Prediction of Hydrate and Solvate Formation Using Statistical Models

Novel, knowledge based models for the prediction of hydrate and solvate formation are introduced, which require only the molecular formula as input. A data set of more than 19 000 organic, nonionic, and nonpolymeric molecules was extracted from the Cambridge Structural Database. Molecules that formed solvates were compared with those that did not using molecular descriptors and statistical methods, which allowed the identification of chemical properties that contribute to solvate formation. The study was conducted for five types of solvates: ethanol, methanol, dichloromethane, chloroform, and water solvates. The identified properties were all related to the size and branching of the molecules and to the hydrogen bonding ability of the molecules. The corresponding molecular descriptors were used to fit logistic regression models to predict the probability of any given molecule to form a solvate. The established models were able to predict the behavior of ∼80% of the data correctly using only two descriptors in the predictive model

Chemoenzymatic Synthesis of Homogeneous Ultralow Molecular Weight Heparins

Ultralow molecular weight (ULMW) heparins are sulfated glycans that are clinically used to treat thrombotic disorders. ULMW heparins range from 1500 to 3000 daltons, corresponding from 5 to 10 saccharide units. The commercial drug Arixtra (fondaparinux sodium) is a structurally homogeneous ULMW heparin pentasaccharide that is synthesized through a lengthy chemical process. Here, we report 10- and 12-step chemoenzymatic syntheses of two structurally homogeneous ULMW heparins (MW = 1778.5 and 1816.5) in 45 and 37% overall yield, respectively, starting from a simple disaccharide. These ULMW heparins display excellent in vitro anticoagulant activity and comparable pharmacokinetic properties to Arixtra, as demonstrated in a rabbit model. The chemoenzymatic approach is scalable and shows promise for a more efficient route to synthesize this important class of medicinal agent

Leading edge chemical crystallography service provision and its impact on crystallographic data science in the twenty-first century

National facilities provide state-of-the-art crystallographic instrumentation and processes and tend to act as an indicator for the direction of a community in the medium term. There has been a significant step up in terms of instrumentation and approach in the last 10 years which has driven data generation. This has had a significant impact on databases – in turn we observe a substantial change in the use of the Cambridge Structural Database (CSD) from relatively basic search/retrieve to gaining deep understanding about factors that govern the solid state. Databases are now able to drive new science in areas such as crystal engineering. Looking forward, we will see more automated pipelining of the data generation process, and this will require better integration with databases. Databases will provide more predictive power – and this will inform the science/crystallography that should be done