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Brandon Dimcheff

Bio: Brandon Dimcheff is an academic researcher from University of Michigan. The author has contributed to research in topics: Data management & Workflow. The author has an hindex of 1, co-authored 2 publications receiving 132 citations.

Papers
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Journal ArticleDOI
TL;DR: Binding MOAD (Mother of All Databases) is a database of 9836 protein–ligand crystal structures that has almost doubled in size since it was originally introduced in 2004, demonstrating steady growth with each annual update.
Abstract: Binding MOAD (Mother of All Databases) is a database of 9836 protein–ligand crystal structures. All biologically relevant ligands are annotated, and experimental binding-affinity data is reported when available. Binding MOAD has almost doubled in size since it was originally introduced in 2004, demonstrating steady growth with each annual update. Several technologies, such as natural language processing, help drive this constant expansion. Along with increasing data, Binding MOAD has improved usability. The website now showcases a faster, more featured viewer to examine the protein–ligand structures. Ligands have additional chemical data, allowing for cheminformatics mining. Lastly, logins are no longer necessary, and Binding MOAD is freely available to all at http://www.BindingMOAD.org.

149 citations

Journal ArticleDOI
TL;DR: Binding MOAD is the largest collection of high-quality, protein-ligand complexes available from the Protein Data Bank and BUDA (Binding Unstructured Data Analysis) is a custom workflow tool that incorporates natural language processing technologies to facilitate the annotation process.
Abstract: Binding MOAD (Mother of All Databases) is the largest collection of high-quality, protein-ligand complexes available from the Protein Data Bank. It has grown to 9837 hand-curated entries. Here, we describe our semi-annual updating procedures and BUDA (Binding Unstructured Data Analysis), a custom workflow tool that incorporates natural language processing technologies to facilitate the annotation process.

2 citations


Cited by
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Journal ArticleDOI
TL;DR: ChEMBL is an Open Data database containing binding, functional and ADMET information for a large number of drug-like bioactive compounds to maximize their quality and utility across a wide range of chemical biology and drug-discovery research problems.
Abstract: ChEMBL is an Open Data database containing binding, functional and ADMET information for a large number of drug-like bioactive compounds. These data are manually abstracted from the primary published literature on a regular basis, then further curated and standardized to maximize their quality and utility across a wide range of chemical biology and drug-discovery research problems. Currently, the database contains 5.4 million bioactivity measurements for more than 1 million compounds and 5200 protein targets. Access is available through a web-based interface, data downloads and web services at: https://www.ebi.ac.uk/chembldb.

2,956 citations

Journal ArticleDOI
TL;DR: To facilitate template-based ligand–protein docking, virtual ligand screening and protein function annotations, a hierarchical procedure for assessing the biological relevance of ligands present in the PDB structures is developed which involves a four-step biological feature filtering followed by careful manual verifications.
Abstract: BioLiP (http://zhanglab.ccmb.med.umich.edu/BioLiP/) is a semi-manually curated database for biologically relevant ligand-protein interactions. Establishing interactions between protein and biologically relevant ligands is an important step toward understanding the protein functions. Most ligand-binding sites prediction methods use the protein structures from the Protein Data Bank (PDB) as templates. However, not all ligands present in the PDB are biologically relevant, as small molecules are often used as additives for solving the protein structures. To facilitate template-based ligand-protein docking, virtual ligand screening and protein function annotations, we develop a hierarchical procedure for assessing the biological relevance of ligands present in the PDB structures, which involves a four-step biological feature filtering followed by careful manual verifications. This procedure is used for BioLiP construction. Each entry in BioLiP contains annotations on: ligand-binding residues, ligand-binding affinity, catalytic sites, Enzyme Commission numbers, Gene Ontology terms and cross-links to the other databases. In addition, to facilitate the use of BioLiP for function annotation of uncharacterized proteins, a new consensus-based algorithm COACH is developed to predict ligand-binding sites from protein sequence or using 3D structure. The BioLiP database is updated weekly and the current release contains 204 223 entries.

548 citations

Journal ArticleDOI
TL;DR: This work proposes here a fast machine-learning approach for predicting binding affinities using state-of-the-art 3D-convolutional neural networks and compares this approach to other machine- learning and scoring methods using several diverse data sets.
Abstract: Accurately predicting protein–ligand binding affinities is an important problem in computational chemistry since it can substantially accelerate drug discovery for virtual screening and lead optimization. We propose here a fast machine-learning approach for predicting binding affinities using state-of-the-art 3D-convolutional neural networks and compare this approach to other machine-learning and scoring methods using several diverse data sets. The results for the standard PDBbind (v.2016) core test-set are state-of-the-art with a Pearson’s correlation coefficient of 0.82 and a RMSE of 1.27 in pK units between experimental and predicted affinity, but accuracy is still very sensitive to the specific protein used. KDEEP is made available via PlayMolecule.org for users to test easily their own protein–ligand complexes, with each prediction taking a fraction of a second. We believe that the speed, performance, and ease of use of KDEEP makes it already an attractive scoring function for modern computational ch...

531 citations

Journal ArticleDOI
TL;DR: Three basic types of scoring functions (force-field, empirical, and knowledge-based) and the consensus scoring technique that are used for protein-ligand docking are reviewed and a discussion of the challenges faced by existing scoring functions and possible future directions for developing improved scoring functions is discussed.
Abstract: The scoring function is one of the most important components in structure-based drug design. Despite considerable success, accurate and rapid prediction of protein–ligand interactions is still a challenge in molecular docking. In this perspective, we have reviewed three basic types of scoring functions (force-field, empirical, and knowledge-based) and the consensus scoring technique that are used for protein–ligand docking. The commonly-used assessment criteria and publicly available protein–ligand databases for performance evaluation of the scoring functions have also been presented and discussed. We end with a discussion of the challenges faced by existing scoring functions and possible future directions for developing improved scoring functions.

396 citations

Journal ArticleDOI
TL;DR: This review outlines the latest progress and challenges in polypharmacology studies and opens novel avenues to rationally design the next generation of more effective, but less toxic, therapeutic agents.
Abstract: In recent years, even with remarkable scientific advancements and a significant increase of global research and development spending, drugs are frequently withdrawn from markets. This is primarily due to their side effects or toxicities. Drug molecules often interact with multiple targets, coined as polypharmacology, and the unintended drug-target interactions could cause side effects. Polypharmacology remains one of the major challenges in drug development, and it opens novel avenues to rationally design the next generation of more effective, but less toxic, therapeutic agents. This review outlines the latest progress and challenges in polypharmacology studies.

361 citations