Computational prediction of complex cationic rearrangement outcomes.

Recent years have seen revived interest in computer-assisted organic synthesis1,2. The use of reaction- and neural-network algorithms that can plan multistep synthetic pathways have revolutionized this field1,3-7, including examples leading to advanced natural products6,7. Such methods typically operate on full, literature-derived 'substrate(s)-to-product' reaction rules and cannot be easily extended to the analysis of reaction mechanisms. Here we show that computers equipped with a comprehensive knowledge-base of mechanistic steps augmented by physical-organic chemistry rules, as well as quantum mechanical and kinetic calculations, can use a reaction-network approach to analyse the mechanisms of some of the most complex organic transformations: namely, cationic rearrangements. Such rearrangements are a cornerstone of organic chemistry textbooks and entail notable changes in the molecule's carbon skeleton8-12. The algorithm we describe and deploy at https://HopCat.allchemy.net/ generates, within minutes, networks of possible mechanistic steps, traces plausible step sequences and calculates expected product distributions. We validate this algorithm by three sets of experiments whose analysis would probably prove challenging even to highly trained chemists: (1) predicting the outcomes of tail-to-head terpene (THT) cyclizations in which substantially different outcomes are encoded in modular precursors differing in minute structural details; (2) comparing the outcome of THT cyclizations in solution or in a supramolecular capsule; and (3) analysing complex reaction mixtures. Our results support a vision in which computers no longer just manipulate known reaction types1-7 but will help rationalize and discover new, mechanistically complex transformations.

Computer-designed repurposing of chemical wastes into drugs.

As the chemical industry continues to produce considerable quantities of waste chemicals1,2, it is essential to devise 'circular chemistry'3-8 schemes to productively back-convert at least a portion of these unwanted materials into useful products. Despite substantial progress in the degradation of some classes of harmful chemicals9, work on 'closing the circle'-transforming waste substrates into valuable products-remains fragmented and focused on well known areas10-15. Comprehensive analyses of which valuable products are synthesizable from diverse chemical wastes are difficult because even small sets of waste substrates can, within few steps, generate millions of putative products, each synthesizable by multiple routes forming densely connected networks. Tracing all such syntheses and selecting those that also meet criteria of process and 'green' chemistries is, arguably, beyond the cognition of human chemists. Here we show how computers equipped with broad synthetic knowledge can help address this challenge. Using the forward-synthesis Allchemy platform16, we generate giant synthetic networks emanating from approximately 200 waste chemicals recycled on commercial scales, retrieve from these networks tens of thousands of routes leading to approximately 300 important drugs and agrochemicals, and algorithmically rank these syntheses according to the accepted metrics of sustainable chemistry17-19. Several of these routes we validate by experiment, including an industrially realistic demonstration on a 'pharmacy on demand' flow-chemistry platform20. Wide adoption of computerized waste-to-valuable algorithms can accelerate productive reuse of chemicals that would otherwise incur storage or disposal costs, or even pose environmental hazards.

Scaffold-Directed Face Selectivity Machine-Learned from Vectors of Non-covalent Interactions.

This work describes a method to vectorize and Machine-Learn, ML, non-covalent interactions responsible for scaffold-directed reactions important in synthetic chemistry. Models trained on this representation predict correct face of approach in ca. 90 % of Michael additions or Diels-Alder cycloadditions. These accuracies are significantly higher than those based on traditional ML descriptors, energetic calculations, or intuition of experienced synthetic chemists. Our results also emphasize the importance of ML models being provided with relevant mechanistic knowledge; without such knowledge, these models cannot easily "transfer-learn" and extrapolate to previously unseen reaction mechanisms.

Catalytic Fields as a Tool to Analyze Enzyme Reaction Mechanism Variants and Reaction Steps.

Catalytic fields representing the topology of the optimal molecular environment charge distribution that reduces the activation barrier have been used to examine alternative reaction variants and to determine the role of conserved catalytic residues for two consecutive reactions catalyzed by the same enzyme. Until now, most experimental and conventional top-down theoretical studies employing QM/MM or ONIOM methods have focused on the role of enzyme electric fields acting on broken bonds of reactants. In contrast, our bottom-up approach dealing with a small reactant and transition-state model allows the analysis of the opposite effects: how the catalytic field resulting from the charge redistribution during the enzyme reaction acts on conserved amino acid residues and contributes to the reduction of the activation barrier. This approach has been applied to the family of histidyl tRNA synthetases involved in the translation of the genetic code into the protein amino acid sequence. Activation energy changes related to conserved charged amino acid residues for 12 histidyl tRNA synthetases from different biological species allowed to compare on equal footing the catalytic residues involved in ATP aminoacylation and tRNA charging reactions and to analyze different reaction mechanisms proposed in the literature. A scan of the library of atomic multipoles for amino acid side-chain rotamers within the catalytic field pointed out the change in the Glu83 conformation as the critical catalytic effect, providing, at low computational cost, insight into the electrostatic preorganization of the enzyme catalytic site at a level of detail that has not yet been accessible in conventional experimental or theoretical methods. This opens the way for rational reverse biocatalyst design at a very limited computational cost without resorting to empirical methods.

Computer-generated "synthetic contingency" plans at times of logistics and supply problems: scenarios for hydroxychloroquine and remdesivir.

A computer program for retrosynthetic planning helps develop multiple "synthetic contingency" plans for hydroxychloroquine and also routes leading to remdesivir, both promising but yet unproven medications against COVID-19. These plans are designed to navigate, as much as possible, around known and patented routes and to commence from inexpensive and diverse starting materials, so as to ensure supply in case of anticipated market shortages of commonly used substrates. Looking beyond the current COVID-19 pandemic, development of similar contingency syntheses is advocated for other already-approved medications, in case such medications become urgently needed in mass quantities to face other public-health emergencies.