Towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learning

Predicting organic reaction feasibility and robustness against environmental factors is challenging. We address this issue by integrating high throughput experimentation (HTE) and Bayesian deep learning. Diverging from existing HTE studies focused on niche chemical spaces, in this work, our in-house HTE platform conducted 11,669 distinct acid amine coupling reactions in 156 working hours, yielding the most extensive single HTE dataset at a volumetric scale for industrial delivery. Our Bayesian neural network model achieved a benchmark for prediction accuracy of 89.48% for reaction feasibility. Furthermore, our fine-grained uncertainty disentanglement enables efficient active learning, reducing 80% of data requirements. Additionally, our uncertainty analysis effectively identifies out-of-domain reactions and evaluates reaction robustness or reproducibility against environmental factors for scaling up, offering a practical framework for navigating chemical spaces and designing highly robust industrial processes.

Exploring BERT for Reaction Yield Prediction: Evaluating the Impact of Tokenization, Molecular Representation, and Pretraining Data Augmentation

Predicting reaction yields in synthetic chemistry remains a significant challenge. This study systematically evaluates the impact of tokenization, molecular representation, pretraining data, and adversarial training on a BERT-based model for yield prediction of Buchwald-Hartwig and Suzuki-Miyaura coupling reactions using publicly available HTE data sets. We demonstrate that molecular representation choice (SMILES, DeepSMILES, SELFIES, Morgan fingerprint-based notation, IUPAC names) has minimal impact on model performance, while typically BPE and SentencePiece tokenization outperform other methods. WordPiece is strongly discouraged for SELFIES and fingerprint-based notation. Furthermore, pretraining with relatively small data sets (<100 K reactions) achieves comparable performance to larger data sets containing millions of examples. The use of artificially generated domain-specific pretraining data is proposed. The artificially generated sets prove to be a good surrogate for the reaction schemes extracted from reaction data sets such as Pistachio or Reaxys. The best performance was observed for hybrid pretraining sets combining the real and the domain-specific, artificial data. Finally, we show that a novel adversarial training approach, perturbing input embeddings dynamically, improves model robustness and generalizability for yield and reaction success prediction. These findings provide valuable insights for developing robust and practical machine learning models for yield prediction in synthetic chemistry. GSK's BERT training code base is made available to the community with this work.

High-throughput microdroplet-based synthesis using automated array-to-array transfer

Automation of chemical synthesis and high-throughput (HT) screening are important for speeding up drug discovery. Here, we describe an automated HT picomole scale synthesis system which uses desorption electrospray ionization (DESI) to create microdroplets of reaction mixtures at individual positions from a two-dimensional reactant array and transfer them to a corresponding position in an array of collected reaction products. On-the-fly chemical transformations are facilitated by the reaction acceleration phenomenon in microdroplets and high reaction conversions are achieved during the milliseconds droplet flight time from the reactant to the product array. Successful functionalization of bioactive molecules is demonstrated through the generation of 172 analogs (64% success rate) using multiple reaction types. Synthesis throughput is ∼45 seconds/reaction including droplet formation, reaction, and collection steps, all of which occur in an integrated fashion, generating product amounts sufficient for subsequent bioactivity screening (low ng to low μg). Quantitative performance was validated using LC/MS. This system bridges the demonstrated capabilities of HT-DESI for reaction screening and label-free bioassays, allowing consolidation of the key early drug discovery steps around a single synthetic-analytical technology.

Local reaction condition optimization via machine learning

CONTEXT

Reaction condition optimization addresses shared requirements across academia and industry, particularly in chemistry, pharmaceutical development, and fine chemical engineering. This review examines recent progress and persistent challenges in machine learning-guided optimization of localized reaction conditions, with an emphasis on three core aspects: dataset, condition representation, and optimization methods, as well as the main issues in each related stage. The review explores challenges such as dataset scarcity, data quality, and the "completeness trap" in dataset preparation stage, summarizes the limitations of current molecular representation techniques in condition representation stage, and discusses the search efficiency challenges of optimization methods in optimization stage.

METHODS

The review analyzes the molecular representation techniques and identifies them as the primary bottleneck in advancing localized reaction condition optimization. It further examines existing optimization methodologies. Among them, Bayesian optimization and active learning emerges as the most commonly applied approaches in this field, utilizing incremental learning mechanisms and human-in-the-loop strategies to minimize experimental data requirements while mitigating molecular representation limitations. The review concludes that advancements in molecular representation techniques are essential for developing more efficient optimization methods in the future.

Machine Learning for Developing Sustainable Polymers

Sustainable polymers from renewable resources have been gaining importance due to their recyclability and reduced environmental impact. However, their development through conventional trial-and-error methods remains inefficient and resource-intensive. Machine learning (ML) has emerged as a powerful tool in polymer science, enabling rapid prediction, and discovery of new chemicals and materials. In this review, we examine emerging trends in ML applications for sustainable polymer development, focusing on catalyst discovery, property optimization, and new polymer design. We analyze unique challenges in applying ML to sustainable polymers and evaluate proposed solutions, providing insights for future development in this rapidly evolving field.

High-Throughput Drug Derivatization and Bioassay by Desorption Electrospray Ionization Mass Spectrometry

Adapting high-throughput (HT) synthetic methods to the modification of drugs and to testing of their bioactivity should expedite drug discovery. Herein, the applicability of HT desorption electrospray ionization mass spectrometry (DESI MS) to achieve late-stage functionalization (LSF) and rapidly generate a modified opioid library is demonstrated. Specifically, aza-Michael addition and sulfur (VI) fluoride exchange reactions are used for functionalization. The modified drugs are both synthesized and characterized using an automated HT-DESI MS platform, with the reaction occurring during the droplet flight. Analysis by MS characterizes reaction products at a throughput of >1 reaction per second. With this platform, multiple nor-opioid scaffolds and functionalization reagents are screened and a selection of the hits obtained is subjected to HT label-free bioassays using the same DESI-MS platform. This combination of accelerated LSF reactions to rapidly create a diverse library of functionalized drugs with direct bioassays of the crude reaction mixtures for structure-activity relationship evaluation, both using the same platform, is anticipated to help expedite the early drug discovery process.

HiRXN: Hierarchical Attention-Based Representation Learning for Chemical Reaction

In recent years, natural language processing (NLP) techniques, including large language modeling (LLM), have contributed significantly to advancements in organic chemistry research. Chemical reaction representations provide a link between NLP models and chemistry prediction tasks and enable the translation of complex chemical processes into a format that NLP models can understand and learn from. However, previous representation methods fail to adequately consider the hierarchical and structural information inherent in chemical reactions. Here, we propose a tool named HiRXN to learn the comprehensive representation of chemical reactions based on their hierarchical structure. In order to significantly enhance feature engineering for machine learning (ML) models, HiRXN develops an effective tokenization method called RXNTokenizer to capture atomic microenvironment features with multiradius. Then, the hierarchical attention network is used to integrate information from atomic microenvironment-level and molecule-level to accurately understand chemical reactions. The experimental results show that HiRXN is capable of representing chemical reactions and achieves remarkable performance in terms of reaction regression and classification prediction tasks. A web server has been developed to provide a specialized service that accepts Reaction SMILES as input and provides predicted results. The Web site is accessible at http://bdatju.com.

Predicting and Explaining Yields with Machine Learning for Carboxylated Azoles and Beyond

Carbon dioxide (CO2) can be transformed into valuable chemical building blocks, including C2-carboxylated 1,3-azoles, which have potential applications in pharmaceuticals, cosmetics, and pesticides. However, only a small fraction of the millions of available 1,3-azoles are carboxylated at the C2 position, highlighting significant opportunities for further research in the synthesis and application of these compounds. In this study, we utilized a supervised machine learning approach to predict reaction yields for a data set of amide-coupled C2-carboxylated 1,3-azoles. To facilitate molecular design, we integrated an interpretable heat-mapping algorithm named PIXIE (Predictive Insights and Xplainability for Informed chemical space Exploration). PIXIE visualizes the influence of molecular substructures on predicted yields by leveraging fingerprint bit importances, providing synthetic chemists with a powerful tool for the rational design of molecules. While heat mapping is an established technique, its integration with a machine-learning model tailored to the chemical space of C2-carboxylated 1,3-azoles represents a significant advancement. This approach not only enables targeted exploration of this underrepresented chemical space, fostering the discovery of new bioactive compounds, but also demonstrates the potential of combining these methods for broader applications in other chemical domains.

Chemically Informed Deep Learning for Interpretable Radical Reaction Prediction

Organic radical reactions are crucial in many areas of chemistry, including synthetic, biological, and atmospheric chemistry. We develop a predictive framework based on the interaction of molecular orbitals that operates on mechanistic-level radical reactions. Given our chemistry-aware model, all predictions are provided with different levels of interpretability. Our models are trained and evaluated using the RMechDB database of radical reaction steps. Our model predicts the correct orbital interaction and products for 96% of the test reactions in RMechDB. By chaining these predictions, we perform a pathway search capable of identifying all intermediates and byproducts of a radical reaction. We test the pathway search on two classes of problems in atmospheric and polymerization chemistry. RMechRP is publicly available online at https://deeprxn.ics.uci.edu/rmechrp/.

An active representation learning method for reaction yield prediction with small-scale data

Reaction optimization plays an essential role in chemical research and industrial production. To explore a large reaction system, a practical issue is how to reduce the heavy experimental load for finding the high-yield conditions. In this paper, we present an efficient machine learning tool called "RS-Coreset", where the key idea is to take advantage of deep representation learning techniques to guide an interactive procedure for representing the full reaction space. Our proposed tool only uses small-scale data, say 2.5% to 5% of the instances, to predict the yields of the reaction space. We validate the performance on three public datasets and achieve state-of-the-art results. Moreover, we apply this tool to assist the realistic exploration of the Lewis base-boryl radicals enabled dechlorinative coupling reactions in our lab. The tool can help us to effectively predict the yields and even discover several feasible reaction combinations that were overlooked in previous articles.

Flow chemistry-enabled asymmetric synthesis of cyproterone acetate in a chemo-biocatalytic approach

Flow chemistry has many advantages over batch synthesis of organic small-molecules in terms of environmental compatibility, safety and synthetic efficiency when scale-up is considered. Herein, we report the 10-step chemo-biocatalytic continuous flow asymmetric synthesis of cyproterone acetate (4) in which 10 transformations are combined into a telescoped flow linear sequence from commercially available 4-androstene-3, 17-dione (11). This integrated one-flow synthesis features an engineered 3-ketosteroid-Δ1-dehydrogenase (ReM2)-catalyzed Δ1-dehydrogenation to form the C1, C2-double bond of A ring, a substrate-controlled Co-catalyzed Mukaiyama hydration of 9 to forge the crucial chiral C17α-OH group of D ring with excellent stereoselectivity, and a rapid flow Corey-Chaykovsky cyclopropanation of 7 to build the cyclopropyl core of A ring. By strategic use of these three key reactions and fully continuous-flow operations, cyproterone acetate (4) is produced in an overall yield of 9.6% in 3 h of total reaction time, this is the highest total number of chemical transformation performance in any other continuous-flow synthesis reported to date.

Impact of Adding N-hexylamine to Nickel Metallophotoredox C-N Coupling to Form Diarylamines

The mechanistic understanding of how alkylamines impact Ni-metallophotoredox C-N coupling to form diarylamines remains unclear. In this study, 12-alkylamines were evaluated as additives to determine their effects on the synthesis of diarylamines in a flow photochemical system. Notably, n-hexylamine demonstrated the most significant promotional effect. Spectroscopic studies and experimental data reveal n-hexylamine substitutes DABCO as a Ni catalyst ligand, enhancing yields particularly in sterically hindered arylamines.

Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes

The breadth of transition metal chemical space covered by databases such as the Cambridge Structural Database and the derived computational database tmQM is not conducive to application-specific modeling and the development of structure-property relationships. Here, we employ both supervised and unsupervised natural language processing (NLP) techniques to link experimentally synthesized compounds in the tmQM database to their respective applications. Leveraging NLP models, we curate four distinct datasets: tmCAT for catalysis, tmPHOTO for photophysical activity, tmBIO for biological relevance, and tmSCO for magnetism. Analyzing the chemical substructures within each dataset reveals common chemical motifs in each of the designated applications. We then use these common chemical structures to augment our initial datasets for each application, yielding a total of 21 631 compounds in tmCAT, 4599 in tmPHOTO, 2782 in tmBIO, and 983 in tmSCO. These datasets are expected to accelerate the more targeted computational screening and development of refined structure-property relationships with machine learning.

Emerging trends in the optimization of organic synthesis through high-throughput tools and machine learning

The discovery of the optimal conditions for chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically, the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and through the design of experiments where reaction variables are modified one at a time to find the optimal conditions for a specific reaction outcome. Recently, a paradigm change in chemical reaction optimization has been enabled by advances in lab automation and the introduction of machine learning algorithms. Therein, multiple reaction variables can be synchronously optimized to obtain the optimal reaction conditions, requiring a shorter experimentation time and minimal human intervention. Herein, we review the currently used state-of-the-art high-throughput automated chemical reaction platforms and machine learning algorithms that drive the optimization of chemical reactions, highlighting the limitations and future opportunities of this new field of research.

SynAsk: unleashing the power of large language models in organic synthesis

The field of natural language processing (NLP) has witnessed a transformative shift with the emergence of large language models (LLMs), revolutionizing various language tasks and applications, and the integration of LLMs into specialized domains enhances their capabilities for domain-specific applications. Notably, NLP has made significant strides in organic chemistry, particularly in predicting synthetic tasks, paving the way for the development of LLMs tailored to the organic chemistry field. In this work, we introduce SynAsk, a comprehensive organic chemistry domain-specific LLM platform developed by AIChemEco Inc. By fine-tuning an LLM with domain-specific data and integrating it with a chain of thought approach, SynAsk seamlessly accesses our knowledge base and advanced chemistry tools in a question-and-answer format. This includes functionalities such as a basic chemistry knowledge base, molecular information retrieval, reaction performance prediction, retrosynthesis prediction, chemical literature acquisition, and more. This novel methodology synergizes fine-tuning techniques with external resource integration, resulting in an organic chemistry-specific model poised to facilitate research and discovery in the field. Accessible at https://synask.aichemeco.com, SynAsk represents a significant advancement in leveraging NLP for synthetic applications.

An Automated Electrochemical Flow Platform to Accelerate Library Synthesis and Reaction Optimization

Automated batch and flow setups are well-established for high throughput experimentation in both thermal chemistry and photochemistry. However, the development of automated electrochemical platforms is hindered by cell miniaturization challenges in batch and difficulties in designing effective single-pass flow systems. In order to address these issues, we have designed and implemented a new, slug-based automated electrochemical flow platform. This platform was successfully demonstrated for electrochemical C-N cross-couplings of E3 ligase binders with diverse amines (44 examples), which were subsequently transferred to a continuous-flow mode for confirmation and isolation, showing its applicability for medicinal chemistry purposes. To further validate the versatility of the platform, Design of Experiments (DoE) optimization was performed for an unsuccessful library target. This optimization process, fully automated by the platform, resulted in a remarkable 6-fold increase in reaction yield.

Rapid Exploration of Chemical Space by High-Throughput Desorption Electrospray Ionization Mass Spectrometry

This study leverages accelerated reactions at the solution/air interface of microdroplets generated by desorption electrospray ionization (DESI) to explore the chemical space. DESI is utilized to synthesize drug analogs at an overall rate of 1 reaction mixture per second, working on the low-nanogram scale. Transformations of multiple drug molecules at specific functionalities (phenol, hydroxyl, amino, carbonyl, phenyl, thiophenyl, and alkenyl) are achieved using electrophilic/nucleophilic, redox, C-H functionalization, and coupling reactions. These transformations occur under ambient conditions on the millisecond time scale with direct detection of products being successful in all but three of the reaction types studied. The large scope (22 bioactive compounds, >20 chemical transformations, and >300 functionalization reagents) and high speed (>3000 reactions/hour) provide access to a wide array of drug analogs that can be used for bioactivity testing. A total of ∼6800 unique reactions were examined through a data-driven workflow, and more than 3000 unique derivatives (∼44%) were identified tentatively by the m/z value and signal-to-control ratio in single-stage mass spectrometry (MS) analysis, with over 1000 being further characterized by tandem MS. The speed of the DESI-MS reaction screen provides potential advantages for emerging machine learning-based predictions of organic synthesis, and it sets the stage for future online DESI-MS bioassays and scaled-up microdroplet synthesis before formal characterization of hit compounds is sought using traditional methods of drug discovery.

An automatic end-to-end chemical synthesis development platform powered by large language models

The rapid emergence of large language model (LLM) technology presents promising opportunities to facilitate the development of synthetic reactions. In this work, we leveraged the power of GPT-4 to build an LLM-based reaction development framework (LLM-RDF) to handle fundamental tasks involved throughout the chemical synthesis development. LLM-RDF comprises six specialized LLM-based agents, including Literature Scouter, Experiment Designer, Hardware Executor, Spectrum Analyzer, Separation Instructor, and Result Interpreter, which are pre-prompted to accomplish the designated tasks. A web application with LLM-RDF as the backend was built to allow chemist users to interact with automated experimental platforms and analyze results via natural language, thus, eliminating the need for coding skills and ensuring accessibility for all chemists. We demonstrated the capabilities of LLM-RDF in guiding the end-to-end synthesis development process for the copper/TEMPO catalyzed aerobic alcohol oxidation to aldehyde reaction, including literature search and information extraction, substrate scope and condition screening, reaction kinetics study, reaction condition optimization, reaction scale-up and product purification. Furthermore, LLM-RDF's broader applicability and versability was validated on various synthesis tasks of three distinct reactions (SNAr reaction, photoredox C-C cross-coupling reaction, and heterogeneous photoelectrochemical reaction).

At the Speed of Light: The Systematic Implementation of Photoredox Cross-Coupling Reactions for Medicinal Chemistry Research

The adoption of new and emerging techniques in organic synthesis is essential to promote innovation in drug discovery. In this Perspective, we detail the strategy we used for the systematic deployment of photoredox-mediated, metal-catalyzed cross-coupling reactions in AbbVie's medicinal chemistry organization, focusing on topics such as assessment, evaluation, implementation, and accessibility. The comprehensive evaluation of photoredox reaction setups and published methods will be discussed, along with internal efforts to build expertise and photoredox high-throughput experimentation capabilities. We also highlight AbbVie's academic-industry collaborations in this field that have been leveraged to develop new synthetic strategies, along with discussing the internal adoption of photoredox cross-coupling reactions. The work described herein has culminated in robust photocatalysis and cross-coupling capabilities which are viewed as key platforms for medicinal chemistry research at AbbVie.

The ubiquitous P(o-tol)3 ligand promotes formation of catalytically-active higher order palladacyclic clusters

The Herrmann-Beller catalyst, Pd[(C^P)(μ2-OAc)]2, is readily formed by reaction of the cyclic trimer of 'Pd(OAc)2' with P(o-tol)3. In the presence of hydroxide, Pd(C^P)(μ2-OAc)]2 converts to [Pd(C^P)(μ 2 -OH)]2. Here, we report how this activated Pd precatalyst species, and related species, serve as a conduit for formation of higher order Pd n clusters containing multiple cyclopalladated P(o-tol)3 ligands. The catalytic competency of a Pd4-palladacyclic cluster is demonstrated in an arylated Heck cross-coupling, which is comparable to the base-activated form of Herrmann's catalyst, namely [Pd(C^P)(μ2-OH)]2. The findings show that 'simple' ubiquitous phosphine ligands can promote higher order Pd speciation, moving beyond well-known phosphine-ligated Pd1 and Pd2 complexes. The findings challenge the status quo in the field, in that phosphine ligands can ligate higher order Pd n species which are catalytically competent species in cross-coupling reactions.

Roboticized AI-assisted microfluidic photocatalytic synthesis and screening up to 10,000 reactions per day

The current throughput of conventional organic chemical synthesis is usually a few experiments for each operator per day. We develop a robotic system for ultra-high-throughput chemical synthesis, online characterization, and large-scale condition screening of photocatalytic reactions, based on the liquid-core waveguide, microfluidic liquid-handling, and artificial intelligence techniques. The system is capable of performing automated reactant mixture preparation, changing, introduction, ultra-fast photocatalytic reactions in seconds, online spectroscopic detection of the reaction product, and screening of different reaction conditions. We apply the system in large-scale screening of 12,000 reaction conditions of a photocatalytic [2 + 2] cycloaddition reaction including multiple continuous and discrete variables, reaching an ultra-high throughput up to 10,000 reaction conditions per day. Based on the data, AI-assisted cross-substrate/photocatalyst prediction is conducted.

Machine learning-guided strategies for reaction conditions design and optimization

This review surveys the recent advances and challenges in predicting and optimizing reaction conditions using machine learning techniques. The paper emphasizes the importance of acquiring and processing large and diverse datasets of chemical reactions, and the use of both global and local models to guide the design of synthetic processes. Global models exploit the information from comprehensive databases to suggest general reaction conditions for new reactions, while local models fine-tune the specific parameters for a given reaction family to improve yield and selectivity. The paper also identifies the current limitations and opportunities in this field, such as the data quality and availability, and the integration of high-throughput experimentation. The paper demonstrates how the combination of chemical engineering, data science, and ML algorithms can enhance the efficiency and effectiveness of reaction conditions design, and enable novel discoveries in synthetic chemistry.

Computer-Aided Synthesis Planning (CASP) and Machine Learning: Optimizing Chemical Reaction Conditions

Computer-aided synthesis planning (CASP) has garnered increasing attention in light of recent advancements in machine learning models. While the focus is on reverse synthesis or forward outcome prediction, optimizing reaction conditions remains a significant challenge. For datasets with multiple variables, the choice of descriptors and models is pivotal. This selection dictates the effective extraction of conditional features and the achievement of higher prediction accuracy. This review delineates the origins of data in conditional optimization, the criteria for descriptor selection, the response models, and the metrics for outcome evaluation, aiming to acquaint readers with the latest research trends and facilitate more informed research in this domain.

Chemical Graph-Based Transformer Models for Yield Prediction of High-Throughput Cross-Coupling Reaction Datasets

The chemical reaction yield is an important factor to determine the reaction conditions. Recently, many data-driven models for yield prediction using high-throughput experimentation datasets have been reported. In this study, we propose a neural network architecture based on the chemical graphs of the reaction components to predict the reaction yield. The proposed model is the sequential combination of a message-passing neural network and a transformer encoder (MPNN-Transformer). The reaction components are converted to molecular matrices by the first network, followed by the interplay of the reaction components in the second network after adding the embeddings of the compound roles in the chemical reaction. The predictive ability of the proposed models was compared with state-of-the-art yield prediction models using two high-throughput experimental datasets: the Buchwald-Hartwig cross-coupling (BHC) and Suzuki-Miyaura cross-coupling (SMC) reaction datasets. Overall, the MPNN-Transformer models showed high prediction accuracy for the BHC reaction datasets and some of the extrapolation-oriented SMC reaction datasets. These models also performed well when the training dataset size was relatively large. Furthermore, analyzing the poorly predicted reactions for the BHC reaction dataset revealed a limitation of the data-driven yield prediction approach based on the chemical structural similarity.

Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis

Organocatalysis has established itself as a third pillar of homogeneous catalysis, besides transition metal catalysis and biocatalysis, as its use for enantioselective reactions has gathered significant interest over the last decades. Concurrent to this development, machine learning (ML) has been increasingly applied in the chemical domain to efficiently uncover hidden patterns in data and accelerate scientific discovery. While the uptake of ML in organocatalysis has been comparably slow, the last two decades have showed an increased interest from the community. This review gives an overview of the work in the field of ML in organocatalysis. The review starts by giving a short primer on ML for experimental chemists, before discussing its application for predicting the selectivity of organocatalytic transformations. Subsequently, we review ML employed for privileged catalysts, before focusing on its application for catalyst and reaction design. Concluding, we give our view on current challenges and future directions for this field, drawing inspiration from the application of ML to other scientific domains.

Revealing the Hidden Complexity and Reactivity of Palladacyclic Precatalysts: The P(o-tolyl)3 Ligand Enables a Cocktail of Active Species Utilizing the Pd(II)/Pd(IV) and Pd(0)/Pd(II) Pathways for Efficient Catalysis

The ligand, P(o-tolyl)3, is ubiquitous in applied synthetic chemistry and catalysis, particularly in Pd-catalyzed processes, which typically include Pd(OAc)2 (most commonly used as Pd3(OAc)6) as a precatalyst. The Herrmann-Beller palladacycle [Pd(C^P)(μ2-OAc)]2 (where C^P = monocyclopalladated P(o-tolyl)3) is easily formed from reaction of Pd(OAc)2 with P(o-tolyl)3. The mechanisms by which this precatalyst system operates are inherently complex, with studies previously implicating Pd nanoparticles (PdNPs) as reservoirs for active Pd(0) species in arylative cross-coupling reactions. In this study, we reveal the fascinating, complex, and nontrivial behavior of the palladacyclic group. First, in the presence of hydroxide base, [Pd(C^P)(μ2-OAc)]2 is readily converted into an activated form, [Pd(C^P)(μ2-OH)]2, which serves as a conduit for activation to catalytically relevant species. Second, palladacyclization imparts unique stability for catalytic species under reaction conditions, bringing into play a Pd(II)/Pd(IV) cross-coupling mechanism. For a benchmark Suzuki-Miyaura cross-coupling (SMCC) reaction, there is a shift from a mononuclear Pd catalytic pathway to a PdNP-controlled catalytic pathway during the reaction. The activation pathway of [Pd(C^P)(μ2-OH)]2 has been studied using an arylphosphine-stabilized boronic acid and low-temperature NMR spectroscopic analysis, which sheds light on the preactivation step, with water and/or acid being critical for the formation of active Pd(0) and Pd(II) species. In situ reaction monitoring has demonstrated that there is a sensitivity to the structure of the arylboron species in the presence of pinacol. This work, taken together, highlights the mechanistic complexity accompanying the use of palladacyclic precatalyst systems. It builds on recent findings involving related Pd(OAc)2/PPh3 precatalyst systems which readily form higher order Pdn clusters and PdNPs under cross-coupling reaction conditions. Thus, generally, one needs to be cautious with the assumption that Pd(OAc)2/tertiary phosphine mixtures cleanly deliver mononuclear "Pd(0)Ln" species and that any assessment of individual phosphine ligands may need to be taken on a case-by-case basis.

Machine Learning Optimizing Enzyme/ZIF Biocomposites for Enhanced Encapsulation Efficiency and Bioactivity

In this study, we present the first example of using a machine learning (ML)-assisted design strategy to optimize the synthesis formulation of enzyme/ZIFs (zeolitic imidazolate framework) for enhanced performance. Glucose oxidase (GOx) and horseradish peroxidase (HRP) were chosen as model enzymes, while Zn(eIM)2 (eIM = 2-ethylimidazolate) was selected as the model ZIF to test our ML-assisted workflow paradigm. Through an iterative ML-driven training-design-synthesis-measurement workflow, we efficiently discovered GOx/ZIF (G151) and HRP/ZIF (H150) with their overall performance index (OPI) values (OPI represents the product of encapsulation efficiency (E in %), retained enzymatic activity (A in %), and thermal stability (T in %)) at least 1.3 times higher than those in systematic seed data studies. Furthermore, advanced statistical methods derived from the trained random forest model qualitatively and quantitatively reveal the relationship among synthesis, structure, and performance in the enzyme/ZIF system, offering valuable guidance for future studies on enzyme/ZIFs. Overall, our proposed ML-assisted design strategy holds promise for accelerating the development of enzyme/ZIFs and other enzyme immobilization systems for biocatalysis applications and beyond, including drug delivery and sensing, among others.

Democratizing Microreactor Technology for Accelerated Discoveries in Chemistry and Materials Research

Microreactor technologies have emerged as versatile platforms with the potential to revolutionize chemistry and materials research, offering sustainable solutions to global challenges in environmental and health domains. This survey paper provides an in-depth review of recent advancements in microreactor technologies, focusing on their role in facilitating accelerated discoveries in chemistry and materials. Specifically, we examine the convergence of microfluidics with machine intelligence and automation, enabling the exploitation of the cyber-physical environment as a highly integrated experimentation platform for rapid scientific discovery and process development. We investigate the applicability and limitations of microreactor-enabled discovery accelerators in various chemistry and materials contexts. Despite their tremendous potential, the integration of machine intelligence and automation into microreactor-based experiments presents challenges in establishing fully integrated, automated, and intelligent systems. These challenges can hinder the broader adoption of microreactor technologies within the research community. To address this, we review emerging technologies that can help lower barriers and facilitate the implementation of microreactor-enabled discovery accelerators. Lastly, we provide our perspective on future research directions for democratizing microreactor technologies, with the aim of accelerating scientific discoveries and promoting widespread adoption of these transformative platforms.

Printed polymer platform empowering machine-assisted chemical synthesis in stacked droplets

Efficiently exploring organic molecules through multi-step processes demands a transition from conventional laboratory synthesis to automated systems. Existing platforms for machine-assistant synthetic workflows compatible with multiple liquid-phases require substantial engineering investments for setup, thereby hindering quick customization and throughput increasement. Here we present a droplet-based chip that facilitates the self-organization of various liquid phases into stacked layers for conducting chemical transformations. The chip's precision polymer printing capability, enabled by digital micromirror device (DMD)-maskless photolithography and dual post-chemical modifications, allows it to create customized, sub-10 µm featured patterns to confine diverse liquids, regardless of density, within each droplet. The robustness and open design of surface-templated liquid layers actualize machine-assistant droplet manipulation, synchronous reaction triggering, local oscillation, and real-time monitoring of individual layers into a reality. We propose that, with further integration of machine operation line and self-learning, this droplet-based platform holds the potential to become a valuable addition to the toolkit of chemistry process, operating autonomously and with high-throughput.

Reaction acceleration at the surface of a levitated droplet by vapor dosing from a partner droplet

Chemical reactions in micrometer-sized droplets can be accelerated by up to six orders of magnitude. However, this acceleration factor (ratio of rate constants relative to bulk) drops to less than 10 for millimeter-sized droplets due to the reduction in surface/volume ratio. To enhance the acceleration in millimeter-sized droplets, we use a new synthesis platform that directly doses reagent vapor onto the reaction droplet surface from a second levitated droplet. Using Katritzky transamination as a model reaction, we made quantitative measurements on size-controlled vapor-dosed droplets, revealing a 31-fold increase in reaction rate constants when examining the entire droplet contents. This enhancement is attributed to a greater reaction rate constant in the droplet surface region (estimated as 105 times greater than that for the bulk). The capability for substantial reaction acceleration in large droplets highlights the potential for rapid synthesis of important chemicals at useful scales. For example, we successfully prepared 23 pyridinium salts within minutes. This efficiency positions droplets as an exceptional platform for rapid, in situ catalyst synthesis. This is illustrated by the preparation of pyridinium salts as photocatalysts and their subsequent use in mediation of amine oxidation both within the same droplet.

Advances in machine learning with chemical language models in molecular property and reaction outcome predictions

Molecular properties and reactions form the foundation of chemical space. Over the years, innumerable molecules have been synthesized, a smaller fraction of them found immediate applications, while a larger proportion served as a testimony to creative and empirical nature of the domain of chemical science. With increasing emphasis on sustainable practices, it is desirable that a target set of molecules are synthesized preferably through a fewer empirical attempts instead of a larger library, to realize an active candidate. In this front, predictive endeavors using machine learning (ML) models built on available data acquire high timely significance. Prediction of molecular property and reaction outcome remain one of the burgeoning applications of ML in chemical science. Among several methods of encoding molecular samples for ML models, the ones that employ language like representations are gaining steady popularity. Such representations would additionally help adopt well-developed natural language processing (NLP) models for chemical applications. Given this advantageous background, herein we describe several successful chemical applications of NLP focusing on molecular property and reaction outcome predictions. From relatively simpler recurrent neural networks (RNNs) to complex models like transformers, different network architecture have been leveraged for tasks such as de novo drug design, catalyst generation, forward and retro-synthesis predictions. The chemical language model (CLM) provides promising avenues toward a broad range of applications in a time and cost-effective manner. While we showcase an optimistic outlook of CLMs, attention is also placed on the persisting challenges in reaction domain, which would optimistically be addressed by advanced algorithms tailored to chemical language and with increased availability of high-quality datasets.

Benchmarking machine-readable vectors of chemical reactions on computed activation barriers

In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks. Consequently, various predictive approaches have emerged, ranging from graph-based models to methods based on the three-dimensional structure of reactants and products. In tandem, many representations have been developed to predict experimental targets, which may hold promise for barrier prediction as well. Here, we bring together all of these efforts and benchmark various methods (Morgan fingerprints, the DRFP, the CGR representation-based Chemprop, SLATMd, B2Rl2, EquiReact and language model BERT + RXNFP) for the prediction of computed activation barriers on three diverse datasets.

A green, facile, and practical preparation of capsaicin derivatives with thiourea structure

Capsaicin derivatives with thiourea structure (CDTS) is highly noteworthy owing to its higher analgesic potency in rodent models and higher agonism in vitro. However, the direct synthesis of CDTS remains t one or more shortcomings. In this study, we present reported a green, facile, and practical synthetic method of capsaicin derivatives with thiourea structure is developed by using an automated synthetic system, leading to a series of capsaicin derivatives with various electronic properties and functionalities in good to excellent yields.

Automated, Capsule-Based Suzuki-Miyaura Cross Couplings

The development of an automated process for Suzuki-Miyaura cross couplings is described, in which the complete reaction, workup, and product isolation are effected automatically with no user involvement, aside from loading of the starting materials and reaction capsule. This practical and simple method was successfully demonstrated to provide the desired biaryl products using a range of aryl bromides and boronic acids and is also effective for the late-stage functionalization of aryl halides in bioactive molecules.

A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization

Flow processing offers many opportunities to optimize reactions in a rapid and automated manner, yet often requires relatively large quantities of input materials. To combat this, the use of a flexible slug flow reactor, equipped with two analytical instruments, for low-volume optimization experiments are reported. A Buchwald-Hartwig amination toward the drug olanzapine, with 6 independent optimizable variables, is optimized using three different automated approaches: self-optimization, design of experiments, and kinetic modeling. These approaches are complementary and provide differing information on the reaction: pareto optimal operating points, response surface models, and mechanistic models, respectively. The results are achieved using <10% of the material that would be required for standard flow operation. Finally, a chemometric model is built utilizing automated data handling and three subsequent validation experiments demonstrate good agreement between the slug flow reactor and a standard (larger scale) flow reactor.

Probing the chemical 'reactome' with high-throughput experimentation data

High-throughput experimentation (HTE) has the potential to improve our understanding of organic chemistry by systematically interrogating reactivity across diverse chemical spaces. Notable bottlenecks include few publicly available large-scale datasets and the need for facile interpretation of these data's hidden chemical insights. Here we report the development of a high-throughput experimentation analyser, a robust and statistically rigorous framework, which is applicable to any HTE dataset regardless of size, scope or target reaction outcome, which yields interpretable correlations between starting material(s), reagents and outcomes. We improve the HTE data landscape with the disclosure of 39,000+ previously proprietary HTE reactions that cover a breadth of chemistry, including cross-coupling reactions and chiral salt resolutions. The high-throughput experimentation analyser was validated on cross-coupling and hydrogenation datasets, showcasing the elucidation of statistically significant hidden relationships between reaction components and outcomes, as well as highlighting areas of dataset bias and the specific reaction spaces that necessitate further investigation.

Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation

Electrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and machine-learning algorithms unlock the possibility for accelerated studies of electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous electrochemical platform that implements an adaptive, closed-loop workflow for mechanistic investigation of molecular electrochemistry. As a proof-of-concept, this platform autonomously identifies and investigates an EC mechanism, an interfacial electron transfer (E step) followed by a solution reaction (C step), for cobalt tetraphenylporphyrin exposed to a library of organohalide electrophiles. The generally applicable workflow accurately discerns the EC mechanism's presence amid negative controls and outliers, adaptively designs desired experimental conditions, and quantitatively extracts kinetic information of the C step spanning over 7 orders of magnitude, from which mechanistic insights into oxidative addition pathways are gained. This work opens opportunities for autonomous mechanistic discoveries in self-driving electrochemistry laboratories without manual intervention.

Recent advances and applications in high-throughput continuous flow

High-throughput continuous flow technology has emerged as a revolutionary approach in chemical synthesis, offering accelerated experimentation and improved efficiency. With the aid of process analytical technology and automation, this system not only enables rapid optimisation of reaction conditions at the millimole to the picomole scale, but also facilitates automated scale-up synthesis. It can even achieve the self-planning and self-synthesis of small drug molecules with artificial intelligence incorporated in the system. The versatility of the system is highlighted by its compatibility with both electrochemistry and photochemistry, and its significant applications in organic synthesis and drug discovery. This highlight summarises its recent developments and applications, emphasising its significant impact on advancing research across multiple disciplines.

High-Throughput Capillary Liquid Chromatography Using a Droplet Injection and Application to Reaction Screening

The cycle time of a standard liquid chromatography (LC) system is the sum of the time for the chromatographic run and the autosampler injection sequence. Although LC separation times in the 1-10 s range have been demonstrated, injection sequences are commonly >15 s, limiting throughput possible with LC separations. Further, such separations are performed on relatively large bore columns requiring flow rates of ≥5 mL/min, thus generating large volumes of mobile phase waste when used for large scale screening and increasing the difficulty in interfacing to mass spectrometry. Here, a droplet injector system was established that replaces the autosampler with a four-port, two-position valve equipped with a 20 nL internal loop interfaced to a syringe pump and a three-axis positioner to withdraw sample droplets from a well plate. In the system, sample and immiscible fluid are pulled alternately from a well plate into a capillary and then through the injection valve. The valve is actuated when sample fills the loop to allow sequential injection of samples at high throughput. Capillary LC columns with 300 μm inner diameter were used to reduce the consumption of mobile phase and sample. The system achieved 96 separations of 20 nL droplet samples containing 3 components in as little as 8.1 min with 5-s cycle time. This system was coupled to a mass spectrometer through an electrospray ionization source for high-throughput chemical reaction screening.

Improving chemical reaction yield prediction using pre-trained graph neural networks

Graph neural networks (GNNs) have proven to be effective in the prediction of chemical reaction yields. However, their performance tends to deteriorate when they are trained using an insufficient training dataset in terms of quantity or diversity. A promising solution to alleviate this issue is to pre-train a GNN on a large-scale molecular database. In this study, we investigate the effectiveness of GNN pre-training in chemical reaction yield prediction. We present a novel GNN pre-training method for performance improvement.Given a molecular database consisting of a large number of molecules, we calculate molecular descriptors for each molecule and reduce the dimensionality of these descriptors by applying principal component analysis. We define a pre-text task by assigning a vector of principal component scores as the pseudo-label to each molecule in the database. A GNN is then pre-trained to perform the pre-text task of predicting the pseudo-label for the input molecule. For chemical reaction yield prediction, a prediction model is initialized using the pre-trained GNN and then fine-tuned with the training dataset containing chemical reactions and their yields. We demonstrate the effectiveness of the proposed method through experimental evaluation on benchmark datasets.

Prediction of chemical reaction yields with large-scale multi-view pre-training

Developing machine learning models with high generalization capability for predicting chemical reaction yields is of significant interest and importance. The efficacy of such models depends heavily on the representation of chemical reactions, which has commonly been learned from SMILES or graphs of molecules using deep neural networks. However, the progression of chemical reactions is inherently determined by the molecular 3D geometric properties, which have been recently highlighted as crucial features in accurately predicting molecular properties and chemical reactions. Additionally, large-scale pre-training has been shown to be essential in enhancing the generalization capability of complex deep learning models. Based on these considerations, we propose the Reaction Multi-View Pre-training (ReaMVP) framework, which leverages self-supervised learning techniques and a two-stage pre-training strategy to predict chemical reaction yields. By incorporating multi-view learning with 3D geometric information, ReaMVP achieves state-of-the-art performance on two benchmark datasets. Notably, the experimental results indicate that ReaMVP has a significant advantage in predicting out-of-sample data, suggesting an enhanced generalization ability to predict new reactions. Scientific Contribution: This study presents the ReaMVP framework, which improves the generalization capability of machine learning models for predicting chemical reaction yields. By integrating sequential and geometric views and leveraging self-supervised learning techniques with a two-stage pre-training strategy, ReaMVP achieves state-of-the-art performance on benchmark datasets. The framework demonstrates superior predictive ability for out-of-sample data and enhances the prediction of new reactions.

Visible-light-mediated direct C3 alkylation of quinoxalin-2(1H)-ones using alkanes

Due to the high C-H bond dissociation energy of alkanes, the utilization of alkanes as alkyl radical precursors for C-H functionalization of heteroarenes is synthetically captivating but practically challenging, especially under metal- and photocatalyst-free conditions. We report herein a mild and practical visible-light-mediated method for C-H alkylation of quinoxalin-2(1H)-ones using trifluoroacetic acid as a hydrogen atom transfer reagent and air as an oxidant. This mild protocol was performed under metal- and photocatalyst-free circumstances and presented good functional-group tolerance as well as a broad substrate scope.

MicroCycle: An Integrated and Automated Platform to Accelerate Drug Discovery

We herein describe the development and application of a modular technology platform which incorporates recent advances in plate-based microscale chemistry, automated purification, in situ quantification, and robotic liquid handling to enable rapid access to high-quality chemical matter already formatted for assays. In using microscale chemistry and thus consuming minimal chemical matter, the platform is not only efficient but also follows green chemistry principles. By reorienting existing high-throughput assay technology, the platform can generate a full package of relevant data on each set of compounds in every learning cycle. The multiparameter exploration of chemical and property space is hereby driven by active learning models. The enhanced compound optimization process is generating knowledge for drug discovery projects in a time frame never before possible.

Automated self-optimization, intensification, and scale-up of photocatalysis in flow

The optimization, intensification, and scale-up of photochemical processes constitute a particular challenge in a manufacturing environment geared primarily toward thermal chemistry. In this work, we present a versatile flow-based robotic platform to address these challenges through the integration of readily available hardware and custom software. Our open-source platform combines a liquid handler, syringe pumps, a tunable continuous-flow photoreactor, inexpensive Internet of Things devices, and an in-line benchtop nuclear magnetic resonance spectrometer to enable automated, data-rich optimization with a closed-loop Bayesian optimization strategy. A user-friendly graphical interface allows chemists without programming or machine learning expertise to easily monitor, analyze, and improve photocatalytic reactions with respect to both continuous and discrete variables. The system's effectiveness was demonstrated by increasing overall reaction yields and improving space-time yields compared with those of previously reported processes.

Enabling High Throughput Kinetic Experimentation by Using Flow as a Differential Kinetic Technique

Kinetic data is most commonly collected through the generation of time-series data under either batch or flow conditions. Existing methods to generate kinetic data in flow collect integral data (concentration over time) only. Here, we report a method for the rapid and direct collection of differential kinetic data (direct measurement of rate) in flow by performing a series of instantaneous rate measurements on sequential small-scale reactions. This technique decouples the time required to generate a full kinetic profile from the time required for a reaction to reach completion, enabling high throughput kinetic experimentation. In addition, comparison of kinetic profiles constructed at different residence times allows the robustness, or stability, of homogeneously catalysed reactions to be interrogated. This approach makes use of a segmented flow platform which was shown to quantitatively reproduce batch kinetic data. The proline mediated aldol reaction was chosen as a model reaction to perform a high throughput kinetic screen of 216 kinetic profiles in 90 hours, one every 25 minutes, which would have taken an estimated continuous 3500 hours in batch, an almost 40-fold increase in experimental throughput matched by a corresponding reduction in material consumption.

Enhancing Generic Reaction Yield Prediction through Reaction Condition-Based Contrastive Learning

Deep learning (DL)-driven efficient synthesis planning may profoundly transform the paradigm for designing novel pharmaceuticals and materials. However, the progress of many DL-assisted synthesis planning (DASP) algorithms has suffered from the lack of reliable automated pathway evaluation tools. As a critical metric for evaluating chemical reactions, accurate prediction of reaction yields helps improve the practicality of DASP algorithms in the real-world scenarios. Currently, accurately predicting yields of interesting reactions still faces numerous challenges, mainly including the absence of high-quality generic reaction yield datasets and robust generic yield predictors. To compensate for the limitations of high-throughput yield datasets, we curated a generic reaction yield dataset containing 12 reaction categories and rich reaction condition information. Subsequently, by utilizing 2 pretraining tasks based on chemical reaction masked language modeling and contrastive learning, we proposed a powerful bidirectional encoder representations from transformers (BERT)-based reaction yield predictor named Egret. It achieved comparable or even superior performance to the best previous models on 4 benchmark datasets and established state-of-the-art performance on the newly curated dataset. We found that reaction-condition-based contrastive learning enhances the model's sensitivity to reaction conditions, and Egret is capable of capturing subtle differences between reactions involving identical reactants and products but different reaction conditions. Furthermore, we proposed a new scoring function that incorporated Egret into the evaluation of multistep synthesis routes. Test results showed that yield-incorporated scoring facilitated the prioritization of literature-supported high-yield reaction pathways for target molecules. In addition, through meta-learning strategy, we further improved the reliability of the model's prediction for reaction types with limited data and lower data quality. Our results suggest that Egret holds the potential to become an essential component of the next-generation DASP tools.

When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges

Machine Learning (ML) techniques face significant challenges when predicting advanced chemical properties, such as yield, feasibility of chemical synthesis, and optimal reaction conditions. These challenges stem from the high-dimensional nature of the prediction task and the myriad essential variables involved, ranging from reactants and reagents to catalysts, temperature, and purification processes. Successfully developing a reliable predictive model not only holds the potential for optimizing high-throughput experiments but can also elevate existing retrosynthetic predictive approaches and bolster a plethora of applications within the field. In this review, we systematically evaluate the efficacy of current ML methodologies in chemoinformatics, shedding light on their milestones and inherent limitations. Additionally, a detailed examination of a representative case study provides insights into the prevailing issues related to data availability and transferability in the discipline.

Dataset Design for Building Models of Chemical Reactivity

Models can codify our understanding of chemical reactivity and serve a useful purpose in the development of new synthetic processes via, for example, evaluating hypothetical reaction conditions or in silico substrate tolerance. Perhaps the most determining factor is the composition of the training data and whether it is sufficient to train a model that can make accurate predictions over the full domain of interest. Here, we discuss the design of reaction datasets in ways that are conducive to data-driven modeling, emphasizing the idea that training set diversity and model generalizability rely on the choice of molecular or reaction representation. We additionally discuss the experimental constraints associated with generating common types of chemistry datasets and how these considerations should influence dataset design and model building.

Autonomous chemical research with large language models

Transformer-based large language models are making significant strides in various fields, such as natural language processing1-5, biology6,7, chemistry8-10 and computer programming11,12. Here, we show the development and capabilities of Coscientist, an artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation. Coscientist showcases its potential for accelerating research across six diverse tasks, including the successful reaction optimization of palladium-catalysed cross-couplings, while exhibiting advanced capabilities for (semi-)autonomous experimental design and execution. Our findings demonstrate the versatility, efficacy and explainability of artificial intelligence systems like Coscientist in advancing research.

Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors

We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property-free knowledge base for future anticoronavirus drug discovery.