There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The relatively small diffuse function-augmented basis set, 3-21+G, is shown to describe anion geometries and proton affinities adequately. The diffuse sp orbital exponents are recommended for general use to augment larger basis sets.

Efficient diffuse function‐augmented basis sets for anion calculations. III. The 3‐21+G basis set for first‐row elements, Li–F

A. Water Exchange 2326 B. Proton Exchange 2327 C. Electronic Relaxation 2327 D. Relaxivity 2331 E. Outerand Second-Sphere Relaxivity 2334 F. Methods of Improving Relaxivity 2336 V. Macromolecular Conjugates 2336 A. Introduction 2336 B. General Conjugation Methods 2336 C. Synthetic Linear Polymers 2336 D. Synthetic Dendrimer-Based Agents 2338 E. Naturally Occurring Polymers (Proteins, Polysaccharides, and Nucleic Acids) 2339

https://mriquestions.com/uploads/3/4/5/7/34572113/lauffer_1999_review.pdf

Gadolinium(III) Chelates as MRI Contrast Agents: Structure, Dynamics, and Applications

I. Introduction: Conceptual vs Fundamental andComputational Aspects of DFT1793II. Fundamental and Computational Aspects of DFT 1795A. The Basics of DFT: The Hohenberg−KohnTheorems1795B. DFT as a Tool for Calculating Atomic andMolecular Properties: The Kohn−ShamEquations1796C. Electronic Chemical Potential andElectronegativity: Bridging Computational andConceptual DFT1797III. DFT-Based Concepts and Principles 1798A. General Scheme: Nalewajski’s ChargeSensitivity Analysis1798B. Concepts and Their Calculation 18001. Electronegativity and the ElectronicChemical Potential18002. Global Hardness and Softness 18023. The Electronic Fukui Function, LocalSoftness, and Softness Kernel18074. Local Hardness and Hardness Kernel 18135. The Molecular Shape FunctionsSimilarity 18146. The Nuclear Fukui Function and ItsDerivatives18167. Spin-Polarized Generalizations 18198. Solvent Effects 18209. Time Evolution of Reactivity Indices 1821C. Principles 18221. Sanderson’s Electronegativity EqualizationPrinciple18222. Pearson’s Hard and Soft Acids andBases Principle18253. The Maximum Hardness Principle 1829IV. Applications 1833A. Atoms and Functional Groups 1833B. Molecular Properties 18381. Dipole Moment, Hardness, Softness, andRelated Properties18382. Conformation 18403. Aromaticity 1840C. Reactivity 18421. Introduction 18422. Comparison of Intramolecular ReactivitySequences18443. Comparison of Intermolecular ReactivitySequences18494. Excited States 1857D. Clusters and Catalysis 1858V. Conclusions 1860VI. Glossary of Most Important Symbols andAcronyms1860VII. Acknowledgments 1861VIII. Note Added in Proof 1862IX. References 1865

/pdf/conceptual-density-functional-theory-btz9wwrxv9.pdf

Conceptual density functional theory.

Asymmetric enamine catalysis.

/pdf/structure-of-trans-di-3-iodobvenzoato-bis-1-3-diaminopropane-1inwq78p15.pdf

Structure of trans-Di(3-iodobvenzoato)bis(1,3-diaminopropane)copper(II), C20H28CuI2N4O4

Abstract De novo enzyme design has sought to introduce active sites and substrate-binding pockets that are predicted to catalyse a reaction of interest into geometrically compatible native scaffolds 1,2 , but has been limited by a lack of suitable protein structures and the complexity of native protein sequence–structure relationships. Here we describe a deep-learning-based ‘family-wide hallucination’ approach that generates large numbers of idealized protein structures containing diverse pocket shapes and designed sequences that encode them. We use these scaffolds to design artificial luciferases that selectively catalyse the oxidative chemiluminescence of the synthetic luciferin substrates diphenylterazine 3 and 2-deoxycoelenterazine. The designed active sites position an arginine guanidinium group adjacent to an anion that develops during the reaction in a binding pocket with high shape complementarity. For both luciferin substrates, we obtain designed luciferases with high selectivity; the most active of these is a small (13.9 kDa) and thermostable (with a melting temperature higher than 95 °C) enzyme that has a catalytic efficiency on diphenylterazine ( k cat / K m = 10 6 M −1 s −1 ) comparable to that of native luciferases, but a much higher substrate specificity. The creation of highly active and specific biocatalysts from scratch with broad applications in biomedicine is a key milestone for computational enzyme design, and our approach should enable generation of a wide range of luciferases and other enzymes. 

/pdf/de-novo-design-of-luciferases-using-deep-learning-1upyx26y.pdf

De novo design of luciferases using deep learning

Selectivity of (trifluoromethyl)chlorocarbene

Ab initio calculation of the effects of cyano substituents on the Cope rearrangement

A 5-step total synthesis of microfungal alkaloid (+/-)-lapatin B has been accomplished via a key 2-aza-Diels-Alder reaction. Bronsted acids catalyze the cycloaddition step and provide improved exo selectivity. This synthetic route has been applied to the construction of related spiro-quinazoline structures.

K. N. Houk

Papers

Structure of trans-Di(3-iodobvenzoato)bis(1,3-diaminopropane)copper(II), C20H28CuI2N4O4

De novo design of luciferases using deep learning

Selectivity of (trifluoromethyl)chlorocarbene

Ab initio calculation of the effects of cyano substituents on the Cope rearrangement

Acid-catalyzed aza-Diels-Alder reactions for the total synthesis of (+/-)-lapatin B.