Similarity based functionalization for enumeration of synthetically plausible chemical libraries surrounding a target.

Functionalization of lead compounds to create analogs is a challenging step in discovering new molecules with desired properties and it is conducted throughout the chemical industry, including pharmaceuticals and agrochemicals. The process can be time-consuming and expensive, requiring expert intuition and experience. To help address synthesis planning challenges in late-stage functionalization, we have developed a molecular similarity approach that proposes single-step functionalization reactions based on analogy to precedent reactions. The developed approach mimics reaction strategies and suggests co-reactants defined implicitly by a corpus of known reactions. Using ca. 348 k reactions from the patent literature as a knowledge base, the recorded products or close analogs are among the top 20 proposed products in 74% of ∼44 k test reactions. The combinatorial growth inherent in recursive applications of the tool allows the enumeration of chemical libraries surrounding a target compound of interest. Moreover, each step of the resulting library synthesis leverages common chemical transformations reported in the literature accessible to most chemists.

Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients.

Reaction optimization and characterization depend on reliable measures of reaction yield, often measured by high-performance liquid chromatography (HPLC). Peak areas in HPLC chromatograms are correlated to analyte concentrations by way of calibration standards, typically pure samples of known concentration. Preparing the pure material required for calibration runs can be tedious for low-yielding reactions and technically challenging at small reaction scales. Herein, we present a method to quantify the yield of reactions by HPLC without needing to isolate the product(s) by combining a machine learning model for molar extinction coefficient estimation, and both UV-vis absorption and mass spectra. We demonstrate the method for a variety of reactions important in medicinal and process chemistry, including amide couplings, palladium catalyzed cross-couplings, nucleophilic aromatic substitutions, aminations, and heterocycle syntheses. The reactions were all performed using an automated synthesis and isolation platform. Calibration-free methods such as the presented approach are necessary for such automated platforms to be able to discover, characterize, and optimize reactions automatically.

Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back.

A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. We demonstrated two case studies on dye-like molecules, targeting absorption wavelength, lipophilicity, and photooxidative stability. In the first study, the platform experimentally realized 294 unreported molecules across three automatic iterations of molecular design-make-test-analyze cycles while exploring the structure-function space of four rarely reported scaffolds. In each iteration, the property prediction models that guided exploration learned the structure-property space of diverse scaffold derivatives, which were realized with multistep syntheses and a variety of reactions. The second study exploited property models trained on the explored chemical space and previously reported molecules to discover nine top-performing molecules within a lightly explored structure-property space.

Heterogeneous photochemical reaction enabled by an ultrasonic microreactor.

The presence of solids as starting reagents/reactants or products in flow photochemical reactions can lead to reactor clogging and yield reduction from side reactions. We address this limitation with a new ultrasonic microreactor for continuous solid-laden photochemical reactions. The ultrasonic photochemical microreactor is characterized by the liquid and solid residence time distribution (RTD) and the absorbed photon flux in the reactor via chemical actinometry. The solid-handling capability of the ultrasonic photochemical microreactor is demonstrated with a silyl radical-mediated metallaphotoredox cross-electrophile coupling with a solid base as a reagent.

Parallel multi-droplet platform for reaction kinetics and optimization.

We present an automated droplet reactor platform possessing parallel reactor channels and a scheduling algorithm that orchestrates all of the parallel hardware operations and ensures droplet integrity as well as overall efficiency. We design and incorporate all of the necessary hardware and software to enable the platform to be used to study both thermal and photochemical reactions. We incorporate a Bayesian optimization algorithm into the control software to enable reaction optimization over both categorical and continuous variables. We demonstrate the capabilities of both the preliminary single-channel and parallelized versions of the platform using a series of model thermal and photochemical reactions. We conduct a series of reaction optimization campaigns and demonstrate rapid acquisition of the data necessary to determine reaction kinetics. The platform is flexible in terms of use case: it can be used either to investigate reaction kinetics or to perform reaction optimization over a wide range of chemical domains.

Computer-assisted multistep chemoenzymatic retrosynthesis using a chemical synthesis planner.

Chemoenzymatic synthesis methods use organic and enzyme chemistry to synthesize a desired small molecule. Complementing organic synthesis with enzyme-catalyzed selective transformations under mild conditions enables more sustainable and synthetically efficient chemical manufacturing. Here, we present a multistep retrosynthesis search algorithm to facilitate chemoenzymatic synthesis of pharmaceutical compounds, specialty chemicals, commodity chemicals, and monomers. First, we employ the synthesis planner ASKCOS to plan multistep syntheses starting from commercially available materials. Then, we identify transformations that can be catalyzed by enzymes using a small database of biocatalytic reaction rules previously curated for RetroBioCat, a computer-aided synthesis planning tool for biocatalytic cascades. Enzymatic suggestions captured by the approach include ones capable of reducing the number of synthetic steps. We successfully plan chemoenzymatic routes for active pharmaceutical ingredients or their intermediates (e.g., Sitagliptin, Rivastigmine, and Ephedrine), commodity chemicals (e.g., acrylamide and glycolic acid), and specialty chemicals (e.g., S-Metalochlor and Vanillin), in a retrospective fashion. In addition to recovering published routes, the algorithm proposes many sensible alternative pathways. Our approach provides a chemoenzymatic synthesis planning strategy by identifying synthetic transformations that could be candidates for enzyme catalysis.

Community Resource for Innovation in Polymer Technology (CRIPT): A Scalable Polymer Material Data Structure.

The Community Resource for Innovation in Polymer Technology (CRIPT) data model is designed to address the high complexity in defining a polymer structure and the intricacies involved with characterizing material properties.

Open-Source Chromatographic Data Analysis for Reaction Optimization and Screening.

Automation and digitalization solutions in the field of small molecule synthesis face new challenges for chemical reaction analysis, especially in the field of high-performance liquid chromatography (HPLC). Chromatographic data remains locked in vendors' hardware and software components, limiting their potential in automated workflows and data science applications. In this work, we present an open-source Python project called MOCCA for the analysis of HPLC-DAD (photodiode array detector) raw data. MOCCA provides a comprehensive set of data analysis features, including an automated peak deconvolution routine of known signals, even if overlapped with signals of unexpected impurities or side products. We highlight the broad applicability of MOCCA in four studies: (i) a simulation study to validate MOCCA's data analysis features; (ii) a reaction kinetics study on a Knoevenagel condensation reaction demonstrating MOCCA's peak deconvolution feature; (iii) a closed-loop optimization study for the alkylation of 2-pyridone without human control during data analysis; (iv) a well plate screening of categorical reaction parameters for a novel palladium-catalyzed cyanation of aryl halides employing O-protected cyanohydrins. By publishing MOCCA as a Python package with this work, we envision an open-source community project for chromatographic data analysis with the potential of further advancing its scope and capabilities.

Machine-Learning-Guided Discovery of Electrochemical Reactions.

The molecular structures synthesizable by organic chemists dictate the molecular functions they can create. The invention and development of chemical reactions are thus critical for chemists to access new and desirable functional molecules in all disciplines of organic chemistry. This work seeks to expedite the exploration of emerging areas of organic chemistry by devising a machine-learning-guided workflow for reaction discovery. Specifically, this study uses machine learning to predict competent electrochemical reactions. To this end, we first develop a molecular representation that enables the production of general models with limited training data. Next, we employ automated experimentation to test a large number of electrochemical reactions. These reactions are categorized as competent or incompetent mixtures, and a classification model was trained to predict reaction competency. This model is used to screen 38,865 potential reactions in silico, and the predictions are used to identify a number of reactions of synthetic or mechanistic interest, 80% of which are found to be competent. Additionally, we provide the predictions for the 38,865-member set in the hope of accelerating the development of this field. We envision that adopting a workflow such as this could enable the rapid development of many fields of chemistry.

Building Chemical Property Models for Energetic Materials from Small Datasets Using a Transfer Learning Approach.

For many experimentally measured chemical properties that cannot be directly computed from first-principles, the existing physics-based models do not extrapolate well to out-of-sample molecules, and experimental datasets themselves are too small for traditional machine learning (ML) approaches. To overcome these limitations, we apply a transfer learning approach, whereby we simultaneously train a multi-target regression model on a small number of molecules with experimentally measured values and a large number of molecules with related computed properties. We demonstrate this methodology on predicting the experimentally measured impact sensitivity of energetic crystals, finding that both characteristics of the computed dataset and model architecture are important to prediction accuracy of the small experimental dataset. Our directed-message passing neural network (D-MPNN) ML model using transfer learning outperforms direct-ML and physics-based models on a diverse test set, and the new methods described here are widely applicable to modeling many other structure-property relationships.