All of the individual docking data and ESSENCE-Dock consensus results for 21 diverse DUD-E targets as presented in the paper "ESSENCE-Dock: A Consensus-Based Approach to Enhance Virtual Screening Enrichment in Drug Discovery".

The data is sorted per DUD-E target. It contains the prepared data that was used for the docking calculations (in the Undocked directory), as well as our docking results. Finally, our ESSENCE-Dock Consensus results are included as well

Docking calculations were performed using:

• Metascreener (V1.1) (Gnina and LeadFinder Calculations; prefix VS_GN_ and VS_LF_ respectively)
• DiffDockHPC (v1.0) (DiffDock calculations; prefix VS_DD_ )

The consensus calculations were performed using ESSENCE-Dock, available via Metascreener as well.

The whole methodology and all of the details are described in the ESSENCE-Dock paper: https://doi.org/10.1021/acs.jcim.3c01982

Paper Abstract

Drug development is a complex, costly, and time-consuming endeavor. While high-throughput screening (HTS) plays a critical role in the discovery stage, it is one of many factors contributing to these challenges. In certain contexts, virtual screening can complement HTS, potentially offering a more streamlined approach in the initial stages of drug discovery. Molecular docking is an example of a popular virtual screening technique that is often used for this purpose, however, its effectiveness can vary greatly. This has led to the use of consensus docking approaches, which combine results from different docking methods to improve the identification of active compounds and reduce the occurrence of false positives. However, many of these methods do not fully leverage the latest advancements in molecular docking.
In response, we present ESSENCE-Dock (Effective Structural Screening ENrichment ConsEnsus Dock), a new consensus docking workflow aimed at decreasing false positives and increasing the discovery of active compounds. By utilizing a combination of novel docking algorithms, we improve the selection process for potential active compounds. ESSENCE-Dock has been made to be user-friendly, requiring only a few simple commands to perform a complete screening, while also being designed for use in high-performance computing (HPC) environments.

Molecular Docking Analysis.

The code, dataset, and model weights are described in the paper "Interformer: An Interaction-Aware Model for Protein-Ligand Docking and Affinity Prediction."

experiment_results.zip: Contains generated results that can reproduce the result from the reported paper.

benchmark.zip: Contains docking and affinity input data of the interformer. You can use the source code to make predictions and reproduce the number of the reported paper.

checkpoints.zip: Contains one weight for the Energy and four PoseScore and Affinity models.

source_code_1.0.zip: Contains the initial version of the source code.

interformer_train.tar.gz: Contains prepared training data for interformer. poses/ contains all structure need for training, poses/ligand contains the re-docking poses generated by interformer energy, poses/ligand/rcsb contains the conformation of reference ligand, poses/pocket contains all pocket extract by raw PDB from rcsb, poses/uff contains all ligand conformation minimized using UFF from reference ligand, and train/ contains the training csv.

You can also find the newest version of the source code at https://github.com/tencent-ailab/Interformer

The data proves that a longer self-immolative linker of FMP improves the probe responsivity toward FAPα. To uncover the underlying mechanism, theoretical molecular docking simulation is further carried out to elucidate the different FAPα responsivity towards FMP and N-FMP by MolAICal 1.3. The X-ray crystal structure of FAPα from the Protein Data Bank (PDB code 1Z68) is used. 3D structures of FMP and N-FMP are obtained and energetically optimized by a ChemDraw 3D software. After molecular docking, FMP presents a strong hydrogen bond between the peptide substrate of the probe and FAPα residues at the site of Val540, Ser548, Gln547, Gly542, and Ser546 in the active pocket. By contrast, N-FMP shows relatively weak hydrogen bond interaction with only one site of FAPα at the Gln547 residue in the active pocket. The theoretical simulations indicate that the higher affinity of FAPα towards FMP and thus promotes the enzymatic cleavage efficiency relative to N-FMP, which is consistent well with t...

This repository contains relevant data from the study:

M. Goullieux, V. Zoete, U.F. Roehrig
Two-Step Covalent Docking with Attracting Cavities

Fragment-based drug design is a popular approach in drug discovery, which makes use of computational methods such as molecular docking. To assess fragment placement performance of molecular docking programs, we constructed LEADS-FRAG, a benchmark data set containing 93 high-quality protein-fragment complexes that were selected from the Protein Data Bank using a rational and unbiased process. The data set contains fully prepared protein and fragment structures and is publicly available. Moreover, we used LEADS-FRAG for evaluating the small-molecule docking programs AutoDock, AutoDock Vina, FlexX, and GOLD for their fragment docking performance. GOLD in combination with the scoring function ChemPLP and AutoDock Vina performed best and generated near-native conformations (root mean square deviation

Paper Abstract:

Sampling physically valid ligand-binding poses remains a major challenge in molecular docking, particularly for unseen or structurally diverse targets. We introduce PocketVina, a fast and memory-efficient, search-based docking framework that combines pocket prediction with systematic multi-pocket exploration. We evaluate PocketVina across four established benchmarks—PDBbind2020 (timesplit and unseen), DockGen, Astex, and PoseBusters—and observe consistently strong performance in sampling physically valid docking poses. PocketVina achieves state-of-the-art performance when jointly considering ligand r.m.s.d. and physical validity (PB-valid), while remaining competitive with deep learning–based approaches in terms of r.m.s.d. alone, particularly on structurally diverse and previously unseen targets. PocketVina also maintains state-of-the-art physically valid docking accuracy across ligands with varying degrees of flexibility. We further introduce TargetDock-AI, a benchmarking dataset we curated, consisting of over 500,000 protein–ligand pairs, and a partition of the dataset labeled with PubChem activity annotations. On this large-scale dataset, PocketVina successfully discriminates active from inactive targets, outperforming a deep learning baseline while requiring significantly less GPU memory and runtime. PocketVina offers a robust and scalable docking strategy that requires no task-specific training and runs efficiently on standard GPUs, making it well-suited for high-throughput virtual screening and structure-based drug discovery.

The *.dock4 files are result files of molecular docking with the software Autodock Vina to the human ASIC1a closed state model, of the peptides FRRFa and KNFLRFa (FRRF.dock4, KNFLRF.dock4) that can be visualized with structure viewing programs such as UCSF Chimera on the closed ASIC1a model file (closed_ASIC_pH7.4.pdb). The file “FRRF_KNFLRF_complexes.pdb” provides the structures of selected poses of FRRFa and KNFLRFa peptides docked to the closed conformation of the human ASIC1a model.

Compilation of mooring and dock numbers for RI embayments. This dataset is associated with the following publication: Boothman, W.S., and L. Coiro. Mapping Hypoxia Response to Estuarine Nitrogen Loading Using Molybdenum in Sediments. Estuaries and Coasts. Estuarine Research Federation, Port Republic, MD, USA, 46(5): 1363–1374, (2023).

This is a processed molecular dataset from this https://doi.ccs.ornl.gov/ui/doi/348 adding up to 50M molecules for the training and 486K molecules for the validation. Instructions on how to use/run/train this dataset can be found here: https://code.ornl.gov/candle/mlmol

The protein-ligand scoring function plays an important role in computer-aided drug discovery, which is heavily used in virtual screening and lead optimization. In this study, we developed a new empirical protein-ligand scoring function, which is a linear combination of empirical energy components, including hydrogen bond, van der Waals, electrostatic, hydrophobic, ��-stacking, ��-cation, and metal-ligand interaction. Different from previous empirical scoring functions, AA-Score uses several amino acid-specific empirical interaction components. We tested AA-Score on several test sets. The resulting performance shows AA-Score performs well on scoring, docking, and ranking compared with other widely used traditional scoring functions. Our results suggest that AA-Score gains substantial improvements from using detailed protein-ligand interaction components. Besides, we developed an easy-to-use tool to analyze protein-ligand interaction fingerprint and predict binding affinity using AA-Score.

FTDMP docking results for protein-protein, protein-DNA, protein-RNA benchmarks.

FTDMP is a software system for running docking experiments and scoring/ranking multimeric models. This dataset contains FTDMP docking results for protein-protein, protein-DNA, protein-RNA benchmarks. The FTDMP framework itself is available at https://github.com/kliment-olechnovic/ftdmp.

Every *.tar.gz file in this dataset contains two folders: results for unbound-unbound and bound-bound docking. These folders contain results for the benchmark cases:

252 folders with results for the protein-protein docking benchmark cases [1].47 folders with results for the protein-DNA docking benchmark cases [2].42 folders with results for the protein-RNA docking benchmark cases [3-6].

Every folder is named according to the PDB ID of the complex. The folders contain:

1. A subfolder named relaxed_top_complexes. This subfolder contains 200 pdb files of relaxed [7] top docking models.2. A text file named scoring_results-ranks.txt. It contains the names of the models (that are in the relaxed_top_complexes folder) in the ranked order. This means that the first model in the file is considered to be the best prediction by the FTDMP framework.3. A text file named cad_scores.txt. It contains interface CAD-score and binding site CAD-score [8] results for every model.4. A text file named rmsd_results.txt, which is available only for protein-DNA and protein-RNA cases. The file contains ligand-RMSD values for the models, where the DNA/RNA is considered as the ligand.5. A text file named DockQ_results.txt, which is available only for the protein-protein docking cases. The file contains DockQ [9] results for every model, as well as model accuracy based on CAPRI criteria (Incorrect, Acceptable, Medium, High)6. A text file named binding_site_CAD-scores.txt, which contains the binding site CAD-score from the protein side for RNA and DNA docking. This binding site CAD-score shows how accurately the ligand (DNA/RNA) was docked to the protein without taking the orientation of the ligand into consideration. In the case of protein-protein docking the binding site CAD-score file is available only for antibody-antigen docking targets and contains the binding site (epitope) CAD-score for the antigen.

The ligand-RMSD, CAD-scores, and DockQ scores were all calculated by comparing the models to the corresponding targets. The target structures are available at https://zenodo.org/records/10517524. These target structures have the same residue numbering as the models available here.

REFERENCES

[1] Guest, J. D., et al. (2021). An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants. Structure, 29(6), 606–621.e5.[2] van Dijk, M., Bonvin, A.M. (2008). A protein-DNA docking benchmark. Nucleic Acids Res, 36, e88. [3] Perez-Cano, L., et. Al. (2012). A protein-RNA docking benchmark (II): extended set from experimental and homology modeling data. Proteins, 80(7): 1872-1882. [4] Huang, S.Y., Zou, X. (2013). A nonredundant structure dataset for benchmarking protein-RNA computational docking. J Comput Chem, 34(4): 311-318. [5] Nithin, C., et. al. (2017). A non-redundant protein-RNA docking benchmark version 2.0. Proteins, 85(2) :256-267. [6] Zheng, J., et al. (2020). P3DOCK: a protein-RNA docking webserver based on template-based and template-free docking. Bioinformatics, 36(1), 96–103. [7] Eastman, P., et al.(2017). OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLOS Comp. Biol., 13(7): e1005659. [8] Olechnovic, K., Venclovas, C. (2020). Contact area-based structural analysis of proteins and their complexes using CAD-score. Methods Mol Biol, 2112, 75.[9] Basu, S., Wallner, B. (2016). DockQ: A Quality Measure for Protein-Protein Docking Models. PLoS ONE 11(8): e0161879.

Data sets, tools and computational techniques for modeling of protein interactions, including docking benchmarks, docking decoys and docking templates. Adequate computational techniques for modeling of protein interactions are important because of the growing number of known protein 3D structures, particularly in the context of structural genomics. The first release of the DOCKGROUND resource (Douguet et al., Bioinformatics 2006; 22:2612-2618) implemented a comprehensive database of cocrystallized (bound) protein-protein complexes in a relational database of annotated structures. Additional releases added features to the set of bound structures, such as regularly updated downloadable datasets: automatically generated nonredundant set, built according to most common criteria, and a manually curated set that includes only biological nonobligate complexes along with a number of additional useful characteristics. Also included are unbound (experimental and simulated) protein-protein complexes. Complexes from the bound dataset are used to identify crystallized unbound analogs. If such analogs do not exist, the unbound structures are simulated by rotamer library optimization. Thus, the database contains comprehensive sets of complexes suitable for large scale benchmarking of docking algorithms. Advanced methodologies for simulating unbound conformations are being explored for the next release. The Dockground project is developed by the Vakser lab at the Center for Bioinformatics at the University of Kansas. Parts of Dockground were co-developed by Dominique Douguet from the Center of Structural Biochemistry (INSERM U554 - CNRS UMR5048), Montpellier, France.

1.5 Docking Analysis. The X-ray structure of FAPα (PDB code 1Z68) is obtained from the Protein following Data Bank (http://www.rcsb.org/pdb). 3D structures of FMP and N-FMP are obtained and energetically optimized by ChemDraw 3D software. The MolAICal software package is used to process and save FAPα, FMP, and N-FMP in PDBQT molecular format. FAPα is the definition of a crystal ligand in the active pocket of molecular docking. Take x = 39.84 Å, y = 0.221 Å, and z = 59.383 Å as the center coordinate of the FAPα active pocket box, and the size of the grid box is set to 30 Å. The detained operations are as follows: 1.5.1 Software Requirement 1) MolAICal: https://molaical.github.io 2) UCSF Chimera: https://www.cgl.ucsf.edu/chimera 1.5.2 Prepare the Receptor Protein The X-ray structure of FAPα (PDB code 1Z68) was obtained from the protein following data bank (http://www.rcsb.org/pdb). Save the file in 1Z68.pdb format. 1.5.3 Prepare the Ligand Molecule 3D structures of FMP and N-FMP are obta...

We present a simple, modular graph-based convolutional neural network that takes structural information from protein–ligand complexes as input to generate models for activity and binding mode prediction. Complex structures are generated by a standard docking procedure and fed into a dual-graph architecture that includes separate subnetworks for the ligand bonded topology and the ligand-protein contact map. Recent work has indicated that data set bias drives many past promising results derived from combining deep learning and docking. Our dual-graph network allows contributions from ligand identity that give rise to such biases to be distinguished from effects of protein–ligand interactions on classification. We show that our neural network is capable of learning from protein structural information when, as in the case of binding mode prediction, an unbiased data set is constructed. We next develop a deep learning model for binding mode prediction that uses docking ranking as input in combination with docking structures. This strategy mirrors past consensus models and outperforms a baseline docking program (AutoDock Vina) in a variety of tests, including on cross-docking data sets that mimic real-world docking use cases. Furthermore, the magnitudes of network predictions serve as reliable measures of model confidence.

The affinity between the compounds and the targets in molecular docking, and the high-affinity information is summarized in S5

HDD Enclosure Docking Station Market Outlook

The global HDD Enclosure Docking Station market is projected to grow from USD 1.2 billion in 2023 to USD 2.5 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 8.2% over the forecast period. The market size expansion is driven by increased data storage needs and the rising adoption of external storage solutions across various sectors. Influential growth factors include advancements in technology, the proliferation of digital content, and the surge in data generation from both personal and commercial applications.

One of the primary growth drivers for the HDD Enclosure Docking Station market is the exponential rise in data generation. With the increased use of high-definition video, high-resolution photography, and massive datasets in both personal and professional contexts, the demand for external storage solutions has surged. These devices offer a convenient and efficient method to manage large volumes of data, making them indispensable for users who require flexible storage options. Additionally, the emergence of 4K and 8K video formats necessitates higher storage capabilities, further propelling the market.

Technological advancements in connectivity and data transfer speeds are also contributing significantly to the market's growth. The development of USB 3.0, USB-C, Thunderbolt, and eSATA has enhanced data transfer rates and improved the overall performance of HDD enclosure docking stations. These advanced connectivity options allow quicker and more efficient data handling, making these devices more appealing to both personal and commercial users. As technology continues to evolve, we can anticipate further enhancements that will drive market growth.

Moreover, the increasing adoption of smart devices and the Internet of Things (IoT) is another critical factor fueling market expansion. With the proliferation of IoT devices and smart gadgets, there is a growing need for additional storage solutions to manage the vast amounts of data generated. HDD enclosure docking stations provide a reliable and scalable solution to this challenge, making them an essential component in the modern digital ecosystem. This trend is expected to continue, bolstering market growth over the forecast period.

Regionally, North America and Europe dominate the HDD Enclosure Docking Station market due to high levels of technological adoption and the presence of major market players. However, the Asia Pacific region is expected to experience the fastest growth, driven by rapid industrialization, the booming electronics market, and increasing consumer awareness regarding data storage solutions. Countries like China, India, and Japan are poised to be significant contributors to the market's expansion in this region.

Product Type Analysis

The HDD Enclosure Docking Station market can be segmented by product type into Single Bay, Dual Bay, and Multi Bay docking stations. Single Bay docking stations are designed for individual hard drives and primarily cater to personal users who need additional storage for personal data, multimedia files, and small-scale backup solutions. These docking stations are simple to use, cost-effective, and offer a practical solution for users who do not require extensive storage capabilities. As a result, Single Bay docking stations hold a significant share of the market and are projected to maintain steady growth over the forecast period.

In contrast, Dual Bay docking stations cater to users who need to manage data across two hard drives simultaneously. This product type is popular among small business owners, IT professionals, and tech enthusiasts who require more extensive storage options and enhanced data management capabilities. Dual Bay docking stations offer features such as disk cloning, RAID support, and efficient data transfer, making them a preferred choice for users with moderate storage needs. The increasing adoption of Dual Bay docking stations in the commercial sector is expected to drive significant market growth in this segment.

Multi Bay docking stations represent the high-end segment of the market, catering to users with extensive data storage and management requirements. These docking stations can accommodate multiple hard drives simultaneously, offering advanced features such as RAID configuration, high-speed data transfer, and comprehensive data management capabilities. Multi Bay docking stations are widely used in data centers, large enterprises, and by professionals in fields such as video editing, graphic design, and scientific rese

HADDOCK docking models : HADDOCK decoys for 55 new entries in Docking Benchmark 5 ; The set contains decoys (PDB models) with associated HADDOCK scores, i-RMSD, l-RMSD, Fnat and input parameters for 55 complexes for the three stages of HADDOCK (rigid-body, semi-flexible refinement and water refinement)

80842 Global export shipment records of Docking Station with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This repository contains supplementary data to the journal article 'Redocking the PDB' by Flachsenberg et al. (https://doi.org/10.1021/acs.jcim.3c01573)[1]. In this paper, we described two datasets: The PDBScan22 dataset with a large set of 322,051 macromolecule–ligand binding sites generally suitable for redocking and the PDBScan22-HQ dataset with 21,355 binding sites passing different structure quality filters. These datasets were further characterized by calculating properties of the ligand (e.g., molecular weight), properties of the binding site (e.g., volume), and structure quality descriptors (e.g., crystal structure resolution). Additionally, we performed redocking experiments with our novel JAMDA structure preparation and docking workflow[1] and with AutoDock Vina[2,3]. Details for all these experiments and the dataset composition can be found in the journal article[1].

Here, we provide all the datasets, i.e., the PDBScan22 and PDBScan22-HQ datasets as well as the docking results and the additionally calculated properties (for the ligand, the binding sites, and structure quality descriptors). Furthermore, we give a detailed description of their content (i.e., the data types and a description of the column values). All datasets consist of CSV files with the actual data and associated metadata JSON files describing their content. The CSV/JSON files are compliant with the CSV on the web standard (https://csvw.org/).

General hints

• All docking experiment results consist of two CSV files, one with general information about the docking run (e.g., was it successful?) and one with individual pose results (i.e., score and RMSD to the crystal structure).
• All files (except for the docking pose tables) can be indexed uniquely by the column tuple '(pdb, name)' containing the PDB code of the complex (e.g., 1gm8) and the name ligand (in the format '
• All files (except for the docking pose tables) have exactly the same number of rows as the dataset they were calculated on (e.g., PDBScan22 or PDBScan22-HQ). However, some CSV files may have missing values (see also the JSON metadata files) in some or even all columns (except for 'pdb' and 'name').
• The docking pose tables also contain the 'pdb' and 'name' columns. However, these alone are not unique but only together with the 'rank' column (i.e., there might be multiple poses for each docking run or none).

Example usage

Using the pandas library (https://pandas.pydata.org/) in Python, we can calculate the number of protein-ligand complexes in the PDBScan22-HQ dataset with a top-ranked pose RMSD to the crystal structure ≤ 2.0 Å in the JAMDA redocking experiment and a molecular weight between 100 Da and 200 Da:

import pandas as pd

df = pd.read_csv('PDBScan22-HQ.csv')

df_poses = pd.read_csv('PDBScan22-HQ_JAMDA_NL_NR_poses.csv')

df_properties = pd.read_csv('PDBScan22_ligand_properties.csv')

merged = df.merge(df_properties, how='left', on=['pdb', 'name'])

merged = merged[(merged['MW'] >= 100) & (merged['MW'] <= 200)].merge(df_poses[df_poses['rank'] == 1], how='left', on=['pdb', 'name'])

nof_successful_top_ranked = (merged['rmsd_ai'] <= 2.0).sum()

nof_no_top_ranked = merged['rmsd_ai'].isna().sum()

Datasets

• PDBScan22.csv: This is the PDBScan22 dataset[1]. This dataset was derived from the PDB4. It contains macromolecule–ligand binding sites (defined by PDB code and ligand identifier) that can be read by the NAOMI library[5,6] and pass basic consistency filters.
• PDBScan22-HQ.csv: This is the PDBScan22-HQ dataset[1]. It contains macromolecule–ligand binding sites from the PDBScan22 dataset that pass certain structure quality filters described in our publication[1].
• PDBScan22-HQ-ADV-Success.csv: This is a subset of the PDBScan22-HQ dataset without 336 binding sites where AutoDock Vina[2,3] fails.
• PDBScan22-HQ-Macrocycles.csv: This is a subset of the PDBScan22-HQ dataset without 336 binding sites where AutoDock Vina[2,3] fails and only contains molecules with macrocycles with at least ten atoms.

Properties for PDBScan22

• PDBScan22_ligand_properties.csv: Conformation-independent properties of all ligand molecules in the PDBScan22 dataset. Properties were calculated using an in-house tool developed with the NAOMI library[5,6].
• PDBScan22_StructureProfiler_quality_descriptors.csv: Structure quality descriptors for the binding sites in the PDBScan22 dataset calculated using the StructureProfiler tool[7].
• PDBScan22_basic_complex_properties.csv: Simple properties of the binding sites in the PDBScan22 dataset. Properties were calculated using an in-house tool developed with the NAOMI library[5,6].

Properties for PDBScan22-HQ

• PDBScan22-HQ_DoGSite3_pocket_descriptors.csv: Binding site descriptors calculated for the binding sites in the PDBScan22-HQ dataset using the DoGSite3 tool[8].
• PDBScan22-HQ_molecule_types.csv: Assignment of ligands in the PDBScan22-HQ dataset (without 336 binding sites where AutoDock Vina fails) to different molecular classes (i.e., drug-like, fragment-like oligosaccharide, oligopeptide, cofactor, macrocyclic). A detailed description of the assignment can be found in our publication[1].

Docking results on PDBScan22

• PDBScan22_JAMDA_NL_NR.csv: Docking results of JAMDA[1] on the PDBScan22 dataset. This is the general overview for the docking runs; the pose results are given in 'PDBScan22_JAMDA_NL_NR_poses.csv'. For this experiment, the ligand was not considered during preprocessing of the binding site, and the binding site restriction mode (i.e., biasing the docking towards the crystal ligand position) was disabled.
• PDBScan22_JAMDA_NL_NR_poses.csv: Pose scores and RMSDs for the docking results of JAMDA[1] on the PDBScan22 dataset. For this experiment, the ligand was not considered during preprocessing of the binding site, and the binding site restriction mode (i.e., biasing the docking towards the crystal ligand position) was disabled.

Docking results on PDBScan22-HQ

• PDBScan22-HQ_JAMDA_NL_NR.csv: Docking results of JAMDA[1] on the PDBScan22-HQ dataset. This is the general overview for the docking runs; the pose results are given in 'PDBScan22-HQ_JAMDA_NL_NR_poses.csv'. For this experiment, the ligand was not considered during preprocessing of the binding site, and the binding site restriction mode (i.e., biasing the docking towards the crystal ligand position) was disabled.
• PDBScan22-HQ_JAMDA_NL_NR_poses.csv: Pose scores and RMSDs for the docking results of JAMDA[1] on the PDBScan22-HQ dataset. For this experiment, the ligand was not considered during preprocessing of the binding site, and the binding site restriction mode (i.e., biasing the docking towards the crystal ligand position) was disabled.
• PDBScan22-HQ_JAMDA_NL_WR.csv: Docking results of JAMDA[1] on the PDBScan22-HQ dataset. This is the general overview for the docking runs; the pose results are given in 'PDBScan22-HQ_JAMDA_NL_WR_poses.csv'. For this experiment, the ligand was not considered during preprocessing of the binding site, and the binding site restriction mode (i.e., biasing the docking towards the crystal ligand position) was enabled.
• PDBScan22-HQ_JAMDA_NL_WR_poses.csv: Pose scores and RMSDs for the docking results of JAMDA[1] on the PDBScan22-HQ dataset. For this experiment, the ligand was not considered during preprocessing of the binding site and the binding site restriction mode (i.e., biasing the docking towards the crystal ligand position) was enabled.
• PDBScan22-HQ_JAMDA_NW_NR.csv: Docking results of JAMDA[1] on the PDBScan22-HQ dataset. This is the general overview for the docking runs; the pose results are given in 'PDBScan22-HQ_JAMDA_NW_NR_poses.csv'. For this experiment, the ligand was not considered during preprocessing of the binding site, all water molecules were removed from the binding site during preprocessing, and the binding site restriction mode (i.e., biasing the docking towards the crystal ligand position) was disabled.
• PDBScan22-HQ_JAMDA_NW_NR_poses.csv: Pose scores and RMSDs for the docking results of JAMDA[1] on the PDBScan22-HQ dataset. For this experiment, the ligand was not considered during preprocessing of the binding site, all water molecules were removed from the binding site during preprocessing, and the binding site restriction mode (i.e., biasing the docking towards the crystal ligand position) was disabled.
• PDBScan22-HQ_JAMDA_NW_WR.csv: Docking results of JAMDA[1] on the PDBScan22-HQ dataset. This is the general overview for the docking runs; the pose results are given in 'PDBScan22-HQ_JAMDA_NW_WR_poses.csv'. For this experiment, the ligand was not considered during preprocessing of the binding site, all water molecules were removed from the binding site during preprocessing, and the binding site restriction mode (i.e., biasing the docking towards the crystal ligand position) was enabled.
• PDBScan22-HQ_JAMDA_NW_WR_poses.csv: Pose scores and RMSDs for the docking results of JAMDA[1] on the PDBScan22-HQ dataset. For this experiment, the ligand was not considered during preprocessing of the binding site, all water molecules were removed from the binding site during preprocessing, and the binding site restriction mode (i.e., biasing the docking towards the crystal ligand position) was enabled.
• PDBScan22-HQ_JAMDA_WL_NR.csv: Docking results of JAMDA[1] on the PDBScan22-HQ dataset. This is the general overview for the docking runs; the pose results are given in