J. Appl. Cryst.の発刊に際して

This paper describes the current update on macromolecular model validation services that are provided at the MolProbity website, emphasizing changes and additions since the previous review in 2010. There have been many infrastructure improvements, including rewrite of previous Java utilities to now use existing or newly written Python utilities in the open-source CCTBX portion of the Phenix software system. This improves long-term maintainability and enhances the thorough integration of MolProbity-style validation within Phenix. There is now a complete MolProbity mirror site at http://molprobity.manchester.ac.uk. GitHub serves our open-source code, reference datasets, and the resulting multi-dimensional distributions that define most validation criteria. Coordinate output after Asn/Gln/His "flip" correction is now more idealized, since the post-refinement step has apparently often been skipped in the past. Two distinct sets of heavy-atom-to-hydrogen distances and accompanying van der Waals radii have been researched and improved in accuracy, one for the electron-cloud-center positions suitable for X-ray crystallography and one for nuclear positions. New validations include messages at input about problem-causing format irregularities, updates of Ramachandran and rotamer criteria from the million quality-filtered residues in a new reference dataset, the CaBLAM Cα-CO virtual-angle analysis of backbone and secondary structure for cryoEM or low-resolution X-ray, and flagging of the very rare cis-nonProline and twisted peptides which have recently been greatly overused. Due to wide application of MolProbity validation and corrections by the research community, in Phenix, and at the worldwide Protein Data Bank, newly deposited structures have continued to improve greatly as measured by MolProbity's unique all-atom clashscore.

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MolProbity: More and better reference data for improved all-atom structure validation.

Two-dimensional (2D) materials have emerged as promising candidates for next-generation electronic and optoelectronic applications. Yet, only a few dozen 2D materials have been successfully synthesized or exfoliated. Here, we search for 2D materials that can be easily exfoliated from their parent compounds. Starting from 108,423 unique, experimentally known 3D compounds, we identify a subset of 5,619 compounds that appear layered according to robust geometric and bonding criteria. High-throughput calculations using van der Waals density functional theory, validated against experimental structural data and calculated random phase approximation binding energies, further allowed the identification of 1,825 compounds that are either easily or potentially exfoliable. In particular, the subset of 1,036 easily exfoliable cases provides novel structural prototypes and simple ternary compounds as well as a large portfolio of materials to search from for optimal properties. For a subset of 258 compounds, we explore vibrational, electronic, magnetic and topological properties, identifying 56 ferromagnetic and antiferromagnetic systems, including half-metals and half-semiconductors.

Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds

Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods. This work introduces MoleculeNet, a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem open source library). MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance. However, this result comes with caveats. Learnable representations still struggle to deal with complex tasks under data scarcity and highly imbalanced classification. For quantum mechanical and biophysical datasets, the use of physics-aware featurizations can be more important than choice of particular learning algorithm.

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MoleculeNet: a benchmark for molecular machine learning

The Cambridge Crystallographic Data Centre (CCDC) was established in 1965 to record numerical, chemical and bibliographic data relating to published organic and metal–organic crystal structures. The Cambridge Structural Database (CSD) now stores data for nearly 700 000 structures and is a comprehensive and fully retrospective historical archive of small-molecule crystallography. Nearly 40 000 new structures are added each year. As X-ray crystallography celebrates its centenary as a subject, and the CCDC approaches its own 50th year, this article traces the origins of the CCDC as a publicly funded organization and its onward development into a self-financing charitable institution. Principally, however, we describe the growth of the CSD and its extensive associated software system, and summarize its impact and value as a basis for research in structural chemistry, materials science and the life sciences, including drug discovery and drug development. Finally, the article considers the CCDC’s funding model in relation to open access and open data paradigms.

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The Cambridge Structural Database in retrospect and prospect.

The Crystallography Open Database (COD), which is a project that aims to gather all available inorganic, metal–organic and small organic molecule structural data in one database, is described. The database adopts an open-access model. The COD currently contains ∼80 000 entries in crystallographic information file format, with nearly full coverage of the International Union of Crystallography publications, and is growing in size and quality.

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Crystallography Open Database - an open-access collection of crystal structures

A syntax-correcting CIF parser, COD::CIF::Parser, is presented that can parse CIF 1.1 files and accurately report the position and the nature of the discovered syntactic problems. In addition, the parser is able to automatically fix the most common and the most obvious syntactic deficiencies of the input files. Bindings for Perl, C and Python programming environments are available. Based on COD::CIF::Parser, the cod-tools package for manipulating the CIFs in the Crystallography Open Database (COD) has been developed. The cod-tools package has been successfully used for continuous updates of the data in the automated COD data deposition pipeline, and to check the validity of COD data against the IUCr data validation guidelines. The performance, capabilities and applications of different parsers are compared.

/pdf/cod-cif-parser-an-error-correcting-cif-parser-for-the-perl-3fj2uai0me.pdf

COD::CIF::Parser: an error‐correcting CIF parser for the Perl language

/pdf/software-for-maintaining-and-expanding-the-crystallography-1gxuiieh4k.pdf

Justas Butkus

Papers

Crystallography Open Database - an open-access collection of crystal structures

COD::CIF::Parser: an error‐correcting CIF parser for the Perl language

Software for maintaining and expanding the Crystallography Open Database