DFT-Based Stereochemical Rationales for the Bifunctional Brønsted Acid/Base-Catalyzed Diastereodivergent and Enantioselective aza-Henry Reactions of α-Nitro Esters.

A pair of chiral bis(amidine) [BAM] proton complexes provide reagent (catalyst)-controlled, highly diastereo- and enantioselective direct aza-Henry reactions leading to α-alkyl-substituted α,ß-diamino esters. A C2-symmetric ligand provides high anti-selectivity, while a nonsymmetric congener exhibits syn-selectivity in this example of diastereodivergent, enantioselective catalysis. A detailed computational analysis is reported for the first time, one that supports distinct models for selectivity resulting from the more hindered binding cavity of the C1-symmetric ligand. Binding in this congested pocket accommodates four hydrogen bond contacts among ligands and substrates, ultimately favoring a pre-syn arrangement highlighted by pyridinium-azomethine activation and quinolinium-nitronate activation. The complementary transition states reveal a wide range of alternatives. Comparing the C1- and C2-symmetric catalysts highlights distinct electrophile binding orientations despite their common hydrogen bond donor-acceptor features. Among the factors driving unusual high syn-diastereoselection are favorable dispersion forces that leverage the anthracenyl substituent of the C1-symmetric ligand.

Direct Optimization across Computer-Generated Reaction Networks Balances Materials Use and Feasibility of Synthesis Plans for Molecule Libraries.

The synthesis of thousands of candidate compounds in drug discovery and development offers opportunities for computer-aided synthesis planning to simplify the synthesis of molecule libraries by leveraging common starting materials and reaction conditions. We develop an optimization-based method to analyze large organic chemical reaction networks and design overlapping synthesis plans for entire molecule libraries so as to minimize the overall number of unique chemical compounds needed as either starting materials or reaction conditions. We consider multiple objectives, including the number of starting materials, the number of catalysts/solvents/reagents, and the likelihood of success of the overall syntheses plan, to select an optimal reaction network to access the target molecules. The library synthesis planning task was formulated as a network flow optimization problem, and we design an efficient decomposition scheme that reduces solution time by a factor of 5 and scales to instance with 48 target molecules and nearly 8000 intermediate reactions within hours. In four case studies of pharmaceutical compounds, the approach reduces the number of starting materials and catalysts/solvents/reagents needed by 32.2 and 66.0% on average and up to 63.2 and 80.0% in the best cases. The code implementation can be found at https://github.com/Coughy1991/Molecule_library_synthesis.

Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis.

Artificial intelligence and machine learning have demonstrated their potential role in predictive chemistry and synthetic planning of small molecules; there are at least a few reports of companies employing in silico synthetic planning into their overall approach to accessing target molecules. A data-driven synthesis planning program is one component being developed and evaluated by the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) consortium, comprising MIT and 13 chemical and pharmaceutical company members. Together, we wrote this perspective to share how we think predictive models can be integrated into medicinal chemistry synthesis workflows, how they are currently used within MLPDS member companies, and the outlook for this field.

Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors.

Accurate and rapid evaluation of whether substrates can undergo the desired the transformation is crucial and challenging for both human knowledge and computer predictions. Despite the potential of machine learning in predicting chemical reactivity such as selectivity, popular feature engineering and learning methods are either time-consuming or data-hungry. We introduce a new method that combines machine-learned reaction representation with selected quantum mechanical descriptors to predict regio-selectivity in general substitution reactions. We construct a reactivity descriptor database based on ab initio calculations of 130k organic molecules, and train a multi-task constrained model to calculate demanded descriptors on-the-fly. The proposed platform enhances the inter/extra-polated performance for regio-selectivity predictions and enables learning from small datasets with just hundreds of examples. Furthermore, the proposed protocol is demonstrated to be generally applicable to a diverse range of chemical spaces. For three general types of substitution reactions (aromatic C-H functionalization, aromatic C-X substitution, and other substitution reactions) curated from a commercial database, the fusion model achieves 89.7%, 96.7%, and 97.2% top-1 accuracy in predicting the major outcome, respectively, each using 5000 training reactions. Using predicted descriptors, the fusion model is end-to-end, and requires approximately only 70 ms per reaction to predict the selectivity from reaction SMILES strings.

Catalytic, Enantioselective Synthesis of Cyclic Carbamates from Dialkyl Amines by CO2-Capture: Discovery, Development, and Mechanism.

Cyclic carbamates are a common feature of small-molecule therapeutics, offering a constrained hydrogen bond acceptor that is both polar and sterically small. Methods for their preparation most often focus first on amino alcohol synthesis and then reaction with phosgene or its equivalent. This report describes an enantioselective synthesis of cyclic carbamates in which carbon dioxide engages an unsaturated basic amine, facilitated by a bifunctional organocatalyst designed to stabilize a carbamic acid intermediate while activating it toward subsequent enantioselective carbon-oxygen bond formation. Six-membered cyclic carbamates are prepared in good yield with high levels of enantioselection, as constrained 1,3-amino alcohols featuring a chiral tertiary alcohol carbon. Spectroscopic analysis (NMR, DOSY) of various substrate-reagent combinations provides insight into the dominant species under the reaction conditions. Two peculiar requirements were identified to achieve highest consistency: a "Goldilocks" amount of water and the use of a noncrystalline form of the ligand. These atypical features of the final protocol notwithstanding, a diverse range of products could be prepared. Their functionalizations illustrate the versatility of the carbamates as precursors to enantioenriched small molecules.

Using Machine Learning To Predict Suitable Conditions for Organic Reactions.

Reaction condition recommendation is an essential element for the realization of computer-assisted synthetic planning. Accurate suggestions of reaction conditions are required for experimental validation and can have a significant effect on the success or failure of an attempted transformation. However, de novo condition recommendation remains a challenging and under-explored problem and relies heavily on chemists' knowledge and experience. In this work, we develop a neural-network model to predict the chemical context (catalyst(s), solvent(s), reagent(s)), as well as the temperature most suitable for any particular organic reaction. Trained on ∼10 million examples from Reaxys, the model is able to propose conditions where a close match to the recorded catalyst, solvent, and reagent is found within the top-10 predictions 69.6% of the time, with top-10 accuracies for individual species reaching 80-90%. Temperature is accurately predicted within ±20 °C from the recorded temperature in 60-70% of test cases, with higher accuracy for cases with correct chemical context predictions. The utility of the model is illustrated through several examples spanning a range of common reaction classes. We also demonstrate that the model implicitly learns a continuous numerical embedding of solvent and reagent species that captures their functional similarity.

Enantioselective Organocatalytic Amine-Isocyanate Capture-Cyclization: Regioselective Alkene Iodoamination for the Synthesis of Chiral Cyclic Ureas.

Ureas of chiral diamines are prominent features of therapeutics, chiral auxiliaries, and intermediates in complex molecule synthesis. Although many methods for diamine synthesis are available, metal-free enantioselective alkene functionalizations to make protected 1,2- and 1,3-diamines from simple achiral starting materials are rare, and a single reagent that accesses a cross-section of each congener with high enantiomeric excess is not available. We describe a method to synthesize enantioenriched cyclic 5- and 6-membered ureas from allylic amines and an isocyanate using a C2-symmetric BisAmidine (BAM) catalyst that delivers N-selectivity from an ambident sulfonyl imide intermediate, overcoming electronic and steric deactivation at nitrogen. The geometry of 1,2-disubstituted alkenes is correlated to 5-exo and 6-endo cyclizations without altering alkene face selectivity, which is unexpectedly opposite that observed with O-nucleophiles. Straightforward product manipulations to diamine and imidazolidinone derivatives are underscored by the synthesis of an NK1 antagonist.

Enantioselective small molecule synthesis by carbon dioxide fixation using a dual Brønsted acid/base organocatalyst.

Carbon dioxide exhibits many of the qualities of an ideal reagent: it is nontoxic, plentiful, and inexpensive. Unlike other gaseous reagents, however, it has found limited use in enantioselective synthesis. Moreover, unprecedented is a tool that merges one of the simplest biological approaches to catalysis-Brønsted acid/base activation-with this abundant reagent. We describe a metal-free small molecule catalyst that achieves the three component reaction between a homoallylic alcohol, carbon dioxide, and an electrophilic source of iodine. Cyclic carbonates are formed enantioselectively.