HSQC Spectra Simulation and Matching for Molecular Identification.

In the pursuit of improved compound identification and database search tasks, this study explores heteronuclear single quantum coherence (HSQC) spectra simulation and matching methodologies. HSQC spectra serve as unique molecular fingerprints, enabling a valuable balance of data collection time and information richness. We conducted a comprehensive evaluation of the following four HSQC simulation techniques: ACD/Labs (ACD), MestReNova (MNova), Gaussian NMR calculations (DFT), and a graph-based neural network (ML). For the latter two techniques, we developed a reconstruction logic to combine proton and carbon 1D spectra into HSQC spectra. The methodology involved the implementation of three peak-matching strategies (minimum-sum, Euclidean-distance, and Hungarian distance) combined with three padding strategies (zero-padding, peak-truncated, and nearest-neighbor double assignment). We found that coupling these strategies with a robust simulation technique facilitates the accurate identification of correct molecules from similar analogues (regio- and stereoisomers) and allows for fast and accurate large database searches. Furthermore, we demonstrated the efficacy of the best-performing methodology by rectifying the structures of a set of previously misidentified molecules. This research indicates that effective HSQC spectral simulation and matching methodologies significantly facilitate molecular structure elucidation. Furthermore, we offer a Google Colab notebook for researchers to use our methods on their own data (https://github.com/AstraZeneca/hsqc_structure_elucidation.git).

Organic reactivity from mechanism to machine learning.

As more data are introduced in the building of models of chemical reactivity, the mechanistic component can be reduced until 'big data' applications are reached. These methods no longer depend on underlying mechanistic hypotheses, potentially learning them implicitly through extensive data training. Reactivity models often focus on reaction barriers, but can also be trained to directly predict lab-relevant properties, such as yields or conditions. Calculations with a quantum-mechanical component are still preferred for quantitative predictions of reactivity. Although big data applications tend to be more qualitative, they have the advantage to be broadly applied to different kinds of reactions. There is a continuum of methods in between these extremes, such as methods that use quantum-derived data or descriptors in machine learning models. Here, we present an overview of the recent machine learning applications in the field of chemical reactivity from a mechanistic perspective. Starting with a summary of how reactivity questions are addressed by quantum-mechanical methods, we discuss methods that augment or replace quantum-based modelling with faster alternatives relying on machine learning.

Can easy chemistry produce complex, diverse, and novel molecules?

Easy-to-make compounds are often perceived to be inferior compared with molecules obtained through elaborate reaction schemes. This study evaluates in depths whether this perception is true.

Relative Strength of Common Directing Groups in Palladium-Catalyzed Aromatic C-H Activation.

Efficient functionalization of C-H bonds can be achieved using transition metal catalysts, such as Pd(OAc)2. To better control the regioselectivity in these reactions, some functional groups on the substrate may be used as directing groups, guiding the reactivity to an ortho position. Herein, we describe a methodology to score the relative strength of such directing groups in palladium-catalyzed aromatic C-H activation. The results have been collected into a scale that serves to predict the regioselectivity on molecules with multiple competing directing groups. We demonstrate that this scale yields accurate predictions on over a hundred examples, taken from the literature. In addition to the regioselectivity prediction on complex molecules, the knowledge of the relative strengths of directing groups can also be used to work with new combinations of functionalities, exploring uncharted chemical space.

A Predictive Tool for Electrophilic Aromatic Substitutions Using Machine Learning.

At the early stages of the drug development process, thousands of compounds are synthesized in order to attain the best possible potency and pharmacokinetic properties. Once successful scaffolds are identified, large libraries of analogues are made, which is a challenging and time-consuming task. Recently, late stage functionalization (LSF) has become increasingly prominent since these reactions selectively functionalize C-H bonds, allowing to quickly produce analogues. Classical electrophilic aromatic halogenations are a powerful type of reaction in the LSF toolkit. However, the introduction of an electrophile in a regioselective manner on a drug-like molecule is a challenging task. Herein we present a machine learning model able to predict the reactive site of an electrophilic aromatic substitution with an accuracy of 93% (internal validation set). The model takes as input a SMILES of a compound and uses six quantum mechanics descriptors to identify its reactive site(s). On an external validation set, 90% of all molecules were correctly predicted.

Application of Q2MM to predictions in stereoselective synthesis.

Quantum-Guided Molecular Mechanics (Q2MM) can be used to derive transition state force fields (TSFFs) that allow the fast and accurate predictions of stereoselectivity for a wide range of catalytic enantioselective reactions. The basic ideas behind the derivation of TSFFs using Q2MM are discussed and the steps involved in obtaining a TSFF using the Q2MM code, publically available at github.com/q2mm, are shown. The applicability for a range of reactions, including several non-standard applications of Q2MM, is demonstrated. Future developments of the method are also discussed.

Revised Mechanism for a Ruthenium-Catalyzed Coupling of Aldehyde and Terminal Alkyne.

Ruthenium catalysts have been found to be of great use for many kinds of reactions. Understanding the details of the catalytic cycle allows to not only rationalize experimental results but also to improve upon reactions. Herein, we present a detailed computational study of a ruthenium-catalyzed coupling between a terminal alkyne and an aldehyde. The reaction under examination facilitates novel access to olefins with the concurrent loss of a single carbon as carbon monoxide. The reaction was first developed in 2009, but the tentative mechanism initially proposed was proven to be contradictory to some experimental data obtained since then. Using a combination of computational investigations and isotope-labeling experiments, several potential mechanisms have been studied. In contrast to the [2+2] cycloaddition mechanism suggested for similar catalysts, we propose a new consensus pathway that proceeds through the formation of a ruthenium-vinylidene complex that undergoes an aldol-type reaction with the aldehyde to yield the product olefins. Computational insights into the influence of different reagents used to optimize reaction conditions and the intricacies of decarbonylation of a Ru-CO complex affecting catalyst turnover are highlighted.

Fluoride-Mediated Desulfonylative Intramolecular Cyclization to Fused and Bridged Bicyclic Compounds: A Complex Mechanism.

We previously reported the synthesis of polysubstituted chiral oxazepanes in three steps from commercially available starting materials. The unexpected reaction of one of these 1,4-oxazepanes in the presence of TBAF provided a 4-oxa-1-azabicyclo[4.1.0]heptane core. This unusual process significantly increased the complexity of the molecular scaffold by introducing a bicyclic core. Surprisingly, the generated bicyclic structure featuring three stereocenters was a mixture of enantiomers with no other diastereomers observed. These striking experimental observations deserved further investigations. A combination of experimental and computational investigations unveiled a complex diastereoselective mechanism. Mechanistic rationale is presented for this observed rearrangement.

Medicinal Chemistry Projects Requiring Imaginative Structure-Based Drug Design Methods.

Computational methods for docking small molecules to proteins are prominent in drug discovery. There are hundreds, if not thousands, of documented examples-and several pertinent cases within our research program. Fifteen years ago, our first docking-guided drug design project yielded nanomolar metalloproteinase inhibitors and illustrated the potential of structure-based drug design. Subsequent applications of docking programs to the design of integrin antagonists, BACE-1 inhibitors, and aminoglycosides binding to bacterial RNA demonstrated that available docking programs needed significant improvement. At that time, docking programs primarily considered flexible ligands and rigid proteins. We demonstrated that accounting for protein flexibility, employing displaceable water molecules, and using ligand-based pharmacophores improved the docking accuracy of existing methods-enabling the design of bioactive molecules. The success prompted the development of our own program, Fitted, implementing all of these aspects. The primary motivation has always been to respond to the needs of drug design studies; the majority of the concepts behind the evolution of Fitted are rooted in medicinal chemistry projects and collaborations. Several examples follow: (1) Searching for HDAC inhibitors led us to develop methods considering drug-zinc coordination and its effect on the pKa of surrounding residues. (2) Targeting covalent prolyl oligopeptidase (POP) inhibitors prompted an update to Fitted to identify reactive groups and form bonds with a given residue (e.g., a catalytic residue) when the geometry allows it. Fitted-the first fully automated covalent docking program-was successfully applied to the discovery of four new classes of covalent POP inhibitors. As a result, efficient stereoselective syntheses of a few screening hits were prioritized rather than synthesizing large chemical libraries-yielding nanomolar inhibitors. (3) In order to study the metabolism of POP inhibitors by cytochrome P450 enzymes (CYPs)-for toxicology studies-the program Impacts was derived from Fitted and helped us to reveal a complex metabolism with unforeseen stereocenter isomerizations. These efforts, combined with those of other docking software developers, have strengthened our understanding of the complex drug-protein binding process while providing the medicinal chemistry community with useful tools that have led to drug discoveries. In this Account, we describe our contributions over the past 15 years-within their historical context-to the design of drug candidates, including BACE-1 inhibitors, POP covalent inhibitors, G-quadruplex binders, and aminoglycosides binding to nucleic acids. We also remark the necessary developments of docking programs, specifically Fitted, that enabled structure-based design to flourish and yielded multiple fruitful, rational medicinal chemistry campaigns.

Elucidating Hyperconjugation from Electronegativity to Predict Drug Conformational Energy in a High Throughput Manner.

Computational chemists use structure-based drug design and molecular dynamics of drug/protein complexes which require an accurate description of the conformational space of drugs. Organic chemists use qualitative chemical principles such as the effect of electronegativity on hyperconjugation, the impact of steric clashes on stereochemical outcome of reactions, and the consequence of resonance on the shape of molecules to rationalize experimental observations. While computational chemists speak about electron densities and molecular orbitals, organic chemists speak about partial charges and localized molecular orbitals. Attempts to reconcile these two parallel approaches such as programs for natural bond orbitals and intrinsic atomic orbitals computing Lewis structures-like orbitals and reaction mechanism have appeared. In the past, we have shown that encoding and quantifying chemistry knowledge and qualitative principles can lead to predictive methods. In the same vein, we thought to understand the conformational behaviors of molecules and to encode this knowledge back into a molecular mechanics tool computing conformational potential energy and to develop an alternative to atom types and training of force fields on large sets of molecules. Herein, we describe a conceptually new approach to model torsion energies based on fundamental chemistry principles. To demonstrate our approach, torsional energy parameters were derived on-the-fly from atomic properties. When the torsional energy terms implemented in GAFF, Parm@Frosst, and MMFF94 were substituted by our method, the accuracy of these force fields to reproduce MP2-derived torsional energy profiles and their transferability to a variety of functional groups and drug fragments were overall improved. In addition, our method did not rely on atom types and consequently did not suffer from poor automated atom type assignments.

Azo···phenyl stacking: a persistent self-assembly motif guides the assembly of fluorinated cis-azobenzenes into photo-mechanical needle crystals.

We describe a novel, persistent motif of molecular assembly in photo-mechanical crystals and cocrystals of fluorinated cis-azobenzenes. The azo···phenyl stacking, preserved upon either chemical substitution or halogen-bonded cocrystallization, guides the assembly of fluorinated cis-azobenzenes into columnar stacks and drives the formation of crystals with needle-like morphologies optimal for photo-mechanical motion.

Understanding P450-mediated Bio-transformations into Epoxide and Phenolic Metabolites.

Adverse drug reactions are commonly the result of cytochrome P450 enzymes (CYPs) converting the drugs into reactive metabolites. Thus, information about the CYP bioactivation of drugs would not only provide insight into metabolic stability, but also into the potential toxicity. For example, oxidation of phenyl rings may lead to either toxic epoxides or safer phenols. Herein, we demonstrate that the potential to form reactive metabolites is encoded primarily in the properties of the molecule to be oxidized. While the enzyme positions the molecule inside the binding pocket (selects the site of metabolism), the subsequent reaction is only dependent on the substrate itself. To test this hypothesis, we used this observation as a predictor of drug inherent toxicity. This approach was used to successfully identify the formation of reactive metabolites in over 100 drug molecules. These results provide a new perspective on the impact of functional groups on aromatic oxidation of drugs and their effects on toxicity.

Docking ligands into flexible and solvated macromolecules. 7. Impact of protein flexibility and water molecules on docking-based virtual screening accuracy.

The use of predictive computational methods in the drug discovery process is in a state of continual growth. Over the last two decades, an increasingly large number of docking tools have been developed to identify hits or optimize lead molecules through in-silico screening of chemical libraries to proteins. In recent years, the focus has been on implementing protein flexibility and water molecules. Our efforts led to the development of Fitted first reported in 2007 and further developed since then. In this study, we wished to evaluate the impact of protein flexibility and occurrence of water molecules on the accuracy of the Fitted docking program to discriminate active compounds from inactive compounds in virtual screening (VS) campaigns. For this purpose, a total of 171 proteins cocrystallized with small molecules representing 40 unique enzymes and receptors as well as sets of known ligands and decoys were selected from the Protein Data Bank (PDB) and the Directory of Useful Decoys (DUD), respectively. This study revealed that implementing displaceable crystallographic or computationally placed particle water molecules and protein flexibility can improve the enrichment in active compounds. In addition, an informed decision based on library diversity or research objectives (hit discovery vs lead optimization) on which implementation to use may lead to significant improvements.

Multicomponent diversity-oriented synthesis of aza-lysine-peptide mimics.

Copper catalyzed coupling of Mannich reagents to aza-propargylglycine residues has been employed to synthesize constrained aza-lysine peptides. Employing growth hormone releasing peptide-6 (GHRP-6) as a model peptide and a variety of secondary amines, 18 aza-Lys analogs were synthesized by this so-called A(3)-coupling reaction. This effective method for making constrained aza-Lys-peptides offers strong potential for exploring various recognition events implicating lysine residues including post-translational peptide modification.

Shaping crystals with light: crystal-to-crystal isomerization and photomechanical effect in fluorinated azobenzenes.

Unusually long thermal half-lives of perhalogenated cis-azobenzenes enabled their structural characterization and the first evidence of a crystal-to-crystal cis → trans azobenzene isomerization. Irradiation with visible light transforms a perhalogenated cis-azobenzene single crystal into a polycrystalline aggregate of its trans-isomer in a photomechanical transformation that involves a significant, controllable, and thermally irreversible change of crystal shape. This is the first demonstration of permanent photomechanical modification of crystal shape in an azobenzene.