DOI: 10.6075/j0kw5dk5
title: LLNL 3D Protein-Ligand Dataset for Anti-viral Screening against SARS-CoV-2. In Lawrence Livermore National Laboratory (LLNL) Open Data Initiative

description: ML-HDF Structure The ML-HDF files use a hdf5 format. Refer to the readme file for the internal data structure of the ml-hdf files. The Ligand ID is a combination of an internal id used in ConveyorLC and the ZINC database id (e.g., 134_ZINC000003807172). Each ligand has up to 10 docking poses (1, 2, 3, ..., 10). Each docking pose has a dataset named "data" in a 2D array, which is the 3D atomic representation of the binding pocket region with the docked ligand atoms. The dimension of the array is [N, 22] where N is the total number of atoms of the protein-ligand complex, and 22 is a feature vector for each atom. The atom feature includes the following information: --3D coordinate (x, y, z) of the atom --Atomic number --Atom hybridization --Number of heavy atom bonds (heavy valence) --Number of bonds with other heteroatoms (hetero valence) --Structural properties --Partial charge For more detail about the atom features, please refer to the article "Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference" at https://arxiv.org/pdf/2005.07704.pdf How to Read the ML-HDF Files The ML-HDF files can be read using the h5py package in Python or the hdf5 C++ API. Find more detail in the readme document. Model Evaluation: The ground truth labels (binding affinity, toxicity, etc) are not available for this dataset. However, the LLNL Covid-19 Data Portal (https://covid19drugscreen.llnl.gov) provides physics-based scoring results including Molecular Mechanics/Generalized Born-Solvent Accessible Surface Area (MM/GBSA) rescoring (CDT4mmgbsa), a machine learning fusion model predictions using 3D CNN and 3D Spatial Graph CNN (FAST) (https://github.com/llnl/fast), and molecular dynamics (MD) simulation results with the single-point MM/GBSA calculation averaged over multiple time steps, as references. The website also provides safety and pharmacokinetic property prediction results generated with the ATOM Modeling Pipeline (AMPL) and Maestro workflow manager. For more information about the original database and the scoring outputs, see the main LLNL Covid-19 Data Portal (https://covid19drugscreen.llnl.gov).
description: This dataset contains protein-ligand complexes in a 3D representation for anti-viral drug screening against SARS-CoV-2. This is a part of the Lawrence Livermore National Laboratory Covid-19 Therapeutic Design database, but is specifically designed to facilitate machine learning and other data science tasks with regard to both efficacy (protein-ligand binding affinity) and safety. This complex dataset is called "ml-hdf", comprised of ligands and four potential binding pockets of the SARS-CoV-2 protein targets in a 3D atomic representation. The ligands in this dataset includes Federal Drug Administration (FDA) approved drugs and "Other-world-approved" drugs that have been approved for use by the EU, Canada and Japan. The compounds were docked against two binding pockets from the Spike protein (spike, spike1) and two conformations of the main protease (protease, protease2).