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

Cascade Processes with Micellar Reaction Media: Recent Advances and Future Directions

Reducing the use of solvents is an important aim of green chemistry. Using micelles self-assembled from amphiphilic molecules dispersed in water (considered a green solvent) has facilitated reactions of organic compounds. When performing reactions in micelles, the hydrophobic effect can considerably accelerate apparent reaction rates, as well as enhance selectivity. Here, we review micellar reaction media and their potential role in sustainable chemical production. The focus of this review is applications of engineered amphiphilic systems for reactions (surface-active ionic liquids, designer surfactants, and block copolymers) as reaction media. Micelles are a versatile platform for performing a large array of organic chemistries using water as the bulk solvent. Building on this foundation, synthetic sequences combining several reaction steps in one pot have been developed. Telescoping multiple reactions can reduce solvent waste by limiting the volume of solvents, as well as eliminating purification processes. Thus, in particular, we review recent advances in “one-pot” multistep reactions achieved using micellar reaction media with potential applications in medicinal chemistry and agrochemistry. Photocatalyzed reactions in micellar reaction media are also discussed. In addition to the use of micelles, we emphasize the process (steps to isolate the product and reuse the catalyst).

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 ChemPU’s 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.

Sequential Properties Representation Scheme for Recurrent Neural Network-Based Prediction of Therapeutic Peptides

The discovery of therapeutic peptides is often accelerated by means of virtual screening supported by machine learning-based predictive models. The predictive performance of such models is sensitive to the choice of data and its representation scheme. While the peptide physicochemical and compositional representations fail to distinguish sequence permutations, the amino acid arrangement within the sequence lacks the important information contained in physicochemical, conformational, topological, and geometrical properties. In this paper, we propose a solution to the identified information gap by implementing a hybrid scheme that complements the best traits from both approaches with the aim of predicting antimicrobial and antiviral activities based on experimental data from DRAMP 2.0, AVPdb, and Uniprot data repositories. Using the Friedman test of statistical significance, we compared our hybrid, sequential properties approach to peptide properties, one-hot vector encoding, and word embedding schemes in the 10-fold cross-validation setting, with respect to the F1 score, Matthews correlation coefficient, geometric mean, recall, and precision evaluation metrics. Moreover, the sequence modeling neural network was employed to gain insight into the synergic effect of both properties- and amino acid order-based predictions. The results suggest that sequential properties significantly (P < 0.01) surpasses the aforementioned state-of-the-art representation schemes. This makes it a strong candidate for increasing the predictive power of screening methods based on machine learning, applicable to any category of peptides.

Materials Data toward Machine Learning: Advances and Challenges

Machine learning (ML) is believed to have enabled a paradigm shift in materials research, and in practice, ML has demonstrated its power in speeding up the cost-efficient discovery of new materials and autonomizing materials laboratories. In this Perspective, current research progress in materials data which are the backbones of ML are reviewed, focusing on high-throughput data generation, standardized data storage, and data representation. More importantly, the challenging issues in materials data that should be overcome to unlock the full potential of ML in materials research and development, including classic 5V (volume, velocity, variety, veracity, and value) issues, 3M (multicomponent, multiscale, and multistage) challenges, co-mining of experimental and computational data, and materials data toward transferable/explainable ML or causal ML, are discussed.

The case for data science in experimental chemistry: examples and recommendations

The physical sciences community is increasingly taking advantage of the possibilities offered by modern data science to solve problems in experimental chemistry and potentially to change the way we design, conduct and understand results from experiments. Successfully exploiting these opportunities involves considerable challenges. In this Expert Recommendation, we focus on experimental co-design and its importance to experimental chemistry. We provide examples of how data science is changing the way we conduct experiments, and we outline opportunities for further integration of data science and experimental chemistry to advance these fields. Our recommendations include establishing stronger links between chemists and data scientists; developing chemistry-specific data science methods; integrating algorithms, software and hardware to 'co-design' chemistry experiments from inception; and combining diverse and disparate data sources into a data network for chemistry research.

Exploration of Ultralarge Compound Collections for Drug Discovery

Designing new medicines more cheaply and quickly is tightly linked to the quest of exploring chemical space more widely and efficiently. Chemical space is monumentally large, but recent advances in computer software and hardware have enabled researchers to navigate virtual chemical spaces containing billions of chemical structures. This review specifically concerns collections of many millions or even billions of enumerated chemical structures as well as even larger chemical spaces that are not fully enumerated. We present examples of chemical libraries and spaces and the means used to construct them, and we discuss new technologies for searching huge libraries and for searching combinatorially in chemical space. We also cover space navigation techniques and consider new approaches to de novo drug design and the impact of the "autonomous laboratory" on synthesis of designed compounds. Finally, we summarize some other challenges and opportunities for the future.

Making the collective knowledge of chemistry open and machine actionable

Large amounts of data are generated in chemistry labs-nearly all instruments record data in a digital form, yet a considerable proportion is also captured non-digitally and reported in ways non-accessible to both humans and their computational agents. Chemical research is still largely centred around paper-based lab notebooks, and the publication of data is often more an afterthought than an integral part of the process. Here we argue that a modular open-science platform for chemistry would be beneficial not only for data-mining studies but also, well beyond that, for the entire chemistry community. Much progress has been made over the past few years in developing technologies such as electronic lab notebooks that aim to address data-management concerns. This will help make chemical data reusable, however it is only one step. We highlight the importance of centring open-science initiatives around open, machine-actionable data and emphasize that most of the required technologies already exist-we only need to connect, polish and embrace them.

Autonomous Multi-Step and Multi-Objective Optimization Facilitated by Real-Time Process Analytics

Autonomous flow reactors are becoming increasingly utilized in the synthesis of organic compounds, yet the complexity of the chemical reactions and analytical methods remains limited. The development of a modular platform which uses rapid flow NMR and FTIR measurements, combined with chemometric modeling, is presented for efficient and timely analysis of reaction outcomes. This platform is tested with a four variable single-step reaction (nucleophilic aromatic substitution), to determine the most effective optimization methodology. The self-optimization approach with minimal background knowledge proves to provide the optimal reaction parameters within the shortest operational time. The chosen approach is then applied to a seven variable two-step optimization problem (imine formation and cyclization), for the synthesis of the active pharmaceutical ingredient edaravone. Despite the exponentially increased complexity of this optimization problem, the platform achieves excellent results in a relatively small number of iterations, leading to >95% solution yield of the intermediate and up to 5.42 kg L-1 h-1 space-time yield for this pharmaceutically relevant product.

From Platform to Knowledge Graph: Evolution of Laboratory Automation

High-fidelity computer-aided experimentation is becoming more accessible with the development of computing power and artificial intelligence tools. The advancement of experimental hardware also empowers researchers to reach a level of accuracy that was not possible in the past. Marching toward the next generation of self-driving laboratories, the orchestration of both resources lies at the focal point of autonomous discovery in chemical science. To achieve such a goal, algorithmically accessible data representations and standardized communication protocols are indispensable. In this perspective, we recategorize the recently introduced approach based on Materials Acceleration Platforms into five functional components and discuss recent case studies that focus on the data representation and exchange scheme between different components. Emerging technologies for interoperable data representation and multi-agent systems are also discussed with their recent applications in chemical automation. We hypothesize that knowledge graph technology, orchestrating semantic web technologies and multi-agent systems, will be the driving force to bring data to knowledge, evolving our way of automating the laboratory.

Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

Autonomous catalysis research requires elaborate integration of operando experiments into automated workflows. Suitable experimental data for analysis by artificial intelligence can be measured more readily according to standard operating procedures.

Towards 4th industrial revolution efficient and sustainable continuous flow manufacturing of active pharmaceutical ingredients

The convergence of end-to-end continuous flow synthesis with downstream processing, process analytical technology (PAT), artificial intelligence (AI), machine learning and automation in ensuring improved accessibility of quality medicines on demand.

Accelerating Manufacturing for Biomass Conversion via Integrated Process and Bench Digitalization: A Perspective

We present a perspective for accelerating biomass manufacturing via digitalization. We summarize the challenges for manufacturing and identify areas where digitalization can help. A profound potential in using lignocellulosic biomass...

Prediction of Optimal Conditions of Hydrogenation Reaction Using the Likelihood Ranking Approach

The selection of experimental conditions leading to a reasonable yield is an important and essential element for the automated development of a synthesis plan and the subsequent synthesis of the target compound. The classical QSPR approach, requiring one-to-one correspondence between chemical structure and a target property, can be used for optimal reaction conditions prediction only on a limited scale when only one condition component (e.g., catalyst or solvent) is considered. However, a particular reaction can proceed under several different conditions. In this paper, we describe the Likelihood Ranking Model representing an artificial neural network that outputs a list of different conditions ranked according to their suitability to a given chemical transformation. Benchmarking calculations demonstrated that our model outperformed some popular approaches to the theoretical assessment of reaction conditions, such as k Nearest Neighbors, and a recurrent artificial neural network performance prediction of condition components (reagents, solvents, catalysts, and temperature). The ability of the Likelihood Ranking model trained on a hydrogenation reactions dataset, (~42,000 reactions) from Reaxys^® database, to propose conditions that led to the desired product was validated experimentally on a set of three reactions with rich selectivity issues.

Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning

Electronic structure methods based on quantum mechanics (QM) are widely employed in the computational predictions of the molecular properties and optoelectronic properties of molecular materials. The computational costs of these QM methods, ranging from density functional theory (DFT) or time-dependent DFT (TDDFT) to wave-function theory (WFT), usually increase sharply with the system size, causing the curse of dimensionality and hindering the QM calculations for large sized systems such as long polymer oligomers and complex molecular aggregates. In such cases, in recent years low scaling QM methods and machine learning (ML) techniques have been adopted to reduce the computational costs and thus assist computational and data driven molecular material design. In this review, we illustrated low scaling ground-state and excited-state QM approaches and their applications to long oligomers, self-assembled supramolecular complexes, stimuli-responsive materials, mechanically interlocked molecules, and excited state processes in molecular aggregates. Variable electrostatic parameters were also introduced in the modified force fields with the polarization model. On the basis of QM computational or experimental datasets, several ML algorithms, including explainable models, deep learning, and on-line learning methods, have been employed to predict the molecular energies, forces, electronic structure properties, and optical or electrical properties of materials. It can be conceived that low scaling algorithms with periodic boundary conditions are expected to be further applicable to functional materials, perhaps in combination with machine learning to fast predict the lattice energy, crystal structures, and spectroscopic properties of periodic functional materials.

The Open Reaction Database

Chemical reaction data in journal articles, patents, and even electronic laboratory notebooks are currently stored in various formats, often unstructured, which presents a significant barrier to downstream applications, including the training of machine-learning models. We present the Open Reaction Database (ORD), an open-access schema and infrastructure for structuring and sharing organic reaction data, including a centralized data repository. The ORD schema supports conventional and emerging technologies, from benchtop reactions to automated high-throughput experiments and flow chemistry. The data, schema, supporting code, and web-based user interfaces are all publicly available on GitHub. Our vision is that a consistent data representation and infrastructure to support data sharing will enable downstream applications that will greatly improve the state of the art with respect to computer-aided synthesis planning, reaction prediction, and other predictive chemistry tasks.

DeepReac+: deep active learning for quantitative modeling of organic chemical reactions

Various computational methods have been developed for quantitative modeling of organic chemical reactions; however, the lack of universality as well as the requirement of large amounts of experimental data limit their broad applications. Here, we present DeepReac+, an efficient and universal computational framework for prediction of chemical reaction outcomes and identification of optimal reaction conditions based on deep active learning. Under this framework, DeepReac is designed as a graph-neural-network-based model, which directly takes 2D molecular structures as inputs and automatically adapts to different prediction tasks. In addition, carefully-designed active learning strategies are incorporated to substantially reduce the number of necessary experiments for model training. We demonstrate the universality and high efficiency of DeepReac+ by achieving the state-of-the-art results with a minimum of labeled data on three diverse chemical reaction datasets in several scenarios. Collectively, DeepReac+ has great potential and utility in the development of AI-aided chemical synthesis. DeepReac+ is freely accessible at https://github.com/bm2-lab/DeepReac.

Assisted Self-Assembly to Target Heterometallic Mn-Nd and Mn-Sm SMMs: Synthesis and Magnetic Characterisation of [Mn7 Ln3 (O)4 (OH)4 (mdea)3 (piv)9 (NO3 )3 ] (Ln=Nd, Sm, Eu, Gd)*

In an assisted self-assembly approach starting from the [Mn6 O2 (piv)10 (4-Me-py)2 (pivH)2 ] cluster a family of Mn-Ln compounds (Ln=Pr-Yb) was synthesised. The reaction of [Mn6 O2 (piv)10 (4-Me-py)2 (pivH)2 ] (1) with N-methyldiethanolamine (mdeaH2 ) and Ln(NO3 )3  ⋅ 6H2 O in MeCN generally yields two main structure types: for Ln=Tb-Yb a previously reported Mn5 Ln4 motif is obtained, whereas for Ln=Pr-Eu a series of Mn7 Ln3 clusters is obtained. Within this series the GdIII analogue represents a special case because it shows both structural types as well as a third Mn2 Ln2 inverse butterfly motif. Variation in reaction conditions allows access to different structure types across the whole series. This prompts further studies into the reaction mechanism of this cluster assisted self-assembly approach. For the Mn7 Ln3 analogues reported here variable-temperature magnetic susceptibility measurements suggest that antiferromagnetic interactions between the spin carriers are dominant. Compounds incorporating Ln=NdIII (2), SmIII (3) and GdIII (5) display SMM behaviour. The slow relaxation of the magnetisation for these compounds was confirmed by ac measurements above 1.8 K.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

Chemputation and the Standardization of Chemical Informatics

The explosion in the use of machine learning for automated chemical reaction optimization is gathering pace. However, the lack of a standard architecture that connects the concept of chemical transformations universally to software and hardware provides a barrier to using the results of these optimizations and could cause the loss of relevant data and prevent reactions from being reproducible or unexpected findings verifiable or explainable. In this Perspective, we describe how the development of the field of digital chemistry or chemputation, that is the universal code-enabled control of chemical reactions using a standard language and ontology, will remove these barriers allowing users to focus on the chemistry and plug in algorithms according to the problem space to be explored or unit function to be optimized. We describe a standard hardware (the chemical processing programming architecture-the ChemPU) to encompass all chemical synthesis, an approach which unifies all chemistry automation strategies, from solid-phase peptide synthesis, to HTE flow chemistry platforms, while at the same time establishing a publication standard so that researchers can exchange chemical code (χDL) to ensure reproducibility and interoperability. Not only can a vast range of different chemistries be plugged into the hardware, but the ever-expanding developments in software and algorithms can also be accommodated. These technologies, when combined will allow chemistry, or chemputation, to follow computation-that is the running of code across many different types of capable hardware to get the same result every time with a low error rate.

Standardization and Control of Grignard Reactions in a Universal Chemical Synthesis Machine using online NMR

A big problem with the chemistry literature is that it is not standardized with respect to precise operational parameters, and real time corrections are hard to make without expert knowledge. This lack of context means difficult reproducibility because many steps are ambiguous, and hence depend on tacit knowledge. Here we present the integration of online NMR into an automated chemical synthesis machine (CSM aka. "Chemputer" which is capable of small-molecule synthesis using a universal programming language) to allow automated analysis and adjustment of reactions on the fly. The system was validated and benchmarked by using Grignard reactions which were chosen due to their importance in synthesis. The system was monitored in real time using online-NMR, and spectra were measured continuously during the reactions. This shows that the synthesis being done in the Chemputer can be dynamically controlled in response to feedback optimizing the reaction conditions according to the user requirements.

The future directions of synthetic chemistry

After being developed over hundred years, synthetic chemistry has created numerous new molecules and new materials to support a better life welfare. Even so, many challenges still remain in synthetic chemistry, higher selectivity, higher efficiency, environmental benign and sustainable energy are never been so wistful before. Herein, several topics surrounded the ability improvement of synthesis and the application enhancement of synthesis will be briefly discussed.

Toward autonomous design and synthesis of novel inorganic materials

Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and development. Herein, we review recent progress in the design of self-driving laboratories, including robotics to automate materials synthesis and characterization, in conjunction with AI to interpret experimental outcomes and propose new experimental procedures. We focus on efforts to automate inorganic synthesis through solution-based routes, solid-state reactions, and thin film deposition. In each case, connections are made to relevant work in organic chemistry, where automation is more common. Characterization techniques are primarily discussed in the context of phase identification, as this task is critical to understand what products have formed during synthesis. The application of deep learning to analyze multivariate characterization data and perform phase identification is examined. To achieve "closed-loop" materials synthesis and design, we further provide a detailed overview of optimization algorithms that use active learning to rationally guide experimental iterations. Finally, we highlight several key opportunities and challenges for the future development of self-driving inorganic materials synthesis platforms.

IP Analytics and Machine Learning Applied to Create Process Visualization Graphs for Chemical Utility Patents

Researchers must read and understand a large volume of technical papers, including patent documents, to fully grasp the state-of-the-art technological progress in a given domain. Chemical research is particularly challenging with the fast growth of newly registered utility patents (also known as intellectual property or IP) that provide detailed descriptions of the processes used to create a new chemical or a new process to manufacture a known chemical. The researcher must be able to understand the latest patents and literature in order to develop new chemicals and processes that do not infringe on existing claims and processes. This research uses text mining, integrated machine learning, and knowledge visualization techniques to effectively and accurately support the extraction and graphical presentation of chemical processes disclosed in patent documents. The computer framework trains a machine learning model called ALBERT for automatic paragraph text classification. ALBERT separates chemical and non-chemical descriptive paragraphs from a patent for effective chemical term extraction. The ChemDataExtractor is used to classify chemical terms, such as inputs, units, and reactions from the chemical paragraphs. A computer-supported graph-based knowledge representation interface is developed to plot the extracted chemical terms and their chemical process links as a network of nodes with connecting arcs. The computer-supported chemical knowledge visualization approach helps researchers to quickly understand the innovative and unique chemical or processes of any chemical patent of interest.

Expanding the Tool Kit of Automated Flow Synthesis: Development of In-line Flash Chromatography Purification

Recent advancements in in-line extraction and purification technology have enabled complex multistep synthesis in continuous flow reactor systems. However, for the large scope of chemical reactions that yield mixtures of products or residual starting materials, off-line purification is still required to isolate the desired compound. We present the in-line integration of a commercial automated flash chromatography system with a flow reactor for the continuous synthesis and isolation of product(s). A proof-of-principle study was performed to validate the system and test the durability of the column cartridges, performing an automated sequence of 100 runs over 2 days. Three diverse reaction systems that highlight the advantages of flow synthesis were successfully applied with in-line normal- or reversed-phase flash chromatography, continuously isolating products with 97-99% purity. Productivity of up to 9.9 mmol/h was achieved, isolating gram quantities of pure product from a feed of crude reaction mixture. Herein, we describe the development and optimization of the systems and suggest guidelines for selecting reactions well suited to in-line flash chromatography.

Automated Chemical Reaction Extraction from Scientific Literature

Access to structured chemical reaction data is of key importance for chemists in performing bench experiments and in modern applications like computer-aided drug design. Existing reaction databases are generally populated by human curators through manual abstraction from published literature (e.g., patents and journals), which is time consuming and labor intensive, especially with the exponential growth of chemical literature in recent years. In this study, we focus on developing automated methods for extracting reactions from chemical literature. We consider journal publications as the target source of information, which are more comprehensive and better represent the latest developments in chemistry compared to patents; however, they are less formulaic in their descriptions of reactions. To implement the reaction extraction system, we first devised a chemical reaction schema, primarily including a central product, and a set of associated reaction roles such as reactants, catalyst, solvent, and so on. We formulate the task as a structure prediction problem and solve it with a two-stage deep learning framework consisting of product extraction and reaction role labeling. Both models are built upon Transformer-based encoders, which are adaptively pretrained using domain and task-relevant unlabeled data. Our models are shown to be both effective and data efficient, achieving an F1 score of 76.2% in product extraction and 78.7% in role extraction, with only hundreds of annotated reactions.

Inferring experimental procedures from text-based representations of chemical reactions

The experimental execution of chemical reactions is a context-dependent and time-consuming process, often solved using the experience collected over multiple decades of laboratory work or searching similar, already executed, experimental protocols. Although data-driven schemes, such as retrosynthetic models, are becoming established technologies in synthetic organic chemistry, the conversion of proposed synthetic routes to experimental procedures remains a burden on the shoulder of domain experts. In this work, we present data-driven models for predicting the entire sequence of synthesis steps starting from a textual representation of a chemical equation, for application in batch organic chemistry. We generated a data set of 693,517 chemical equations and associated action sequences by extracting and processing experimental procedure text from patents, using state-of-the-art natural language models. We used the attained data set to train three different models: a nearest-neighbor model based on recently-introduced reaction fingerprints, and two deep-learning sequence-to-sequence models based on the Transformer and BART architectures. An analysis by a trained chemist revealed that the predicted action sequences are adequate for execution without human intervention in more than 50% of the cases.

An integrated console for capsule-based, automated organic synthesis

The current laboratory practices of organic synthesis are labor intensive, impose safety and environmental hazards, and hamper the implementation of artificial intelligence guided drug discovery. Using a combination of reagent design, hardware engineering, and a simple operating system we provide an instrument capable of executing complex organic reactions with prepacked capsules. The machine conducts coupling reactions and delivers the purified products with minimal user involvement. Two desirable reaction classes – the synthesis of saturated N-heterocycles and reductive amination – were implemented, along with multi-step sequences that provide drug-like organic molecules in a fully automated manner. We envision that this system will serve as a console for developers to provide synthetic methods as integrated, user-friendly packages for conducting organic synthesis in a safe and convenient fashion.

Using a combination of reagent design, hardware engineering, and a simple operating system we provide an instrument capable of executing complex organic reactions using prepacked capsules with minimal user involvement.

Chemistry Informer Libraries: Conception, Early Experience, and Role in the Future of Cheminformatics

The synthetic chemistry literature traditionally reports the scope of new methods using simple, nonstandardized test molecules that have uncertain relevance in applied synthesis. In addition, published examples heavily favor positive reaction outcomes, and failure is rarely documented. In this environment, synthetic practitioners have inadequate information to know whether any given method is suitable for the task at hand. Moreover, the incomplete nature of published data makes it poorly suited for the creation of predictive reactivity models via machine learning approaches. In 2016, we reported the concept of chemistry informer libraries as standardized sets of medium- to high-complexity substrates with relevance to pharmaceutical synthesis as demonstrated using a multidimensional principle component analysis (PCA) comparison to the physicochemical properties of marketed drugs. We showed how informer libraries could be used to evaluate leading synthetic methods with the complete capture of success and failure and how this knowledge could lead to improved reaction conditions with a broader scope with respect to relevant applications. In this Account, we describe the progress made and lessons learned in subsequent studies using informer libraries to profile eight additional reaction classes. Examining broad trends across multiple types of bond disconnections against a standardized chemistry "measuring stick" has enabled comparisons of the relative potential of different methods for applications in complex synthesis and has identified opportunities for further development. Furthermore, the powerful combination of informer libraries and 1536-well-plate nanoscale reaction screening has allowed the parallel evaluation of scores of synthetic methods in the same experiment and as such illuminated an important role for informers as part of a larger data generation workflow for predictive reactivity modeling. Using informer libraries as problem-dense, strong filters has allowed broad sets of reaction conditions to be narrowed down to those that display the highest tolerance to complex substrates. These best conditions can then be used to survey broad swaths of substrate space using nanoscale chemistry approaches. Our experiences and those of our collaborators from several academic laboratories applying informer libraries in these contexts have helped us identify several areas for potential improvements to the approach that would increase their ease of use, utility in generating interpretable results, and resulting uptake by the broader community. As we continue to evolve the informer library concept, we believe it will play an ever-increasing role in the future of the democratization of high-throughput experimentation and data science-driven synthetic method development.

Ecological and Biotechnological Aspects of Pigmented Microbes: A Way Forward in Development of Food and Pharmaceutical Grade Pigments

Microbial pigments play multiple roles in the ecosystem construction, survival, and fitness of all kinds of organisms. Considerably, microbial (bacteria, fungi, yeast, and microalgae) pigments offer a wide array of food, drug, colorants, dyes, and imaging applications. In contrast to the natural pigments from microbes, synthetic colorants are widely used due to high production, high intensity, and low cost. Nevertheless, natural pigments are gaining more demand over synthetic pigments as synthetic pigments have demonstrated side effects on human health. Therefore, research on microbial pigments needs to be extended, explored, and exploited to find potential industrial applications. In this review, the evolutionary aspects, the spatial significance of important pigments, biomedical applications, research gaps, and future perspectives are detailed briefly. The pathogenic nature of some pigmented bacteria is also detailed for awareness and safe handling. In addition, pigments from macro-organisms are also discussed in some sections for comparison with microbes.

Debugging: putting the synthetic yeast chromosome to work

Synthetic genomics aims to de novo synthesize a functional genome redesigned from natural sequences with custom features. Designed genomes provide new toolkits for better understanding organisms, evolution and the construction of cellular factories. Currently maintaining the fitness of cells with synthetic genomes is particularly challenging as defective designs and unanticipated assembly errors frequently occur. Mapping and correcting bugs that arise during the synthetic process are imperative for the successful construction of a synthetic genome that can sustain a desired cellular function. Here, we review recently developed methods used to map and fix various bugs which arise during yeast genome synthesis with the hope of providing guidance for putting the synthetic yeast chromosome to work.

Natural Products, the Fourth Industrial Revolution, and the Quintuple Helix

The profound interconnectedness of the sciences and technologies embodied in the Fourth Industrial Revolution is discussed in terms of the global role of natural products, and how that interplays with the development of sustainable and climate-conscious practices of cyberecoethnopharmacolomics within the Quintuple Helix for the promotion of a healthier planet and society.

Machine Learning-Aided Crystal Facet Rational Design with Ionic Liquid Controllable Synthesis

Crystallographic facets in a crystal carry interior properties and proffer rich functionalities in a wide range of application areas. However, rational prediction, on-demand customization, and accurate synthesis of facets and facet junctions of a crystal are enormously desirable but still challenging. Herein, a framework of machine learning (ML)-aided crystal facet design with ionic liquid controllable synthesis is developed and then demonstrated with the star-material anatase TiO₂ . Aided by employing ML to acquire surface energies from facet junction datasource, the relationships between surface energy and growth conditions based on the Langmuir adsorption isotherm are unveiled, enabling to develop controllable facet synthetic strategies. These strategies are successfully verified after applied for synthesizing TiO₂ crystals with custom crystal facets and facet junctions under tuning ionic liquid [bmim][BF₄ ] experimental conditions. Therefore, this innovative framework integrates data-intensive rational design and experimental controllable synthesis to develop and customize crystallographic facets and facet junctions. This proves the feasibility of an intelligent chemistry future to accelerate the discovery of facet-governed functional material candidates.

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.

Combining radial and continuous flow synthesis to optimize and scale-up the production of medicines

Rapid, standardized process optimization and development on a radial synthesizer can be directly converted to a dedicated continuous flow process for scale-up, shown for three APIs via single- and multistep syntheses and continuous crystallization.

Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine

Although the automatic synthesis of molecules has been established, each reaction class uses bespoke hardware. This means that the connection of multi-step syntheses in a single machine to run many different protocols and reactions is not possible, as manual intervention is required. Here we show how the Chemputer synthesis robot can be programmed to perform many different reactions, including solid-phase peptide synthesis, iterative cross-coupling and accessing reactive, unstable diazirines in a single, unified system with high yields and purity. Developing universal and modular hardware that can be automated using one software system makes a wide variety of batch chemistry accessible. This is shown by our system, which performed around 8,500 operations while reusing only 22 distinct steps in 10 unique modules, with the code able to access 17 different reactions. We also demonstrate a complex convergent robotic synthesis of a peptide reacted with a diazirine-a process requiring 12 synthetic steps.

Drug design targeting active posttranslational modification protein isoforms

Modern drug design aims to discover novel lead compounds with attractable chemical profiles to enable further exploration of the intersection of chemical space and biological space. Identification of small molecules with good ligand efficiency, high activity, and selectivity is crucial toward developing effective and safe drugs. However, the intersection is one of the most challenging tasks in the pharmaceutical industry, as chemical space is almost infinity and continuous, whereas the biological space is very limited and discrete. This bottleneck potentially limits the discovery of molecules with desirable properties for lead optimization. Herein, we present a new direction leveraging posttranslational modification (PTM) protein isoforms target space to inspire drug design termed as "Post-translational Modification Inspired Drug Design (PTMI-DD)." PTMI-DD aims to extend the intersections of chemical space and biological space. We further rationalized and highlighted the importance of PTM protein isoforms and their roles in various diseases and biological functions. We then laid out a few directions to elaborate the PTMI-DD in drug design including discovering covalent binding inhibitors mimicking PTMs, targeting PTM protein isoforms with distinctive binding sites from that of wild-type counterpart, targeting protein-protein interactions involving PTMs, and hijacking protein degeneration by ubiquitination for PTM protein isoforms. These directions will lead to a significant expansion of the biological space and/or increase the tractability of compounds, primarily due to precisely targeting PTM protein isoforms or complexes which are highly relevant to biological functions. Importantly, this new avenue will further enrich the personalized treatment opportunity through precision medicine targeting PTM isoforms.

Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently

The number of 'small' molecules that may be of interest to chemical biologists - chemical space - is enormous, but the fraction that have ever been made is tiny. Most strategies are discriminative, i.e. have involved 'forward' problems (have molecule, establish properties). However, we normally wish to solve the much harder generative or inverse problem (describe desired properties, find molecule). 'Deep' (machine) learning based on large-scale neural networks underpins technologies such as computer vision, natural language processing, driverless cars, and world-leading performance in games such as Go; it can also be applied to the solution of inverse problems in chemical biology. In particular, recent developments in deep learning admit the in silico generation of candidate molecular structures and the prediction of their properties, thereby allowing one to navigate (bio)chemical space intelligently. These methods are revolutionary but require an understanding of both (bio)chemistry and computer science to be exploited to best advantage. We give a high-level (non-mathematical) background to the deep learning revolution, and set out the crucial issue for chemical biology and informatics as a two-way mapping from the discrete nature of individual molecules to the continuous but high-dimensional latent representation that may best reflect chemical space. A variety of architectures can do this; we focus on a particular type known as variational autoencoders. We then provide some examples of recent successes of these kinds of approach, and a look towards the future.