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Fast and accurate prospective predictions of regioselectivity can significantly reduce the time and resources spent on unproductive transformations in the pharmaceutical industry. Density functional theory (DFT) reaction modeling through transition state theory (TST) and machine learning (ML) methods has been widely used to predict reaction outcomes such as selectivity. However, TST reaction modeling and ML methods are either time-consuming or data-dependent. Herein, we introduce a prototype seamlessly bridging ML and TST modeling by triggering resource-intensive but much less domain-sensitive DFT calculations only on less confident ML predictions. The proposed workflow was trained and tested on both the Pfizer internal dataset and the USPTO public dataset to predict regioselectivity for SNAr reactions. Our method is accurate and fast, which achieves 96.3 and 94.7% accuracy in predicting the correct major product on Pfizer and USPTO datasets, respectively, in a fraction of conventional TST computing time.

The hyperbranched polylysine fluoroborate was synthesized by NH3BF4-Lys NCA ROP in triethylamine at 15 °C, during which the cyclic dimer mainly initiated ROP as the secondary initiating species. The hyperbranched polylysine can be used as a stabilizer to prepare the NIR-absorbing HPlys@Au NPs.

Ibuzatrelvir (1) is a second-generation, orally bioavailable, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease inhibitor clinical candidate. Herein, we report the implementation of an in silico and high-throughput experimentation strategy leading to the identification of a rapid, efficient, and sustainable route to trans-4-trifluoromethyl-l-proline (2), a key building block for ibuzatrelvir. This novel synthetic route features a key stereochemical editing step to enable an efficient and scalable protocol that operates under mild conditions with high stereoselectivity, providing effective access to more than 235 kg of trans-4-trifluoromethyl-l-proline 2in a five-step synthetic sequence from readily available starting materials.

We describe an open-source computational toolkit (AARON: An Automated Reaction Optimizer for New catalysts) that automates the quantum mechanical geometry optimization and characterization of the transition state and intermediate structures required to predict the activities and selectivities of asymmetric catalytic reactions. Modern computational quantum chemistry has emerged as a powerful tool for explaining the selectivity and activity of asymmetric catalysts. However, reliably predicting the stereochemical outcome of realistic reactions often requires the geometry optimization of hundreds of transition state and intermediate structures, which is a tedious process. AARON automates these optimizations through an interface with a popular electronic structure package, accelerating quantum chemical workflows to enable the computational screening of potential catalysts. AARON is built using a collection of object-oriented Perl modules (AaronTools) that provide functionality to build and modify molecular and supramolecular structures. The main functionalities of AaronTools are also available as stand-alone command-line scripts. The core features of AaronTools and AARON are explained, and representative applications of AARON to both organocatalyzed and transition-metal-catalyzed reactions are presented.

Density functional theory, symmetry-adapted perturbation theory, and classical molecular dynamics simulations were used to understand the local orientations within columnar stacks of sumanene, sumaneneone, sumanenedione, and sumanenetrione, which can impact the electronic properties of the resulting materials. Unlike sumanene and sumaneneone, which pack in staggered columnar arrangements, sumanenetrione adopts an eclipsed configuration in the solid state. Reliable quantum chemical computations on stacked dimers and trimers of these molecules reveal that all four systems prefer staggered configurations. The tendency of sumanenetrione to pack in an eclipsed configuration was explained based on repulsive intercolumnar O···O interactions that override the inherent tendency of stacked columns to be staggered. Sumanenedione, for which crystal structure data are not available, was predicted to exhibit columnar packing with mixed staggered and eclipsed orientations.

Infrared (IR) spectroscopy remains an important tool for chemical characterization and identification. Chemprop-IR has been developed as a software package for the prediction of IR spectra through the use of machine learning. This work serves the dual purpose of providing a trained general-purpose model for the prediction of IR spectra with ease and providing the Chemprop-IR software framework for the training of new models. In Chemprop-IR, molecules are encoded using a directed message passing neural network, allowing for molecule latent representations to be learned and optimized for the task of spectral predictions. Model training incorporates spectra metrics and normalization techniques that offer better performance with spectral predictions than standard practice in regression models. The model makes use of pretraining using quantum chemistry calculations and ensembling of multiple submodels to improve generalizability and performance. The spectral predictions that result are of high quality, showing capability to capture the extreme diversity of spectral forms over chemical space and represent complex peak structures.