Investigating and Quantifying Molecular Complexity Using Assembly Theory and Spectroscopy.

Current approaches to evaluate molecular complexity use algorithmic complexity, rooted in computer science, and thus are not experimentally measurable. Directly evaluating molecular complexity could be used to study directed vs undirected processes in the creation of molecules, with potential applications in drug discovery, the origin of life, and artificial life. Assembly theory has been developed to quantify the complexity of a molecule by finding the shortest path to construct the molecule from building blocks, revealing its molecular assembly index (MA). In this study, we present an approach to rapidly infer the MA of molecules from spectroscopic measurements. We demonstrate that the MA can be experimentally measured by using three independent techniques: nuclear magnetic resonance (NMR), tandem mass spectrometry (MS/MS), and infrared spectroscopy (IR). By identifying and analyzing the number of absorbances in IR spectra, carbon resonances in NMR, or molecular fragments in tandem MS, the MA of an unknown molecule can be reliably estimated. This represents the first experimentally quantifiable approach to determining molecular assembly. This paves the way to use experimental techniques to explore the evolution of complex molecules as well as a unique marker of where an evolutionary process has been operating.

An integrated self-optimizing programmable chemical synthesis and reaction engine.

Robotic platforms for chemistry are developing rapidly but most systems are not currently able to adapt to changing circumstances in real-time. We present a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, we demonstrate the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures. We also show how the use of in-line spectroscopy such as HPLC, Raman, and NMR can be used for closed-loop optimization of reactions, exemplified using Van Leusen oxazole synthesis, a four-component Ugi condensation and manganese-catalysed epoxidation reactions, as well as two previously unreported reactions, discovered from a selected chemical space, providing up to 50% yield improvement over 25-50 iterations. Finally, we demonstrate an experimental pipeline to explore a trifluoromethylations reaction space, that discovers new molecules.

Automated qualitative and quantitative analysis of complex forensic drug samples using 1 H NMR.

Progress in high-resolution nuclear magnetic resonance (NMR) instrumentation has enabled fast and accurate acquisition of quantitative 1 H NMR (qNMR) data, but analyzing complex forensic drug samples in the presence of significant peak overlap remains challenging. This limitation has hampered the adoption of 1 H NMR in areas such as traditional medicine and law enforcement. We present the NMRquant algorithm, which can detect and quantitate compounds of interest within forensic mixed drug samples even when there is overlap between chemical shift regions. Our algorithm is robust against variations in chemical shift resulting from temperature, concentration, and inter-analyte interactions. We have integrated these desirable features into an automated workflow, enabling routine unattended proton qNMR analysis of forensic drug samples.

Digitization and validation of a chemical synthesis literature database in the ChemPU.

Despite huge potential, automation of synthetic chemistry has only made incremental progress over the past few decades. We present an automatically executable chemical reaction database of 100 molecules representative of the range of reactions found in contemporary organic synthesis. These reactions include transition metal-catalyzed coupling reactions, heterocycle formations, functional group interconversions, and multicomponent reactions. The chemical reaction codes or χDLs for the reactions have been stored in a database for version control, validation, collaboration, and data mining. Of these syntheses, more than 50 entries from the database have been downloaded and robotically run in seven modular chemputers with yields and purities comparable to those achieved by an expert chemist. We also demonstrate the automatic purification of a range of compounds using a chromatography module seamlessly coupled to the platform and programmed with the same language.

Discovering New Chemistry with an Autonomous Robotic Platform Driven by a Reactivity-Seeking Neural Network.

We present a robotic chemical discovery system capable of navigating a chemical space based on a learned general association between molecular structures and reactivity, while incorporating a neural network model that can process data from online analytics and assess reactivity without knowing the identity of the reagents. Working in conjunction with this learned knowledge, our robotic platform is able to autonomously explore a large number of potential reactions and assess the reactivity of mixtures, including unknown chemical spaces, regardless of the identity of the starting materials. Through the system, we identified a range of chemical reactions and products, some of which were well-known, some new but predictable from known pathways, and some unpredictable reactions that yielded new molecules. The validation of the system was done within a budget of 15 inputs combined in 1018 reactions, further analysis of which allowed us to discover not only a new photochemical reaction but also a new reactivity mode for a well-known reagent (p-toluenesulfonylmethyl isocyanide, TosMIC). This involved the reaction of 6 equiv of TosMIC in a "multistep, single-substrate" cascade reaction yielding a trimeric product in high yield (47% unoptimized) with the formation of five new C-C bonds involving sp-sp2 and sp-sp3 carbon centers. An analysis reveals that this transformation is intrinsically unpredictable, demonstrating the possibility of a reactivity-first robotic discovery of unknown reaction methodologies without requiring human input.

Digitizing Chemistry Using the Chemical Processing Unit: From Synthesis to Discovery.

The digitization of chemistry is not simply about using machine learning or artificial intelligence systems to process chemical data, or about the development of ever more capable automation hardware; instead, it is the creation of a hard link between an abstracted process ontology of chemistry and bespoke hardware for performing reactions or exploring reactivity. Chemical digitization is therefore about the unambiguous development of an architecture, a chemical state machine, that uses this ontology to connect precise instruction sets to hardware that performs chemical transformations. This approach enables a universal standard for describing chemistry procedures via a chemical programming language and facilitates unambiguous dissemination of these procedures. We predict that this standard will revolutionize the ability of chemists to collaborate, increase reproducibility and safety, as we all as optimize for cost and efficiency. Most importantly, the digitization of chemistry will dramatically reduce the labor needed to make new compounds and broaden accessible chemical space. In recent years, the developments of automation in chemistry have gone beyond flow chemistry alone, with many bespoke workflows being developed not only for automating chemical synthesis but also for materials, nanomaterials, and formulation production. Indeed, the leap from fixed-configuration synthesis machines like peptide, nucleic acid, or dedicated cross-coupling engines is important for developing a truly universal approach to "dial-a-molecule". In this case, a key conceptual leap is the use of a batch system that can encode the chemical reagents, solvent, and products as packets which can be moved around the system, and a graph-based approach for the description of hardware modules that allows the compilation of chemical code that runs on, in principle, any hardware. Further, the integration of sensor systems for monitoring and controlling the state of the chemical synthesis machine, as well as high resolution spectroscopic tools, is vital if these systems are to facilitate closed-loop autonomous experiments. Systems that not only make molecules and materials, but also optimize their function, and use algorithms to assist with the development of new synthetic pathways and process optimization are also possible. Here, we discuss how the digitization of chemistry is happening, building on the plethora of technological developments in hardware and software. Importantly, digital-chemical robot systems need to integrate feedback from simple sensors, e.g., conductivity or temperature, as well as online analytics in order to navigate process space autonomously. This will open the door to accessing known molecules (synthesis), exploring whether known compounds/reactions are possible under new conditions (optimization), and searching chemical space for unknown and unexpected new molecules, reactions, and modes of reactivity (discovery). We will also discuss the role of chemical knowledge and how this can be used to challenge bias, as well as define and expand synthetically accessible chemical space using programmable robotic chemical state machines.

A universal system for digitization and automatic execution of the chemical synthesis literature.

Robotic systems for chemical synthesis are growing in popularity but can be difficult to run and maintain because of the lack of a standard operating system or capacity for direct access to the literature through natural language processing. Here we show an extendable chemical execution architecture that can be populated by automatically reading the literature, leading to a universal autonomous workflow. The robotic synthesis code can be corrected in natural language without any programming knowledge and, because of the standard, is hardware independent. This chemical code can then be combined with a graph describing the hardware modules and compiled into platform-specific, low-level robotic instructions for execution. We showcase automated syntheses of 12 compounds from the literature, including the analgesic lidocaine, the Dess-Martin periodinane oxidation reagent, and the fluorinating agent AlkylFluor.

Intuition-Enabled Machine Learning Beats the Competition When Joint Human-Robot Teams Perform Inorganic Chemical Experiments.

Traditionally, chemists have relied on years of training and accumulated experience in order to discover new molecules. But the space of possible molecules is so vast that only a limited exploration with the traditional methods can be ever possible. This means that many opportunities for the discovery of interesting phenomena have been missed, and in addition, the inherent variability of these phenomena can make them difficult to control and understand. The current state-of-the-art is moving toward the development of automated and eventually fully autonomous systems coupled with in-line analytics and decision-making algorithms. Yet even these, despite the substantial progress achieved recently, still cannot easily tackle large combinatorial spaces, as they are limited by the lack of high-quality data. Herein, we explore the utility of active learning methods for exploring the chemical space by comparing the collaboration between human experimenters with an algorithm-based search against their performance individually to probe the self-assembly and crystallization of the polyoxometalate cluster Na6[Mo120Ce6O366H12(H2O)78]·200H2O (1). We show that the robot-human teams are able to increase the prediction accuracy to 75.6 ± 1.8%, from 71.8 ± 0.3% with the algorithm alone and 66.3 ± 1.8% from only the human experimenters demonstrating that human-robot teams can beat robots or humans working alone.

Controlling Ligand Exchange through Macrocyclization.

Ligand exchange at a sterically hindered palladium center was investigated for six different ligands. The palladium atom was coordinated to a tridentate, NNN pincer bis(amido)pyridine macrocycle to produce a square-planar complex, in which an acetonitrile molecule occupies one of the coordination sites. Kinetic studies showed that ligand exchange at the palladium center proceeds through an associative mechanism and, as a consequence, is impeded by the small size of the metallomacrocycle cavity. The ligand-exchange rate on the palladium center between acetonitrile and six different ligands has been investigated and compared to the exchange rate on the corresponding open form. Our results demonstrate that macrocyclization of ligands is a way to modify the rate of guest exchange in a square-planar metal complex.

Tuning the tautomeric behavior of tris(salicylaldimines).

Five new tris(N-salicylaldimine) (TSAN) analogues were prepared and characterized. NMR and single-crystal X-ray diffraction studies showed that they are found in different tautomeric forms, ranging from keto-enamine to enol-imine, with two showing intermediate behavior. We present a simple structural model governing the relative stability of the keto-enamine versus enol-imine tautomeric form of TSANs, based on experimental and theoretical findings on the new and existing TSAN analogues. Examination of electron delocalization throughout this range reveals a connection between tautomeric state and whether the substituent is σ or π electron withdrawing/donating. This can be used as a qualitative guide to design TSANs with controlled tautomeric behavior. These results will be helpful to the growing number of researchers in supramolecular chemistry who use TSANs to construct new materials and cages.

Formylation of phenols using formamidine acetate.

We report a new method to formylate phenol derivatives using formamidine acetate and acetic anhydride. This general-purpose transformation is a significant improvement over many other methods and does not require high temperatures or the addition of strong acid or base. Mono-, di-, and tri-formylated product can be obtained, depending on the substrate and conditions used.

Stabilization of a Strained Heteroradialene by Peripheral Electron Delocalization.

Dimethylamine and 2,4,6-triformylphloroglucinol react to form a product with a highly contorted nonplanar geometry due to favorable electron delocalization. This new heteroradialene compound has been studied by X-ray diffraction, variable-temperature multinuclear NMR spectroscopy, IR spectroscopy, UV-vis spectroscopy, and ab initio calculations. Electron delocalization throughout the periphery of the central ring leads to a structure that retains very little of the aromatic characteristics of the starting material.

Deuteration of Aromatic Rings under Very Mild Conditions through Keto-Enamine Tautomeric Amplification.

We have discovered a surprising, mild method for deuteration of select aromatic compounds that is facilitated by a keto-enamine tautomeric intermediate. The mechanism of the reaction has been studied using kinetics experiments and detailed computational analysis. It was found that a chain of water molecules has a substantial role in lowering the activation barrier to the tautomerization-enhanced deuteration reaction. Our results demonstrate that tautomeric forms of aromatic molecules can be exploited to bring about enhanced reactivity.

Novel PPV/mesoporous organosilica composites: influence of the host chirality on a conjugated polymer guest.

The conjugated polymer poly(p-phenylenevinylene) (PPV) was polymerized in the pores of chiral nematic mesoporous organosilica to give a composite film showing the strong characteristic fluorescence of PPV as well as the iridescence due to the photonic band gap of the host material. Detailed circular dichroism (CD) studies reveal a chiral structure of the polymer within the pores. These new fluorescent materials undergo fluorescence quenching upon exposure to electron deficient aromatics such as 2,4,6-trinitrotoluene (TNT), indicating that they may be useful for developing chemical sensors.

Role of entropy and autosolvation in dimerization and complexation of C60 by Zn7 metallocavitands.

The supramolecular chemistry of bowl-shaped heptazinc metallocavitands templated by Schiff base macrocycles has been investigated. Dimerization thermodynamics were probed by (1)H NMR spectroscopy in benzene-d(6), toluene-d(8), and p-xylene-d(10) and revealed the process to be entropy-driven and enthalpy-opposed in each solvent. Trends in the experimentally determined enthalpy and entropy values are related to the thermodynamics of solvent autosolvation, solvent molecules being released from the monomeric metallocavitand cavity into the bulk solvent upon dimerization. The relationship established between experimentally measured dimerization thermodynamics and autosolvation data successfully predicts the absence of dimerization in CH(2)Cl(2) and CHCl(3) and was used to estimate the number of solvent molecules interacting with the monomeric metallocavitand in solution. Host-guest interactions between heptazinc metallocavitands and fullerene C(60) have also been investigated. Interestingly, metallocavitand-C(60) interactions are only observed in solvents that facilitate entropy-driven dimerization suggesting entropy and solvent autosolvation may be important in explaining concave-convex interactions.