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  1. Explored was the competitive ring-closing metathesis vs. ring-rearrangement metathesis of bicyclo[3.2.1]octenes prepared by a simple and convergent synthesis from bicyclic alkylidenemalono-nitriles and allylic electrophiles. It was uncovered that ring-closing metathesis occurs exclusively on the tetraene-variant, yielding unique, stereochemically and functionally rich polycyclic bridged frameworks, whereas the reduced version (a triene) undergoes ring-rearrangement metathesis to 5 – 6 – 5 fused ring systems resembling the isoryanodane core.
  2. Abstract

    Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models. In chemistry, ML has been used to develop models for predicting molecular properties, for example quantum mechanics (QM) calculated potential energy surfaces and atomic charge models. The ANI-1x and ANI-1ccx ML-based general-purpose potentials for organic molecules were developed through active learning; an automated data diversification process. Here, we describe the ANI-1x and ANI-1ccx data sets. To demonstrate data diversity, we visualize it with a dimensionality reduction scheme, and contrast against existing data sets. The ANI-1x data set contains multiple QM properties from 5 M density functional theory calculations, while the ANI-1ccx data set contains 500 k data points obtained with an accurate CCSD(T)/CBS extrapolation. Approximately 14 million CPU core-hours were expended to generate this data. Multiple QM calculated properties for the chemical elements C, H, N, and O are provided: energies, atomic forces, multipole moments, atomic charges, etc. We provide this data to the community to aid research and development of ML models for chemistry.

  3. Abstract

    Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist’s toolset. The use of computer simulations requires a balance between cost and accuracy: quantum-mechanical methods provide high accuracy but are computationally expensive and scale poorly to large systems, while classical force fields are cheap and scalable, but lack transferability to new systems. Machine learning can be used to achieve the best of both approaches. Here we train a general-purpose neural network potential (ANI-1ccx) that approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and drug-like molecular torsions. This is achieved by training a network to DFT data then using transfer learning techniques to retrain on a dataset of gold standard QM calculations (CCSD(T)/CBS) that optimally spans chemical space. The resulting potential is broadly applicable to materials science, biology, and chemistry, and billions of times faster than CCSD(T)/CBS calculations.

  4. Saccharomyces cerevisiae OYE 3 shares 80% sequence identity with the well-studied Saccharomyces pastorianus OYE 1; however, wild-type OYE 3 shows different stereoselectivities toward some alkene substrates. Site-saturation mutagenesis of Trp 116 in OYE 3 followed by substrate profiling showed that the mutations had relatively little effect, opposite to that observed previously for OYE 1. The X-ray crystal structures of unliganded and phenol-bound OYE 3 were solved to 1.8 and 1.9 Å resolution, respectively. Both structures were nearly identical to that of OYE 1, with only a single amino acid difference in the active site region (Ser 296 versus Phe 296, part of loop 6). Despite their essentially identical static X-ray structures, molecular dynamics (MD) simulations revealed that loop 6 conformations differed significantly in solution between OYE 3 and OYE 1. In OYE 3, loop 6 remained nearly as open as observed in the crystal structure; by contrast, loop 6 closed over the active site of OYE 1 by ca. 4 Å. Loop closure likely generates a greater number of active site protein contacts for substrate bound to OYE 1 as compared to OYE 3. These differences provide an explanation for the differing stereoselectivities of OYE 3 and OYE 1, despitemore »their nearly identical X-ray crystal structures.« less