous studies have suggested that a\nminimum pocket volume of ~500 Å³ is required to accommodate drug-like small molecules\n(e.g., J. Phys. Chem. B 2022, 126, 6853–6867). We adopted a more stringent threshold of\n800 Å³ to exclude very small, shallow, or solvent-exposed cavities, thereby retaining pockets\nmost likely to be DEL-compatible. This is motivated by the observation that DEL-binding\npockets are, on average, about 1.3 times larger than FDA-AD pockets.\nAmong 862,347 pockets initially detected in AlphaFold2-predicted human proteins, this filter\nremoved ~37% of the smallest cavities. We further filtered pockets with average pLDDT < 0.7,\nresulting in 182,424 high-confidence pockets for ErePOC analysis. We have added one\nadditional sentence in the main text (Section 2.5) accordingly to reflect this rationale.\n“The 800 Å³ threshold was selected based on prior studies suggesting 500 Å³ as a minimal\ndruggable pocket volume32, together with our observation that DEL‑binding pockets are\nsubstantially larger.”\n12. The Reviewer commented:\nFigs. 2A and S10A are lacking units for the X-axis.\nFigs. 2C and S10C the legend below the graph has a typo: “polor” should be “polar”\nFig. S3 should include a legend indicating the corresponding binding sites and colors as was\ndone in Fig. 4.\nAuthor reply：\nWe thank the reviewer for her or his careful reading and for identifying these omissions and\nerrors. We have now updated the figures as follows: units have been added to the X-axes in\nFigs. 2A and S13A (the original S10A); the typo in Figs. 2C and S13C (the original S10C) has\nbeen corrected to \"polar\"; and a detailed legend clarifying the binding sites and color scheme\nhas been added to Fig. S5 (the original S3), consistent with the style of Fig. 4. These revisions\nhave been incorporated into the revised manuscript.\nResponse to Reviewer #2\n1. The Reviewer commented:\nThe authors describe a method to represent the properties of protein pockets that captures\nboth structural and functional features, and use this method to investigate DEL target space.\nThe paper is well written, but we have a number of comments around the methodology.\nAuthor reply:\nWe sincerely thank the reviewer for carefully reading our manuscript and for providing\nthoughtful comments. We appreciate the constructive feedback, which has helped us improve\nthe clarity and rigor of our work.\n2. The Reviewer commented:\nComments:\nThe DEL dataset contains 128 entries. It would be useful if the authors included the\nassociated SMILES strings for the ligands and a depiction of their structures (as\nsupplementary information).\nAuthor reply:\nWe thank the reviewer for this helpful suggestion. The SMILES strings of the 128 DEL ligands\nhave been added as Supplementary Table S2, and the Methods section (Section 4.1) has been\nupdated accordingly:\n“This dataset ultimately consisted of 128 unique DEL hit–target protein structures. The\ncorresponding SMILES strings of these ligands are provided in Supplementary Table S2.”\n3. The Reviewer commented:\n“We hypothesize that DEL targets exhibit unique pocket characteristics that can be utilized\nin drug design decision-making, such as target prioritization and hit identification.” Does\nthis study shed any light on these unique pocket characteristics? Or indeed is the conclusion\nthat the pocket characteristics are similar to general druggable pocket?\nAuthor Reply:\nWe thank the reviewer for this insightful question. Our results reveal that DEL pockets indeed\ndiffer statistically from general druggable pockets, although these differences are subtle and\npartially overlapping rather than categorically distinct. This observation highlights the need for\ndeveloping a continuous representation such as ErePOC, which provides a more informative\ncharacterization of pockets than conventional geometric or physicochemical feature descriptors.\nSpecifically, we conducted additional quantitative analyses of pocket features (volume, number\nof alpha spheres, alpha sphere density, apolar alpha sphere proportion, mean local hydrophobic\ndensity, proportion of polar atoms, and druggability) and pocket–ligand interaction features\n(hydrophobic, hydrogen-bond, and polar contacts) across DEL, FDA-approved, and BioLiP2\npockets. Statistical analyses using Cliff’s δ effect sizes and p-values, followed by principal\ncomponent analysis (PCA), were performed to quantify and visualize their differences.\nOur results show that DEL pockets are generally larger and structurally more complex, with a\nhydrophobic-dominated binding environment complemented by selective polar contacts.\nStatistical comparisons (Figure S3A) confirm that DEL pockets are significantly larger than\nFDA-AD targets (volume δ = 0.405; number of alpha spheres δ = 0.409; alpha sphere density\nδ = 0.395; p < 3.6e-11) and BioLiP2 pockets (volume δ = 0.302; number of alpha spheres δ =\n0.321; alpha sphere density δ = 0.201; p < 3.4e-09), indicating that DEL screening identifies\nmolecules interacting with larger and more complex binding interfaces. DEL pockets also\nexhibit a balanced polar–apolar composition: the proportion of apolar alpha spheres is slightly\nlower than in FDA-AD targets (δ = -0.155) but higher than in BioLiP2 (δ = 0.193). Mean local\nhydrophobic density (δ = 0.136 vs FDA-AD, δ = 0.342 vs BioLiP2), proportion of polar atoms\n(δ = 0.177 vs FDA-AD, δ = -0.137 vs BioLiP2), and druggability score (δ = -0.109 vs FDA-\nAD, δ = 0.045 vs BioLiP2) further highlight their intermediate physicochemical profile.\nAnalysis of pocket–ligand interactions (Figure S3B) demonstrates that DEL pockets are\ndominated by hydrophobic contacts (δ = 0.122 vs FDA-AD, δ = 0.378 vs BioLiP2), whereas\nhydrogen bonds (δ = -0.150 vs FDA-AD, δ = -0.392 vs BioLiP2) and polar interactions (δ = -\n0.207 vs FDA-AD, δ = -0.459 vs BioLiP2) are reduced. These characteristics indicate that\nhydrophobic effects drive DEL binding, stabilized by a minimal yet functionally critical set of\npolar anchors.\nPCA analysis further shows that DEL pockets cluster in a distinct but partially overlapping\nregion of chemical space. These updated results are summarized in Section 2.2 as follows, with\nsupporting data provided in two new Supplementary Figures (S3 and S4).\n“Statistical analyses were performed by computing Cliff’s δ effect sizes for key pocket and\npocket-ligand interaction features (Figure S3). These analyses confirmed that DEL pockets\nwere significantly larger than FDA-AD targets (volume δ = 0.405; number of alpha spheres δ\n= 0.409; alpha sphere density δ = 0.395; p < 3.6e-11) and BioLiP2 pockets (volume δ = 0.302;\nnumber of alpha spheres δ = 0.321; alpha sphere density δ = 0.201; p < 3.4e-09), and exhibited\na balanced polar–apolar composition, with physicochemical properties intermediate between\nFDA-AD and BioLiP2 (Figure S3A). Analysis of pocket–ligand interactions confirmed that\nDEL pockets were dominated by hydrophobic contacts (δ = 0.122 vs FDA-AD, δ = 0.378 vs\nBioLiP2), whereas hydrogen bonds (δ = -0.150 vs FDA-AD, δ = -0.392 vs BioLiP2) and polar\ninteractions (δ = -0.207 vs FDA-AD, δ = -0.459 vs BioLiP2) were reduced (Figure S3B). These\ncharacteristics indicate that DEL binding is primarily driven by hydrophobic effects, stabilized\nby a minimal yet functionally critical set of polar anchors.\nPrincipal component analysis (PCA) of features further confirmed these patterns, showing that\nDEL pockets occupy a distinct region in PCA space (Figure S4). PC1 primarily reflected\nchemical composition, including apolar/polar atom ratios and interaction types, while PC2\nwas dominated by structural size descriptors, together accounting for approximately 75% of\nthe variance. The pocket analysis was also consistent with molecular property differences\nobtained using MOE29 (Figure S2). DEL molecules exhibited lower water solubility (LogS = -\n6.49) compared to approved drugs (-3.05) and higher hydrophobicity (cLogP = 3.42 vs. 1.44).\nWhile DEL pockets shared the overall druggability characteristics of FDA-AD targets, they\ndisplayed a distinct physicochemical bias, favoring larger, more hydrophobic pockets with a\ndifferent polar–apolar balance. We note that no single feature or simple combination could\ndistinguish DEL from FDA-AD or general protein pockets, possibly due to the broad variability\nof pocket structures. This observation highlights the need for developing a more informative\nrepresentation of pockets.” (Section 2.1)\nFigure S3. Statistical comparison of pocket and interaction features. A. Pocket features\n(volume, alpha spheres, density, polar/apolar composition, hydrophobicity, druggability)\ncompared between DEL, FDA-AD, and BioLiP2 targets using Cliff’s delta; significance: * p\n<0.05, ** p < 0.01, *** p < 0.001; B. Interaction features (hydrophobic, hydrogen-bond, and\npolar contacts) compared across the three datasets using Cliff’s delta with the same\nsignificance notation.\nFigure S4. Principal component analysis (PCA) of pocket and interaction features. DEL\n(blue) and FDA-AD (red) targets were projected onto the PCA space trained using BioLiP2\npockets (gray background). Kernel density contours highlight the distinct yet partially\noverlapping distributions of DEL and FDA-AD targets. PC1 is primarily influenced by chemical\ncomposition, including apolar–polar balance and interaction types (e.g., apolar α-sphere\nproportion 4.1%, proportion of polar atoms 4.0%, hydrophobic 3.8%, hydrogen bonds 3.9%,\npolar interactions 4.0%), whereas PC2 is dominated by structural size descriptors (pocket\nvolume 11.7% and number of alpha spheres 11.2%). Together, these components capture the\nseparation of DEL targets from reference datasets.\n4. The Reviewer commented:\nP6, L123: “DEL and FDA-AD pockets were notably larger than those in BioLiP2 and\nAlphaFill, likely due to differences in ligand characteristics.” I do not believe that thisshows\nthat the pockets are larger, merely that the ligands are larger, and hence the number of\nresidues within 5A. The pocket size is accurately assessed in Section 2.2.\nAuthor reply:\nWe thank the reviewer for pointing this out. We agree that the number of residues primarily reflects\nligand size rather than true pocket volume, which is more accurately assessed in the subsequent\nSection 2.2. Accordingly, we have revised the sentences as follows:\n“As shown in Fig. 1A, the average number of residues in BioLiP2, AlphaFill, DEL, and FDA-\nAD pockets was 12.5, 12.5, 28.1, and 16.1, respectively. The larger number of residues\nsurrounding DEL and FDA-AD ligands likely reflects their greater molecular size and chemical\ncomplexity, often containing halogen atoms and other bulky functional groups that demand\nmore spatially extended binding environments. The average molecular weights of DEL and\nFDA-AD ligands in this study are 560.5 and 310.9 Da, respectively (Figure S2). In contrast,\nregular ligands and their pockets have co-evolved for an optimized fit tuned to biological needs\nrather than maximal binding.”\n5. The Reviewer commented:\nP7: Across the various properties, is the difference between DEL and FDA-AD statistically\nsignificant? Is there any evidence that the targets for which DEL has been run differ from\nthe set of all druggable targets?\nAuthor reply:\nWe thank the reviewer for raising these important questions. As detailed in our response to\nComment #3, we performed statistical analyses comparing DEL and FDA-AD pockets,\nconfirming that DEL pockets are significantly larger and more hydrophobic than FDA-AD\npockets (e.g., Volume δ = 0.405, p < 3.6e-11). PCA further shows that DEL targets occupy a\ndistinct region within the broader druggable pocket space.\nRegarding the second question, there is currently no reliable evidence that targets screened by\nDEL differ systematically from the broader set of druggable proteins, although it is commonly\nbelieved that membrane proteins are less frequently screened with DEL in contrast with\nenzymes. In practice, it is unknown how many targets — and which ones — have already been\nsubjected to DEL screening, as such information is rarely disclosed unless a hit is identified,\nfurther optimized, and ultimately published. Negative results are generally not reported, and\nlarge-scale DEL screening efforts in industry are typically proprietary. As a result, publicly\navailable datasets are inherently biased toward successful cases.\n6. The Reviewer commented:\nThe FDA-AD compounds are the result of a lead optimisation process that would have\neliminated functional groups that are problematic for metabolism. The DEL compounds are\ninvestigational compounds and their structures are expected to be less optimal. This might\nexplain why ionic interactions were found in the DEL dataset. If the authors had included\nthe associated ligand structures for the DEL dataset, this would be easier to assess.\nAuthor reply:\nWe thank the reviewer for this insightful suggestion. We have now included the ligand SMILES\nfor the DEL dataset in the SI (Table S2), which will allow easier assessment of the chemical\nand interaction features. We agree that the higher proportion of ionic interactions observed in\nthe DEL dataset compared to the FDA-AD set likely reflects the fact that DEL hits have not\nundergone further optimization for metabolic stability, whereas FDA-approved drugs have. We\nalso note that the number of ionic interactions in the FDA-AD dataset changed slightly after\napplying a more stringent similarity criterion to define FDA-approved drugs, as suggested by\nthe reviewer. Accordingly, we have revised the sentence in the manuscript to:\n“Ionic interactions were slightly more prevalent in DEL pockets (1.3%) than in FDA-AD\npockets (0.7%), while BioLiP2 pockets exhibited a higher fraction of ionic interactions (3.9%).\nThis pattern may reflect the early, pre-optimization status of DEL compounds compared to\napproved drugs” (Section 2.2)\n7. The Reviewer commented:\nThe phrase “DEL pockets” seems a bit misleading. These are just pockets where DEL ligands\nhave been reported. Presumably, small molecules have also been reported for these pockets,\nor is this not the case?\nAuthor reply:\nWe thank the reviewer for this insightful comment. Yes, as correctly noted, the term “DEL\npockets” refers to pockets where DEL-derived ligands have been reported, not to pockets that\nexclusively bind DEL ligands. In many cases, these pockets also accommodate other small\nmolecules or metabolites such as ATP. We have clarified this in the revised manuscript as\nfollows.\nTo avoid ambiguity, we have revised Section 4.1 of the Methods to clarify this terminology:\n“It should be noted that in this study, “DEL pockets” refer to binding sites identified through\nreported DEL-derived ligands, which may nonetheless also bind other small molecules or\nendogenous metabolites.”\n8. The Reviewer commented:\nP 7, L174: “DEL pockets possess unique features in their physicochemical properties,\ndistinguishing them from general protein pockets.” The results shown seem to indicate that\ntheir properties are similar to those for FDA-AD.\nAuthor reply:\nWe thank the reviewer for this valuable comment. As noted in our earlier responses, DEL and\nFDA-AD pockets do share the common property of druggability, as reflected by their partial\noverlap in PCA space. However, statistical analyses reveal that DEL pockets are significantly\nlarger and exhibit a bias toward hydrophobic interactions, with fewer polar contacts. As this\npart of the manuscript has been revised, the sentence has been replaced by the following text\n(Section 2.2):\n“While DEL pockets shared the overall druggability characteristics of FDA-AD targets, they\ndisplayed a distinct physicochemical bias, favoring larger, more hydrophobic pockets with a\ndifferent polar–apolar balance. We note that no single feature or simple combination could\ndistinguish DEL from FDA-AD or general protein pockets, possibly due to the broad variability\nof pocket structures. This observation highlights the need for developing a more informative\nrepresentation of pockets.”\n9. The Reviewer commented:\nFig 5: If prediction of human protein targets suitable for DEL screening had been done\ninstead with the Drug-AD pockets would there have been any difference in the results? If so,\nthis would be interesting to report. What I am trying to figure out is whether the ‘DEL-able’\npocket space really is different than general druggable pocket space. On P11, it is stated that\n“Together, these results suggest that DEL screening can readily access most of the known\ndruggable target space.” which would indicate that there is little difference.\nAuthor reply:\nWe thank the reviewer for raising this important and thought-provoking comment. We have\nperformed the suggested control analysis by repeating our prediction pipeline using FDA-AD\nreference pockets in place of DEL reference pockets. Consistent with expectations, both FDA-\nAD and DEL-based predictions identified a largely overlapping set of “druggable” human\nprotein pockets. However, important differences emerged, as shown by the enrichment of\nprotein families containing pockets with high similarity (cosine > 0.8) to either FDA-AD or\nDEL reference pockets (Figure S18). While enzyme classes such as transferases, hydrolases,\nand oxidoreductases were enriched in both, we identified divergent preferences in other\nprotein families. In particular, FDA-AD-like pockets clustered in classical targets (receptors,\nion channels, and isomerases), whereas DEL-like pockets were uniquely enriched in families\nsuch as RNA-binding proteins, chromatin regulators, and GTPase activators.\nWe also include an illustrative case comparing MAT2A and MAT2B within the methionine\nadenosyltransferase family. MAT2A—despite being a validated drug target—harbors a small,\npolar pocket with relatively low similarity to DEL reference pockets (maximum cosine\nsimilarity = 0.66). In contrast, MAT2B contains a larger, more hydrophobic pocket with much\nhigher DEL similarity (cosine = 0.93; Figure S19). Following Reviewer 1’s suggestion, we also\nperformed in silico DEL screening by docking a pre-assembled 2.8-million DEL compound\nlibrary against both MAT2A and MAT2B. Consistent with ErePOC predictions, MAT2B\nyielded a substantially more favorable mean docking score (–8.8 vs –5.3 kcal/mol), indicating\na higher likelihood of DEL compatibility.\nIn summary, DEL-compatible pockets largely overlap with the general druggable space but\nexhibit a distinct bias toward larger and more hydrophobic cavities. This complementary\nperspective underscores how DEL can extend beyond traditional drug discovery paradigms. We\nhave added the following revised text to the main manuscript:\nSection 2.4\n“Together, these results suggest that DEL screening can access the majority of known\ndruggable targets, while exhibiting a measurable preference for pockets with features\nconducive to DEL hit generation, such as larger volume and higher hydrophobicity.” (Section\n2.4)\nSection 2.5\n“The FDA-AD dataset, which encompasses 31 protein families, showed a similarly broad\ndistribution, with transferases (20.8%), hydrolases (18.1%), oxidoreductases (14.8%), DNA-\nbinding proteins (7.3%), and receptors (6.9%) as the most frequent classes. … Interestingly,\ntransferases, hydrolases, and oxidoreductases are consistently enriched across all three\ndatasets (DEL, FDA-AD, and predicted DEL-compatible pockets), underscoring their central\nroles as preferred families for DEL screening.” (Section 2.5)\nConclusion and Discussion\n“To clarify how DEL-compatible pockets relate to the traditional druggable space, we repeated\nour ErePOC cosine similarity analysis using FDA-AD reference pockets in place of DEL-\nderived ones. While both sets identified overlapping human pockets, enrichment patterns\nrevealed distinct preferences. Among predicted high-similarity pockets, FDA-AD-like pockets\nwere predominantly enriched in classical target families such as receptors, ion channels, and\nisomerases, whereas DEL-like pockets were uniquely enriched in families including RNA-\nbinding proteins, chromatin regulators, and GTPase activators (Figure S18). Notably,\ntransferases, hydrolases, and oxidoreductases were enriched by both predictions, highlighting\nshared accessible space. The relative scarcity of DEL-compatible receptor and channel targets\nmay reflect the practical challenges of screening membrane proteins in DEL experiments. These\nresults suggest that although DEL screening overlaps with general druggable space, it exhibits\na measurable bias toward target classes with larger, more hydrophobic and chemically\ntractable binding pockets. For example, within the methionine adenosyltransferase family, the\nregulatory MAT2B harbors a larger, more hydrophobic pocket than the catalytic MAT2A\n(Figure S19). Despite MAT2A being a validated drug target39, its pocket showed lower\nsimilarity to DEL reference pockets (cosine = 0.66) compared to MAT2B (cosine = 0.93),\nsuggesting it may be less suitable for DEL screening. In silico DEL screening using docking\nconfirmed that MAT2B exhibited a substantially more favorable mean docking score (–8.8 vs –\n5.3 kcal/mol). Together, these results support the ability of the ErePOC framework to identify\ntargets with better DEL compatibility, enabling more efficient and strategically focused\napplications of DEL in early-stage drug discovery.”\nFigure S18. Comparative analysis of human protein enrichment using pocket similarity\nwith either DEL or FDA-AD pockets using ErePOC representation (cosine similarity > 0.8)\nFigure S19. Comparative Analysis of Pocket Properties between MAT2A and MAT2B. A.\nThe structure and pocket of MAT2A; B. The structure and pocket of MAT2B; C. Distribution of\ndocking scores; D. Distribution of docking Z-scores for MAT2A (blue) and MAT2B (red).\n10. The Reviewer commented:\nP. 13: “These examples highlight how contrastive learning imparts the capacity to detect\nhigher-level functional or physicochemical similarities that are not solely reflected by either\nglobal or local 3D structural alignment.” Are these not false positives, or is there\nexperimental evidence that these bind very similar ligands?\nAuthor reply:\nWe thank the reviewer for this valuable comment. We agree that, without additional\nexperimental validation, some examples shown in Figure 6 could present false positives. These\ncases do not directly demonstrate functional equivalence or ligand binding similarity per se, but\nrather illustrate how embedding-based similarity derived from contrastive learning may capture\nhigher-level functional or physicochemical relationships that are not apparent from global or\nlocal 3D structural alignment alone.\nThis interpretation aligns with prior findings that pockets accommodating the same ligand (e.g.,\nATP) can exhibit substantial geometric variation (Kahraman et al., 2007) and that functional\nconvergence can occur across distinct structural folds (Söding et al., 2008). To reflect this\nnuance and avoid overinterpretation, we have revised the relevant paragraph in the main text\n(Section 2.6) as follows:\n“These cases indicate that contrastive learning may capture potential functional or\nphysicochemical relationships between binding pockets that are not fully explained by global\nprotein fold or local geometric similarity. Comparable observations have been reported in\nearlier studies, where pockets binding the same ligand (e.g., ATP) exhibit considerable\ngeometric diversity20, and functional associations are detectable across distinct structural\nfolds35. Although further experimental evidence will be required to substantiate such\nrelationships in our predictions, these findings suggest that embedding-based similarity could\nprovide information complementary to conventional structure alignment methods and may offer\nnew hypotheses for future exploration.”\n11. The Reviewer commented:\nP17: “A ligand was considered an FDA-approved drug if its Tanimoto similarity exceeded\n0.7.” Unless the similarity is 1.0, the structure is different. It may be a tautomer, in which\ncase you could use the InChI to check for identity. But otherwise, these will not be the same\nmolecule, and I believe that it is incorrect to state that these are FDA-approved drugs. They\nsimply have high similarity to FDA-approved drugs.\nAuthor reply:\nWe thank the reviewer for highlighting this important point. We agree that a Tanimoto similarity\nthreshold < 1.0 could include non-identical molecules, such as tautomers or close analogs. To\navoid this ambiguity, we have revised our workflow to require a Tanimoto similarity of exactly\n1.0, ensuring chemical identity between PDB ligands and FDA-approved drugs. This stricter\ncriterion guarantees that only structurally identical molecules are retained, excluding\ncompounds with even minor differences. As a result, the updated FDA-AD dataset now\ncontains 340 (instead of the original 630) unique crystal structures of drug–pocket complexes,\neach representing a distinct FDA-approved drug–protein pair. All related statistics in the\nmanuscript have been updated accordingly. This revision is reflected in the revised Methods\nsection as follows:\n“Our third dataset focused on FDA-approved drugs, collected from the DrugBank database.\nWe gathered information on 2,863 FDA-approved drugs and used their SMILES representations\nto search the Protein Data Bank (PDB) for corresponding drug–protein complexes. In total,\n5,286 potential drug–protein complex structures were identified. Ligands in these structures\nwere marked using the ‘HET’ keyword, and Tanimoto similarity based on Morgan fingerprints\n(radius = 2) was calculated using RDKit between these ligands and the FDA drug list. A ligand\nwas considered an FDA-approved drug only if its Tanimoto similarity equaled 1.0, ensuring\nexact chemical identity. Pockets were defined as amino acid residues within 5 Å of these ligands,\nresulting in a final dataset of 340 unique crystal structures, each corresponding to a distinct\nFDA-approved (FDA-AD) drug–protein pair. Multiple targets of the same drug were retained,\nbut redundant crystal structures of the same drug–target combination were removed.” (Page\n40, Section 4.1)\n12. The Reviewer commented:\nP5, line 112: \"i) the 112 BioLiP2(22) database, which provides experimentally determined\nbinding structures of natural ligands and their associated pockets.\"\nThis sentence is confusing, as BioLip2 is not limited to natural ligands. If authors used a\nsubset of BioLip2, it should be clearly stated here. Method section, line 438 mentions the\nfilter for “regular ligands”, but this is not clear in the previous sections.\nAuthor Reply:\nWe thank the reviewer for pointing out this ambiguity. Indeed, BioLiP2 includes a broad range\nof ligand types, not just natural ligands. In our study, we specifically restricted the dataset to\n“regular ligands” as defined in the BioLiP2 annotation, excluding crystallization additives,\nmetal ions, and other non-functional or non-biologically relevant molecules. To clarify this, we\nhave revised the text in Section 2.1 to explicitly state that we used the subset of BioLiP2 entries\nwith regular ligands. The revised sentence now reads:\n“(i) the BioLiP223 database, from which we selected entries annotated with regular ligands (i.e.,\nbiologically relevant small molecules) and their associated pockets;”\nWe have also updated the manuscript throughout to replace instances of “natural ligands” with\n“regular ligands” to more accurately reflect the dataset used.\n13. The Reviewer commented:\nP5, line 118: \"They include 326,416, 330,434, 128, and 630 pockets, respectively\"\nThe study's comparative analysis is challenged by the data imbalance, with the BioLip2 and\nAlphaFill datasets being orders of magnitude larger than the primary DEL dataset (n=128).\nCould the authors comment on this and whether it may effect the statistical power of the\nconclusion?\nAuthor reply:\nWe thank the reviewer for raising this important point regarding dataset size imbalance. Indeed,\nthe BioLiP2 and AlphaFill datasets are substantially larger than the DEL dataset (n = 128). To\nensure the robustness of our comparative analyses despite this imbalance, we employed\nstatistical methods that remain valid under unequal sample sizes. Specifically, we used non-\nparametric tests such as the Mann–Whitney U test for comparing medians, and permutation\ntests to assess distributional differences (Figure S3). These approaches rely on the underlying\ndata distributions and are less sensitive to sample size, making them appropriate for our study.\nMoreover, key physicochemical properties—such as pocket volume, hydrophobicity, and\npolarity—showed consistently significant differences (p < 0.05, often p < 0.001) across all\ncomparisons. This indicates that the observed trends are robust and unlikely to be artifacts of\ndata size imbalance. Essentially, while the DEL dataset is relatively small, it is not prohibitively\nso (n=128), and the magnitude and consistency of its distinctive physicochemical features\nenabled statistically significant separation from larger background datasets, supporting the\nvalidity of our conclusions.\n14. The Reviewer commented:\nP5, 118-123: For the BioLiP2 dataset, pockets are defined functionally based on\n\"experimentally annotated binding residues,\" which typically includes a small set of specific\ninteracting residues. For all other datasets, pockets are defined geometrically using a\nuniversal 5A˚ distance cutoff from the ligand which creates a more general, inclusive cavity.\nThis inconsistency introduces a systematic bias in the average number of residues observed\nin Figure 1A.\nAuthor reply:\nWe sincerely thank the reviewer for raising this important concern. In our study, binding pockets\nin the FDA-AD, DEL, and AlphaFill datasets were consistently defined using a 5 Å distance\ncutoff from ligand atoms. The BioLiP2 dataset is annotated with binding pockets, so we\noriginally used the curated functional annotations provided by the database. To ensure\nmethodological consistency and assess robustness, we additionally redefined BioLiP2 pockets\nusing the same 5 Å geometric criterion applied to the other datasets. This unified definition was\nused to re-analyze all datasets and consistent results were obtained between these two BioLiP2\npocket definition.\nIn the revised manuscript (Results, Section 2.1), we now clarify both definition:\n“For BioLiP2, pockets were defined using experimentally annotated binding residues provided\nby the webserver. Meanwhile, pockets for the AlphaFill, DEL, and FDA-AD datasets were\ngenerated by including all amino acid residues within 5 Å of the bound ligand. To assess the\nimpact of this inconsistency, we additionally redefined BioLiP2 pockets using the same\ndistance-based criterion and repeated all analyses under this unified definition. The key\nfindings remained consistent across definitions, suggesting that our conclusions are reasonably\nrobust to differences in pocket definition.”\nWe have also added the following details in the Methods section (4.1):\n“In BioLiP2, binding sites are originally defined based on functional residue annotations.\nSpecifically, a protein residue is labeled as a binding residue if at least one of its atoms is within\na distance equal to the sum of the van der Waals radii plus 0.5 Å of any atom of the bound\nligand, and if there are at least two such inter-atomic contacts, the residue is considered part\nof the binding site. To ensure methodological consistency across datasets, we additionally\ndefined binding pockets using a simple geometric criterion, including all protein atoms within\n5 Å of any non-hydrogen atom of the bound ligand. All analyses were repeated using this\ndefinition, and the results remained consistent across both definitions (Figs. S8–S10 and S14-\nS15).”\nUnder this unified pocket definition, the average number of residues per pocket was\nrecalculated: BioLip2 increased from 9.1 to 12.5 residues, matching AlphaFill (12.5). The DEL\n(28.1) and FDA-AD (16.1) pockets still remain larger than BioLiP2 and AlphaFill, reflecting\nthe generally larger and more chemically complex nature of synthetic drug molecules and DEL\nhits compared to regular ligands. While minor quantitative changes were observed (e.g., slightly\nlower enrichment of charged residues and slightly higher hydrophobic residue proportions in\nBioLiP2), the overall trends and conclusions remain unchanged: DEL pockets are larger and\nmore hydrophobic, exhibiting weaker polar and hydrogen-bond interactions than BioLiP2\npockets. We have also tested the robustness of ErePOC against these two different pocket\ndefinitions, and found very consistent results (Figure S8-S10, S14-S15). Accordingly, we have\nadded the following sentences to the Section 2.3:\n“We note that for ErePOC model training and downstream analyses, the original BioLiP2\nbinding residue annotations were used to preserve experimentally validated contacts. Training\nand analyses using the alternative 5 Å pocket definition yielded highly similar results, as\ndemonstrated in Figure S8-S10.”\n15. The Reviewer commented:\nP7, line 164: “We further analysed the interactions between pocket residues and ligands\nusing the Arpeggio method(25) (Figure 2 a-c). Hydrophobic interactions accounted for 50.7%\nin DEL, 49.5% in FDA-AD, and 30.1% in BioLiP2. Hydrogen bond interactions were 3.8%\nfor DEL, 4.8% for FDA-AD, and 9.9% in BioLiP2, while polar interactions constituted 6.0%,\n7.8%, and 14.8%, respectively. Ionic interactions can be found in the DEL dataset (1.50%)\nbut were almost absent in FDA-AD (0.15%).”\nThe authors report \"Hydrogen bond interactions\" (e.g., 9.9% in BioLiP2) and \"polar\ninteractions\" (14.8% in BioLiP2) as two distinct categories. This is confusing because a\nhydrogen bond is a specific and strong type of polar interaction. What does the \"polar\ninteractions\" category actually contain if it excludes hydrogen bonds?\nAuthor reply:\nWe thank the reviewer for this insightful comment. In Arpeggio, hydrogen bonds are defined\nas strong, directional polar interactions that satisfy specific distance and angle criteria. The\n“polar interactions” category, by contrast, encompasses contacts between polar atoms that do\nnot meet these stringent criteria and thus are not classified as hydrogen bonds. In addition,\n\"weak polar interactions\" represent even more relaxed polar contacts. While all three categories\ninvolve polar atoms, Arpeggio distinguishes them to enable a more nuanced characterization of\nligand–pocket interactions. We have now clarified this distinction in the manuscript as follows:\n“The interaction types included in our analysis were van der Waals, hydrogen bonds, weak\nhydrogen bonds, halogen bonds, ionic interactions, aromatic interactions, hydrophobic\ninteractions, carbonyl interactions, polar interactions (contacts between polar atoms that do\nnot meet the geometric criteria for hydrogen bonds), and weak polar interactions.” (Page 22)\n16. The Reviewer commented:\nMoreover, the authors state that ionic interactions are more common in the DEL set (1.50%)\nthan the FDA-AD set (0.15%). Given the tiny size of the DEL dataset (n=128), could this 1.5%\nbe an artifact arising from just a few charged ligands in their small sample? Also, the\ncorresponding value for the massive BioLiP2 dataset should be reported?\nAuthor reply:\nWe thank the reviewers for these valuable comments and for prompting a closer examination of this\npoint. First, we refined the FDA-AD dataset to include only exact matches to FDA-approved drugs\n(Tanimoto similarity = 1), reducing the dataset from 630 to 340 targets. In this filtered dataset, 24\nFDA-AD targets exhibited ionic interactions, corresponding to 102 atomic pairs classified as ionic\nby Arpeggio. These account for 0.7% of all ligand–protein atomic interaction pairs (102 out of\n13,862). In comparison, 34 targets in the DEL dataset contained ionic interactions, totaling 116\natomic pairs, or 1.3% of all ligand–protein interactions (116 out of 8,903). For BioLiP2, ionic\ninteractions comprised 3.9% of the total.\nThese results confirm that ionic interactions are consistently observed across all datasets and are\nunlikely to be artifacts of sample size. The slightly higher frequency in the DEL dataset likely\nreflects the investigational, pre-optimization status of these compounds, which encompass broader\nchemical diversity and have not yet been optimized for metabolic stability. The revised manuscript\nnow incorporates this clarification:\n“Ionic interactions were slightly more prevalent in DEL pockets (1.3%) than in FDA-AD pockets\n(0.7%), while BioLiP2 pockets exhibited a higher fraction of ionic interactions (3.9%). This pattern\nmay reflect the early, pre-optimization status of DEL compounds compared to approved drugs.”\n(Section 2.2)\n17. The Reviewer commented:\nP16, line 446: The low-quality dataset from AlphaFill is a critical issue that is not adequately\naddressed. The authors' only acknowledgement of this problem is a single sentence stating\nthat the \"quality and resolution of the predicted complexes were relatively low\" due to\n\"potential clashes.\" It appears that the authors used all 330,434 structures without any\nquality-based filtering. Surely this brings into question the results from the AlphaFill\nanalysis as the defined pockets may be artifacts or the wrong size.\nAuthor reply:\nWe thank the reviewer for raising this important point. We fully agree that the structural quality\nof AlphaFill entries is relatively low compared to the other three datasets. This is precisely why\nmost of our analyses are built upon on BioLiP2, DEL and FDA-AD datasets, with AlphaFill\nresults serving only as a secondary comparison.\nFollowing the reviewer’s suggestion, we have now implemented a quality-control procedure\nbased on steric clash detection using an in-house Python script built on Biopython’s Bio.PDB.\nStructures were discarded if the pocket clash score was ≥0.005, or the ligand clash score ≥0.01,\nor the number of protein–ligand clashes ≥5, resulting in the removal of ~11% of entries and\nyielding 293,019 relatively high-quality complexes (Methods, Section 4.1). We repeated all\nrelevant analyses on this filtered dataset and observed only minor changes. For example, the\naverage number of residues per pocket changed slightly from 12.88 to 12.46. All relevant\nstatistics have been updated in the manuscript, along with Figures 1, S6, and S10.\nCorresponding revisions have been made to the Methods (Section 4.1) as follows:\n“Additionally, we curated predicted ligand-protein complex structures from the AlphaFill\ndatabase. AlphaFill transfers ligands, cofactors, and metal ions to AlphaFold-predicted models\nbased on sequence and structural similarities. However, due to potential clashes between the\ntransplanted ligands and the predicted receptor structures, the quality and resolution of the\npredicted complexes were relatively low. To improve the structural quality of AlphaFill entries,\nwe implemented a steric clash detection procedure using an in-house Python script. Steric\nclashes were defined as any pair of non-hydrogen atoms separated by less than 1.2 Å. For each\npocket and ligand, we calculated a clash score as the fraction of atom pairs that fell below this\nthreshold. Protein–ligand clashes were quantified as the number of atom pairs between the\npocket and ligand with interatomic distances shorter than 1.2 Å. Structures were discarded if\nthe pocket clash score was ≥ 0.005, or the ligand clash score was ≥ 0.01, or if the number of\nprotein-ligand clashes was ≥ 5. This procedure removed approximately 11% of the AlphaFill\ndataset, yielding 293,019 relatively high-quality ligand–protein complexes for downstream\nanalysis.”\n18. The Reviewer commented:\nP16, line 454-457: “Ligands in these structures were marked using the 'HET' keyword, and\nTanimoto similarity based on molecular fingerprints was calculated 455 between these\nligands and the FDA drug list. A ligand was considered an FDA-approved drug if its 456\nTanimoto similarity exceeded 0.7.”\nWhat fingerprint (FP) was used when doing the Tanimoto calculation? Without knowing this,\nthe score thresholds are meaningless as interpretation varies from one FP to another. In any\ncase, 0.7 seems very low, and it may find similar compounds but not the exact molecule.\nAuthor reply:\nWe thank the reviewer for highlighting this important point. As noted in our response to\nComment #11, we now set the threshold to 1.0, ensuring exact chemical identity. The Tanimoto\nsimilarity was computed using RDKit with Morgan fingerprints (radius = 2), which is now\nexplicitly stated in the Methods section (Section 4.1):\n“Ligands in these structures were marked using the ‘HET’ keyword, and Tanimoto similarity\nbased on Morgan fingerprints (radius = 2) was calculated using RDKit between these ligands\nand the FDA drug list. A ligand was considered an FDA-approved drug only if its Tanimoto\nsimilarity equaled 1.0, ensuring exact chemical identity.”\n19. The Reviewer commented:\nP17, line 458: “...resulting in a final dataset of 630 unique crystal structures of drug-pocket\ncomplexes.” Although authors mention unique crystal structures, they fail to address the\nmassive overrepresentation of certain drug-target families in the Protein Data Bank (PDB)\n(e.g., kinases, HIV protease, and heat shock proteins). Dozens or even hundreds of these\nPDBs could contain the exact same drug binding to the exact same protein pocket. The\nresults will reflect the specific properties of a few over-studied protein families rather than\nthe general characteristics of FDA-approved drug pockets.\nAuthor reply:\nWe thank the reviewer for raising this important point. To address this comment, we analyzed\nthe family-level composition of the FDA-AD dataset and included the results in Figure 5B.\nAfter applying the Tanimoto similarity = 1 filter described above, the dataset comprised 340\nunique drug–pocket complexes spanning 31 distinct protein families. The most represented\nclasses were transferases (20.8%), hydrolases (18.1%), oxidoreductases (14.8%), DNA-binding\nproteins (7.3%), and receptors (6.9%). This distribution suggests that the FDA-AD dataset is\nnot dominated by a few over-studied protein families but instead reflects the general\ncharacteristics of drug-binding targets. These results and the corresponding discussion have\nbeen added to Section 2.5 of the revised manuscript as follows:\n“Among the predicted human proteins, 17.9% were classified as transferases, 11.6% as\nhydrolases, and 9.4% each for DNA-binding proteins and oxidoreductases. In comparison, the\n(17.4%) and receptors (9.7%). The FDA-AD dataset, which encompasses 31 protein families,\nshowed a similarly broad distribution, with transferases (20.8%), hydrolases (18.1%),\noxidoreductases (14.8%), DNA-binding proteins (7.3%), and receptors (6.9%) as the most\nfrequent classes. … Interestingly, transferases, hydrolases, and oxidoreductases are\nconsistently enriched across all three datasets (DEL, FDA-AD, and predicted DEL-compatible\npockets), underscoring their central roles as preferred families for DEL screening.” (Section\n2.5)\nResponse to Reviewer #3\n1. The Reviewer commented:\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nAuthor reply:\nWe sincerely thank the reviewer who co-reviewed this manuscript. We truly appreciate their\nefforts and constructive feedback, which have contributed meaningfully to improving the\nclarity and rigor of our work."}, {"round": 2, "reviewer_comments": {"reviewer_2": "The authors have addressed the issues we raised.\nSigned Noel M. O'Boyle, Melissa F. Adasme", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter