Shermo: A general code for calculating molecular thermochemistry properties

The ability to differentiate between highly similar C-H bonds in a given molecule remains a fundamental challenge in organic chemistry. In particular, the lack of sufficient steric and electronic differences between C-H bonds located distal to functional groups has prevented the development of site-selective catalysts with broad scope. An emerging approach to circumvent this obstacle is to utilize the distance between a target C-H bond and a coordinating functional group, along with the geometry of the cyclic transition state in directed C-H activation, as core molecular recognition parameters to differentiate between multiple C-H bonds. In this Perspective, we discuss the advent and recent advances of this concept. We cover a wide range of transition-metal-catalyzed, template-directed remote C-H activation reactions of alcohols, carboxylic acids, sulfonates, phosphonates, and amines. Additionally, we review eminent examples which take advantage of non-covalent interactions to achieve regiocontrol. Continued advancement of this distance- and geometry-based differentiation approach for regioselective remote C-H functionalization reactions may lead to the ultimate realization of molecular editing: the freedom to modify organic molecules at any site, in any order.

Achieving Site-Selectivity for C-H Activation Processes Based on Distance and Geometry: A Carpenter's Approach.

Accurate prediction of chemical reactions in solution is challenging for current state-of-the-art approaches based on transition state modelling with density functional theory. Models based on machine learning have emerged as a promising alternative to address these problems, but these models currently lack the precision to give crucial information on the magnitude of barrier heights, influence of solvents and catalysts and extent of regio- and chemoselectivity. Here, we construct hybrid models which combine the traditional transition state modelling and machine learning to accurately predict reaction barriers. We train a Gaussian Process Regression model to reproduce high-quality experimental kinetic data for the nucleophilic aromatic substitution reaction and use it to predict barriers with a mean absolute error of 0.77 kcal mol−1 for an external test set. The model was further validated on regio- and chemoselectivity prediction on patent reaction data and achieved a competitive top-1 accuracy of 86%, despite not being trained explicitly for this task. Importantly, the model gives error bars for its predictions that can be used for risk assessment by the end user. Hybrid models emerge as the preferred alternative for accurate reaction prediction in the very common low-data situation where only 100–150 rate constants are available for a reaction class. With recent advances in deep learning for quickly predicting barriers and transition state geometries from density functional theory, we envision that hybrid models will soon become a standard alternative to complement current machine learning approaches based on ground-state physical organic descriptors or structural information such as molecular graphs or fingerprints.

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Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

Chemists often use statistical analysis of reaction data with molecular descriptors to identify structure-reactivity relationships, which can enable prediction and mechanistic understanding. In thi...

Univariate classification of phosphine ligation state and reactivity in cross-coupling catalysis.

Hydroxylation of aryl carbon-hydrogen bonds with transition metal catalysts has proven challenging when oxygen is used as the oxidant. Here, we report a palladium complex bearing a bidentate pyridine/pyridone ligand that efficiently catalyzes this reaction at ring positions adjacent to carboxylic acids. Infrared, x-ray, and computational analysis support a possible role of ligand tautomerization from mono-anionic (L,X) to neutral (L,L) coordination in the catalytic cycle of aerobic carbon-hydrogen hydroxylation reaction. The conventional site selectivity dictated by heterocycles is overturned by this catalyst, thus allowing late-stage modification of compounds of pharmaceutical interest at previously inaccessible sites.

A tautomeric ligand enables directed C‒H hydroxylation with molecular oxygen

GoodVibes is an open-source Python toolkit for processing the results of quantum chemical calculations. Thermochemical data are not simply parsed, but evaluated by evaluation of translational, rotational, vibrational and electronic partition functions. Changes in concentration, pressure, and temperature can be applied, and deficiencies in the rigid rotor harmonic oscillator treatment can be corrected. Vibrational scaling factors can also be applied by automatic detection of the level of theory and basis set. Absolute and relative thermochemical values are output to text and graphical plots in seconds. GoodVibes provides a transparent and reproducible way to process raw computational data into publication-quality tables and figures without the use of spreadsheets.

GoodVibes: automated thermochemistry for heterogeneous computational chemistry data

Machine-readable chemical structure representations are foundational in all attempts to harness machine learning for the prediction of reactivities, selectivities, and chemical properties directly from molecular structure The featurization of discrete chemical structures into a continuous vector space is a critical phase undertaken before model selection, and the development of new ways to quantitatively encode molecules is an active area of research In this Account, we highlight the application and suitability of different representations, from expert-guided "engineered" descriptors to automatically "learned" features, in different prediction tasks relevant to organic and organometallic chemistry, where differing amounts of training data are available These tasks include statistical models of stereo- and enantioselectivity, thermochemistry, and kinetics developed using experimental and quantum chemical dataThe use of expert-guided molecular descriptors provides an opportunity to incorporate chemical knowledge, domain expertise, and physical constraints into statistical modeling In applications to stereoselective organic and organometallic catalysis, where data sets may be relatively small and 3D-geometries and conformations play an important role, mechanistically informed features can be used successfully to obtain predictive statistical models that are also chemically interpretable We provide an overview of several recent applications of this approach to obtain quantitative models for reactivity and selectivity, where topological descriptors, quantum mechanical calculations of electronic and steric properties, along with conformational ensembles, all feature as essential ingredients of the molecular representations usedAlternatively, more flexible, general-purpose molecular representations such as attributed molecular graphs can be used with machine learning approaches to learn the complex relationship between a structure and prediction target This approach has the potential to out-perform more traditional representation methods such as "hand-crafted" molecular descriptors, particularly as data set sizes grow One area where this is particularly relevant is in the use of large sets of quantum mechanical data to train quantitative structure-property relationships A general approach toward curating useful data sets and training highly accurate graph neural network models is discussed in the context of organic bond dissociation enthalpies, where this strategy outperforms regression using precomputed descriptorsFinally, we describe how graph neural network predictions can be incorporated into mechanistically informed statistical models of chemical reactivity and selectivity Once trained, this approach avoids the expensive computational overhead associated with quantum mechanical calculations, while maintaining chemical interpretability We illustrate examples for which fast predictions of bond dissociation enthalpy and of the identities of radicals formed through cleavage of a molecule's weakest bond are used in simple physical models of site-selectivity and reactivity

Guilian Luchini

Papers

GoodVibes: automated thermochemistry for heterogeneous computational chemistry data

Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties.

Data-mining the diaryl(thio)urea conformational landscape: Understanding the contrasting behavior of ureas and thioureas with quantum chemistry

bobbypaton/GoodVibes: GoodVibes v3.0.0