Summary of Vina, Gnina and Pafnucy performance on DUD-E targets.

Enrichment of ligands versus property-matched decoys is widely used to test and optimize docking library screens. However, the unconstrained optimization of enrichment alone can mislead, leading to false confidence in prospective performance. This can arise by over-optimizing for enrichment against property-matched decoys, without considering the full spectrum of molecules to be found in a true large library screen. Adding decoys representing charge extrema helps mitigate over-optimizing for electrostatic interactions. Adding decoys that represent the overall characteristics of the library to be docked allows one to sample molecules not represented by ligands and property-matched decoys but that one will encounter in a prospective screen. An optimized version of the DUD-E set (DUDE-Z), as well as Extrema and sets representing broad features of the library (Goldilocks), is developed here. We also explore the variability that one can encounter in enrichment calculations and how that can temper one’s confidence in small enrichment differences. The new tools and new decoy sets are freely available at http://tldr.docking.org and http://dudez.docking.org.

Training and test datasets of the paper "Improving the versatility of deep learning-based protein-ligand interaction prediction for accurate binding affinity scoring and virtual screening".

Comparative evaluation of virtual screening methods requires a rigorous benchmarking procedure on diverse, realistic, and unbiased data sets. Recent investigations from numerous research groups unambiguously demonstrate that artificially constructed ligand sets classically used by the community (e.g., DUD, DUD-E, MUV) are unfortunately biased by both obvious and hidden chemical biases, therefore overestimating the true accuracy of virtual screening methods. We herewith present a novel data set (LIT-PCBA) specifically designed for virtual screening and machine learning. LIT-PCBA relies on 149 dose–response PubChem bioassays that were additionally processed to remove false positives and assay artifacts and keep active and inactive compounds within similar molecular property ranges. To ascertain that the data set is suited to both ligand-based and structure-based virtual screening, target sets were restricted to single protein targets for which at least one X-ray structure is available in complex with ligands of the same phenotype (e.g., inhibitor, inverse agonist) as that of the PubChem active compounds. Preliminary virtual screening on the 21 remaining target sets with state-of-the-art orthogonal methods (2D fingerprint similarity, 3D shape similarity, molecular docking) enabled us to select 15 target sets for which at least one of the three screening methods is able to enrich the top 1%-ranked compounds in true actives by at least a factor of 2. The corresponding ligand sets (training, validation) were finally unbiased by the recently described asymmetric validation embedding (AVE) procedure to afford the LIT-PCBA data set, consisting of 15 targets and 7844 confirmed active and 407,381 confirmed inactive compounds. The data set mimics experimental screening decks in terms of hit rate (ratio of active to inactive compounds) and potency distribution. It is available online at http://drugdesign.unistra.fr/LIT-PCBA for download and for benchmarking novel virtual screening methods, notably those relying on machine learning.

The repository contains the benchmarking data obtained alongside the first version of DockM8.

The file structure is explained in DockM8_v1_file_structure_explanation.txt

We hope this data is useful for benchmarking scoring functions and machine learning models, as well as being a large repository of pre-docked poses using a variety of algorithms.

Ligand-only CNN models that achieved high AUC (greater than 0.9) for COMT.

Additional digital data to "RASPD+: Fast protein-ligand binding free energy prediction using simplified physicochemical features" (ChemRxiv preprint:https://doi.org/10.26434/chemrxiv.12636704).

Associated code can be found at: https://github.com/HITS-MCM/RASPDplus

Files:

weights.tar.gz: contains the model weights of one random dataset split and its associated crossvalidation folds. Used for standard RASPD+ evaluation.

additional_model_replicates.tar.gz: contains the remaining models trained on the full set of descriptors.

external_test_sets.tar.gz: contains the descriptor tables for all external test sets used

dude.tar.gz: contains the descriptor tables for and several identifier lists for evaluation on the Directory of Useful Decoys - Enhanced (DUD-E)

run_outputs.tar.gz: Performance metric data and predicted values created during the model training and evaluation runs. Basis for the figures and metrics in the manuscript.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This dataset provides information about the number of properties, residents, and average property values for Dude Hadley Road cross streets in Perdido, AL.

The mean and SD of the AUC values across three target groups.

ADMMR map collection: Dude Mining Claims, Claim Map; 1 in. to 200 feet; 22 x 17 in.

Explore the historical Whois records related to dude.com (Domain). Get insights into ownership history and changes over time.

This dataset provides information on 67 in India as of March, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

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This dataset is about books and is filtered where the book is It's love, dude, featuring 7 columns including author, BNB id, book, book publisher, and ISBN. The preview is ordered by publication date (descending).

Credit report of The Bio Dude Inc contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

Financial overview and grant giving statistics of The Dude Ranchers' Association

The Dude Ranchers Association (DRA) is a non-profit organization dedicated to preserving and promoting the dude ranching industry. Founded in 1926, the DRA represents over 90 dude and guest ranches across the Western United States and Canada. These ranches offer an all-inclusive vacation experience, providing a unique opportunity for guests to participate in various activities such as horseback riding, hiking, fishing, and more.

The DRA is committed to maintaining the highest standards of quality and authenticity within the dude ranching industry. To achieve this, the organization has established a rigorous inspection and approval process, ensuring that member ranches meet certain criteria. The DRA also provides resources and support to its member ranches, helping them to thrive and continue to offer exceptional vacations to their guests.

Given the size of the relevant chemical space for drug discovery, working with fully enumerated compound libraries (especially in three-dimensional (3D)) is unfeasible. Nonenumerated virtual chemical spaces are a practical solution to this issue, where compounds are described as building blocks which are then connected by rules. One concrete example of such is the BioSolveIT chemical spaces file format (.space). Tools to search these space-files exist that are using ligand-based methods including two-dimensional (2D) fingerprint similarity, substructure matching, and fuzzier similarity metrics such as FTrees. However, there is no software available that enables the screening of these nonenumerated spaces using protein structure as the input query. Here, a hybrid ligand/structure-based virtual screening tool, called SpaceHASTEN, was developed on top of SpaceLight, FTrees, LigPrep, and Glide to allow efficient structure-based virtual screening of nonenumerated chemical spaces. SpaceHASTEN was validated using three public targets picked from the DUD-E data set. It was able to retrieve a large number of diverse and novel high-scoring compounds (virtual hits) from nonenumerated chemical spaces of billions of molecules, after docking a few million compounds. The software can be freely used and is available from http://github.com/TuomoKalliokoski/SpaceHASTEN.

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