DrugSpaceX: a large screenable and synthetically tractable database extending drug space

One of the most prominent topics in drug discovery is efficient exploration of the vast drug-like chemical space to find synthesizable and novel chemical structures with desired biological properties. To address this challenge, we created the DrugSpaceX (https://drugspacex.simm.ac.cn/) database based on expert-defined transformations of approved drug molecules. The current version of DrugSpaceX contains >100 million transformed chemical products for virtual screening, with outstanding characteristics in terms of structural novelty, diversity and large three-dimensional chemical space coverage. To illustrate its practical application in drug discovery, we used a case study of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, to show DrugSpaceX performing a quick search of initial hit compounds. Additionally, for ligand identification and optimization purposes, DrugSpaceX also provides several subsets for download, including a 10% diversity subset, an extended drug-like subset, a drug-like subset, a lead-like subset, and a fragment-like subset. In addition to chemical properties and transformation instructions, DrugSpaceX can locate the position of transformation, which will enable medicinal chemists to easily integrate strategy planning and protection design.

Automatic Cavity Identification and Decomposition into Subpockets with CAVIAR

<p><b>Motivation.</b> The detection of small molecules binding sites in proteins is central to structure-based drug design and chemical biology. Many tools were developed in the last 40 years, but few of them are still available in 2020, open-source, and suitable for the analysis of large databases or for the integration in automatic workflows. No software can characterize subpockets solely with the information of the protein structure, a pivotal concept in fragment-based drug design.</p> <p><b>Results.</b> CAVIAR is a new open source tool for protein <u>cav</u>ity <u>i</u>dentification <u>a</u>nd <u>r</u>ationalization, supporting PDB and mmCIF files as well as DCD trajectories from molecular dynamics simulations. The protein structure serves as input for automatic cavity detection and computation of properties, including ligandability. A subcavity segmentation algorithm decomposes binding sites into subpockets without requiring the presence of a ligand. The defined subpockets mimick the empirical definitions of subpockets in medicinal chemistry projects. A tool like CAVIAR may be valuable to support chemical biology, medicinal chemistry and ligand identification efforts. Our analysis of the PDB shows that liganded cavities tend to be bigger, more hydrophobic and more complex than apo cavities. Moreover, in line with the paradigm of fragment-based drug design, the binding affinity scales relatively well with the number of subcavities filled by the ligand. Compounds binding to more than three subcavities are mostly in the nanomolar or better range of affinities to their target.</p> <p><b>Availability and implementation. </b>Installation notes, user manual and support for CAVIAR are available at <a href="https://jr-marchand.github.io/caviar/">https://jr-marchand.github.io/caviar/</a>. The CAVIAR GUI and CAVIAR command line tool are available on GitHub at <a href="https://github.com/jr-marchand/caviar">https://github.com/jr-marchand/caviar</a> and a conda package is hosted on Anaconda cloud at <a href="https://anaconda.org/jr-marchand/caviar">https://anaconda.org/jr-marchand/caviar</a>. The software suite is free and all of the source code is available under a permissive MIT license. The lists of PDB files used for validation, as well as the results of subpocket decomposition with CAVIAR and DoGSite are hosted on GitHub at <a href="https://github.com/jr-marchand/caviar/tree/master/validation_sets">https://github.com/jr-marchand/caviar/tree/master/validation_sets</a>.</p> <p><b>Contact: </b><a href="mailto:[email protected]">[email protected]</a>; <a href="mailto:[email protected]">[email protected]</a> </p><br>

Automatic Cavity Identification and Decomposition into Subpockets with CAVIAR

<p><b>Motivation.</b> The detection of small molecules binding sites in proteins is central to structure-based drug design and chemical biology. Many tools were developed in the last 40 years, but few of them are still available in 2020, open-source, and suitable for the analysis of large databases or for the integration in automatic workflows. No software can characterize subpockets solely with the information of the protein structure, a pivotal concept in fragment-based drug design.</p> <p><b>Results.</b> CAVIAR is a new open source tool for protein <u>cav</u>ity <u>i</u>dentification <u>a</u>nd <u>r</u>ationalization, supporting PDB and mmCIF files as well as DCD trajectories from molecular dynamics simulations. The protein structure serves as input for automatic cavity detection and computation of properties, including ligandability. A subcavity segmentation algorithm decomposes binding sites into subpockets without requiring the presence of a ligand. The defined subpockets mimick the empirical definitions of subpockets in medicinal chemistry projects. A tool like CAVIAR may be valuable to support chemical biology, medicinal chemistry and ligand identification efforts. Our analysis of the PDB shows that liganded cavities tend to be bigger, more hydrophobic and more complex than apo cavities. Moreover, in line with the paradigm of fragment-based drug design, the binding affinity scales relatively well with the number of subcavities filled by the ligand. Compounds binding to more than three subcavities are mostly in the nanomolar or better range of affinities to their target.</p> <p><b>Availability and implementation. </b>Installation notes, user manual and support for CAVIAR are available at <a href="https://jr-marchand.github.io/caviar/">https://jr-marchand.github.io/caviar/</a>. The CAVIAR GUI and CAVIAR command line tool are available on GitHub at <a href="https://github.com/jr-marchand/caviar">https://github.com/jr-marchand/caviar</a> and a conda package is hosted on Anaconda cloud at <a href="https://anaconda.org/jr-marchand/caviar">https://anaconda.org/jr-marchand/caviar</a>. The software suite is free and all of the source code is available under a permissive MIT license. The lists of PDB files used for validation, as well as the results of subpocket decomposition with CAVIAR and DoGSite are hosted on GitHub at <a href="https://github.com/jr-marchand/caviar/tree/master/validation_sets">https://github.com/jr-marchand/caviar/tree/master/validation_sets</a>.</p> <p><b>Contact: </b><a href="mailto:[email protected]">[email protected]</a>; <a href="mailto:[email protected]">[email protected]</a> </p><br>

Navigating the DNA encoded libraries chemical space

DNA-encoded library (DEL) technology is a novel ligand identification strategy that allows the synthesis and screening of unprecedented chemical diversity more efficiently than conventional methods. However, no reports have been published to systematically study how to increase the diversity and improve the molecular property space that can be covered with DEL. This report describes the development and application of eDESIGNER, an algorithm that comprehensively generates all possible library designs, enumerates and profiles samples from each library and evaluates them to select the libraries to be synthesized. This tool utilizes suitable on-DNA chemistries and available building blocks to design and identify libraries with a pre-defined molecular weight distribution and maximal diversity compared with compound collections from other sources.

Automatic Cavity Identification and Decomposition into Subpockets with CAVIAR

<div>Motivation: The detection of small molecules binding sites in proteins is central to structure based drug design. Many tools were developed in the last 40 years, but only few of them are available today, open-source, and suitable for the analysis of large databases or for the integration in automatic workflows. In addition, no software can characterize subpockets solely with the information of the protein structure, a pivotal concept in fragment-based drug design.</div><div>Results: CAVIAR is a new open source tool for protein cavity identification and rationalization. Protein pockets are automatically detected based on the protein structure. It comprises a subcavity segmentation algorithm that decomposes binding sites into subpockets without requiring the presence of a ligand. The defined subpockets mimick the empirical definitions of subpockets in medicinal chemistry projects. A tool like CAVIAR may be valuable to support chemical biology, medicinal chemistry and ligand identification efforts. Our analysis of the entire PDB and the</div><div>PDBBind confirms that liganded cavities tend to be bigger, more hydrophobic and more complex than apo cavities. Moreover, in line with the paradigm of fragment-based drug design, the binding affinity scales relatively well with the number of subcavities filled by the ligand. Compounds binding to more than three of the subcavities identified by CAVIAR are mostly in the nanomolar or better range of affinities to their target.</div><div>Availability and implementation: Installation notes, user manual and support for CAVIAR are available at https://jr-marchand.github.io/caviar/. The CAVIAR GUI and CAVIAR command line tool are available on GitHub at https://github.com/jr-marchand/caviar and the package is hosted on Anaconda cloud at https://anaconda.org/jr-marchand/caviar under a MIT license. The GitHub</div><div>repository also hosts the validation datasets.</div>