Seeking rules governing mixed molecular crystallization

Mixed crystals result when components of the structure are randomly replaced by analogues in ratios that can be varied continuously over certain ranges. Mixed crystals are useful because their properties can be adjusted by increments, imply by altering the ratio of components. Unfortunately, no clear rules exist to predict when two compounds are similar enough to form mixed crystals containing substantial amounts of both. To gain further understanding, we have used single-crystal X-ray diffraction, computational methods, and other tools to study mixed crystallizations within a selected set of structurally related compounds. This work has allowed us to begin to clarify the rules governing the phenomenon by showing that mixed crystals can have compositions and properties that vary continuously over wide ranges, even when the individual components do not normally crystallize in the same way. Moreover, close agreement of the results of our experiments and computational modeling demonstrates that reliable predictions about mixed crystallization can be made, despite the complexity of the phenomenon

Roles and opportunities for machine learning in organic molecular crystal structure prediction and its applications

The field of crystal structure prediction (CSP) has changed dramatically over the past decade and methods now exist that will strongly influence the way that new materials are discovered, in areas such as pharmaceutical materials and the discovery of new, functional molecular materials with targeted properties. Machine learning (ML) methods, which are being applied in many areas of chemistry, are starting to be explored for CSP. This overview will discuss the areas where ML is expected to have the greatest impact on CSP and its applications: improving the evaluation of energies; analyzing the landscapes of predicted structures and for the identification of promising molecules for a target property

Non-Invasive Detection Of Urothelial Cancer Through The Analysis Of Driver Gene Mutations And Aneuploidy

Current non-invasive approaches for detection of urothelial cancers are suboptimal. We developed a test to detect urothelial neoplasms using DNA recovered from cells shed into urine. UroSEEK incorporates massive parallel sequencing assays for mutations in 11 genes and copy number changes on 39 chromosome arms. In 570 patients at risk for bladder cancer ( BC), UroSEEK was positive in 83% of those who developed BC. Combined with cytology, UroSEEK detected 95% of patients who developed BC. Of 56 patients with upper tract urothelial cancer, 75% tested positive by UroSEEK, including 79% of those with non-invasive tumors. UroSEEK detected genetic abnormalities in 68% of urines obtained from BC patients under surveillance who demonstrated clinical evidence of recurrence. The advantages of UroSEEK over cytology were evident in low-grade BCs; UroSEEK detected 67% of cases whereas cytology detected none. These results establish the foundation for a new non-invasive approach for detection of urothelial cancer.WoSScopu