Exploration of the chemical space and its three historical regimes

Reaction rebalancing: a novel approach to curating reaction databases

PURPOSE

Reaction databases are a key resource for a wide variety of applications in computational chemistry and biochemistry, including Computer-aided Synthesis Planning (CASP) and the large-scale analysis of metabolic networks. The full potential of these resources can only be realized if datasets are accurate and complete. Missing co-reactants and co-products, i.e., unbalanced reactions, however, are the rule rather than the exception. The curation and correction of such incomplete entries is thus an urgent need.

METHODS

The SynRBL framework addresses this issue with a dual-strategy: a rule-based method for non-carbon compounds, using atomic symbols and counts for prediction, alongside a Maximum Common Subgraph (MCS)-based technique for carbon compounds, aimed at aligning reactants and products to infer missing entities.

RESULTS

The rule-based method exceeded 99% accuracy, while MCS-based accuracy varied from 81.19 to 99.33%, depending on reaction properties. Furthermore, an applicability domain and a machine learning scoring function were devised to quantify prediction confidence. The overall efficacy of this framework was delineated through its success rate and accuracy metrics, which spanned from 89.83 to 99.75% and 90.85 to 99.05%, respectively.

CONCLUSION

The SynRBL framework offers a novel solution for recalibrating chemical reactions, significantly enhancing reaction completeness. With rigorous validation, it achieved groundbreaking accuracy in reaction rebalancing. This sets the stage for future improvement in particular of atom-atom mapping techniques as well as of downstream tasks such as automated synthesis planning.

SCIENTIFIC CONTRIBUTION

SynRBL features a novel computational approach to correcting unbalanced entries in chemical reaction databases. By combining heuristic rules for inferring non-carbon compounds and common subgraph searches to address carbon unbalance, SynRBL successfully addresses most instances of this problem, which affects the majority of data in most large-scale resources. Compared to alternative solutions, SynRBL achieves a dramatic increase in both success rate and accurary, and provides the first freely available open source solution for this problem.

A stochastic approach for parameter optimization of feature detection algorithms for non-target screening in mass spectrometry

Feature detection plays a crucial role in non-target screening (NTS), requiring careful selection of algorithm parameters to minimize false positive (FP) features. In this study, a stochastic approach was employed to optimize the parameter settings of feature detection algorithms used in processing high-resolution mass spectrometry data. This approach was demonstrated using four open-source algorithms (OpenMS, SAFD, XCMS, and KPIC2) within the patRoon software platform for processing extracts from drinking water samples spiked with 46 per- and polyfluoroalkyl substances (PFAS). The designed method is based on a stochastic strategy involving random sampling from variable space and the use of Pearson correlation to assess the impact of each parameter on the number of detected suspect analytes. Using our approach, the optimized parameters led to improvement in the algorithm performance by increasing suspect hits in case of SAFD and XCMS, and reducing the total number of detected features (i.e., minimizing FP) for OpenMS. These improvements were further validated on three different drinking water samples as test dataset. The optimized parameters resulted in a lower false discovery rate (FDR%) compared to the default parameters, effectively increasing the detection of true positive features. This work also highlights the necessity of algorithm parameter optimization prior to starting the NTS to reduce the complexity of such datasets.

Single-Molecule Mixture: A Concept in Polymer Science

In theory, two extreme forms of substances exist: the pure form and the single-molecule mixture form. The latter contains a mixture of molecules with molecularly different structures. Inspired by the "chemical space" concept, in this paper, I report a study of the single-molecule mixture state that combines model construction and mathematical analysis, obtaining some interesting results. These results provide theoretical evidence that the single-molecule mixture state may indeed exist in realistic synthetic or natural polymer systems.

Transformers enable accurate prediction of acute and chronic chemical toxicity in aquatic organisms

Environmental hazard assessments are reliant on toxicity data that cover multiple organism groups. Generating experimental toxicity data is, however, resource-intensive and time-consuming. Computational methods are fast and cost-efficient alternatives, but the low accuracy and narrow applicability domains have made their adaptation slow. Here, we present a AI-based model for predicting chemical toxicity. The model uses transformers to capture toxicity-specific features directly from the chemical structures and deep neural networks to predict effect concentrations. The model showed high predictive performance for all tested organism groups-algae, aquatic invertebrates and fish-and has, in comparison to commonly used QSAR methods, a larger applicability domain and a considerably lower error. When the model was trained on data with multiple effect concentrations (EC50/EC10), the performance was further improved. We conclude that deep learning and transformers have the potential to markedly advance computational prediction of chemical toxicity.

Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

Hexadecanoic acid analogs as potential CviR-mediated quorum sensing inhibitors in Chromobacterium violaceum: an in silico study

Chromobacterium violaceum is a Gram-negative, rod-shaped and opportunistic human pathogen. C. violaceum is resistant to various antibiotics due to the production of quorum sensing (QS)-controlled virulence factor and biofilm formation. Hence, we need to find alternative strategies to overcome the antimicrobial resistance and biofilm formation in Gram-negative bacteria. QS is a mechanism in which bacteria's ability to regulate the virulence factors and biofilm formations leads to disease progression. Previously, hexadecanoic acid was identified as a CviR-mediated quorum-sensing inhibitor. In this study, we aimed to discover potential analogs of hexadecanoic acid as a CviR-mediated quorum-sensing inhibitor against C. violaceum by using ADME/T prediction, density functional theory, molecular docking, molecular dynamics and free energy binding calculations. ADME/T properties predicted for analogs were acceptable for human oral absorption and feasibility. The highest occupied molecular orbitals and lowest unoccupied molecular orbitals gap energies predicted and found oleic acid with -0.3748 energies. Docosatrienoic acid exhibited the highest binding affinity -8.15 Kcal/mol and strong and stable interactions with the amino acid residues on the active site of the CviR protein. These compounds on MD simulations for 100 ns show strong hydrogen-bonding interactions with the protein and remain stable inside the active site. Our results suggest hexadecanoic acid analogs could serve as anti-QS and anti-biofilm molecules for treating C. violaceum infections. However, further validation and investigation of these inhibitors against CviR are needed to claim their candidacy for clinical trials.Communicated by Ramaswamy H. Sarma.

An integrated non-targeted and targeted analysis approach for identification of semi-volatile organic compounds in indoor dust

Household dust contains a wide variety of semi-volatile organic compounds (SVOCs) that may pose health risks. We developed a method integrating non-targeted analysis (NTA) and targeted analysis (TA) to identify SVOCs in indoor dust. Based on a combined use of gas and liquid chromatography with high-resolution mass spectrometry, an automated, time-efficient NTA workflow was developed, and high accuracy was observed. A total of 128 compounds were identified at confidence level 1 or 2 in NIST standard reference material dust (SRM 2585). Among them, 113 compounds had not been reported previously, and this suggested the value of NTA in characterizing contaminants in dust. Additionally, TA was done to avoid the loss of trace compounds. By integrating data obtained from the NTA and TA approaches, SVOCs in SRM 2585 were prioritized based on exposure and chemical toxicity. Six of the top 20 compounds have never been reported in SRM 2585, including melamine, dinonyl phthalate, oxybenzone, diheptyl phthalate, drometrizole, and 2-phenylphenol. Additionally, significant influences of analytical instruments and sample preparation on NTA results were observed. Overall, the developed method provided a powerful tool for identifying SVOCs in indoor dust, which is necessary to obtain a more complete understanding of chemical exposures and risks in indoor environments.

Risk assessment of chemicals and their mixtures are hindered by scarcity and inconsistencies between different environmental exposure limits

In chemical risk assessment, measured or modelled environmental concentrations are compared to environmental exposure limits (EELs), such as Predicted No Effect Concentrations (PNECs) or hazardous concentrations for 5% of species (HC05s) derived from species sensitivity distributions (SSDs). However, for many chemicals the EELs include large uncertainties or, in the worst case, the necessary data for their estimation are completely missing. This makes the assessment of chemical risks and any subsequent implementation of management strategies challenging. In this study we analyzed the uncertainty of EELs and its impact on chemical risk assessment. First, we compared three individual EEL datasets, two primarily based on experimental data and one based on computational predictions. The comparison demonstrates large disagreements between EEL data sources, with experimentally derived EELs differing by more than seven orders of magnitude. In a case-study, based on the predicted emissions of 2005 chemicals, we showed that these uncertainties lead to significantly different risk assessment outcomes, including large differences in the magnitude of the total risk, risk driver identification, and the ranking of use categories as risk contributors. We also show that the large data-gaps in EEL datasets cannot be covered by commonly used computational approaches (QSARs). We conclude that an expanded framework for interpreting risk characterization outcomes is needed. We also argue that the large data-gaps present in ecotoxicological data need to be addressed in order to achieve the European zero pollution vision as the growing emphasis on ambient exposures will further increase the demand for accurate and well-established EELs.

The six stages of the convergence of the periodic system to its final structure

The periodic system encodes order and similarity among chemical elements arising from known substances at a given time that constitute the chemical space. Although the system has incorporated new elements, the connection with the remaining space is still to be analysed, which leads to the question of how the exponentially growing space has affected the periodic system. Here we show, by analysing the space between 1800 and 2021, that the system has converged towards its current stable structure through six stages, respectively characterised by the finding of elements (1800-1826), the emergence of the core structure of the system (1826-1860), its organic chemistry bias (1860-1900) and its further stabilisation (1900-1948), World War 2 new chemistry (1948-1980) and the system final stabilisation (1980-). Given the self-reinforced low diversity of the space and the limited chemical possibilities of the elements to be synthesised, we hypothesise that the periodic system will remain largely untouched.

Benzoquinoline Chemical Space: A Helpful Approach in Antibacterial and Anticancer Drug Design

Benzoquinolines are used in many drug design projects as starting molecules subject to derivatization. This computational study aims to characterize e benzoquinone drug space to ease future drug design processes based on these molecules. The drug space is composed of all benzoquinones, which are active on topoisomerase II and ATP synthase. Topological, chemical, and bioactivity spaces are explored using computational methodologies based on virtual screening and scaffold hopping and molecular docking, respectively. Topological space is a geometrical space in which the elements composing it can be defined as a set of neighbors (which satisfy a particular axiom). In such space, a chemical space can be defined as the property space spanned by all possible molecules and chemical compounds adhering to a given set of construction principles and boundary conditions. In this chemical space, the potentially pharmacologically active molecules form the bioactivity space. Results show a poly-morphological chemical space that suggests distinct characteristics. The chemical space is correlated with properties such as steric energy, the number of hydrogen bonds, the presence of halogen atoms, and membrane permeability-related properties. Lastly, novel chemical compounds (such as oxadiazole methybenzamide and floro methylcyclohexane diene) with drug-like potential, active on TOPO II and ATP synthase have been identified.

Light-empowered contra-thermodynamic stereochemical editing

Creating, conserving and modifying the stereochemistry of organic compounds has been the subject of significant research efforts in synthetic chemistry. Most synthetic routes are designed according to the stereoselectivity-determining step. Stereochemical editing is an alternative strategy, wherein the chiral-defining or geometry-defining steps are independent of the construction of the major scaffold or complexity. It enables late-stage alterations of stereochemistry and can generate isomers from a single compound. However, in many instances, stereochemical editing processes are contra-thermodynamic, meaning the transformation is unfavourable. To overcome this barrier, photocatalysis uses photogenerated radical species and introduces thermochemical biases. A range of synthetically valuable contra-thermodynamic stereochemical editing processes have been invented, including deracemization of chiral molecules, positional alkene isomerization and dynamic epimerization of sugars and diols. In this Review, we highlight the fundamental mechanisms of visible-light photocatalysis and the general reactivity modes of the photogenerated radical intermediates towards contra-thermodynamic stereochemical editing processes.

Integrative Metallomics Studies of Toxic Metal(loid) Substances at the Blood Plasma–Red Blood Cell–Organ/Tumor Nexus

Globally, an estimated 9 million deaths per year are caused by human exposure to environmental pollutants, including toxic metal(loid) species. Since pollution is underestimated in calculations of the global burden of disease, the actual number of pollution-related deaths per year is likely to be substantially greater. Conversely, anticancer metallodrugs are deliberately administered to cancer patients, but their often dose-limiting severe adverse side-effects necessitate the urgent development of more effective metallodrugs that offer fewer off-target effects. What these seemingly unrelated events have in common is our limited understanding of what happens when each of these toxic metal(loid) substances enter the human bloodstream. However, the bioinorganic chemistry that unfolds at the plasma/red blood cell interface is directly implicated in mediating organ/tumor damage and, therefore, is of immediate toxicological and pharmacological relevance. This perspective will provide a brief synopsis of the bioinorganic chemistry of AsIII, Cd2+, Hg2+, CH3Hg+ and the anticancer metallodrug cisplatin in the bloodstream. Probing these processes at near-physiological conditions and integrating the results with biochemical events within organs and/or tumors has the potential to causally link chronic human exposure to toxic metal(loid) species with disease etiology and to translate more novel anticancer metal complexes to clinical studies, which will significantly improve human health in the 21st century.

An automated method for graph-based chemical space exploration and transition state finding

Algorithms that automatically explore the chemical space have been limited to chemical systems with a low number of atoms due to expensive involved quantum calculations and the large amount of possible reaction pathways. The method described here presents a novel solution to the problem of chemical exploration by generating reaction networks with heuristics based on chemical theory. First, a second version of the reaction network is determined through molecular graph transformations acting upon functional groups of the reacting. Only transformations that break two chemical bonds and form two new ones are considered, leading to a significant performance enhancement compared to previously presented algorithm. Second, energy barriers for this reaction network are estimated through quantum chemical calculations by a growing string method, which can also identify non-octet species missed during the previous step and further define the reaction network. The proposed algorithm has been successfully applied to five different chemical reactions, in all cases identifying the most important reaction pathways.

The expansion of chemical space in 1826 and in the 1840s prompted the convergence to the periodic system

The number and diversity of substances constituting the chemical space triggered, in two important steps, the convergence of the periodic system toward a stable backbone structure eventually unveiled in the 1860s. The first step occurred in 1826, and the second was between 1835 and 1845. Interestingly, the salient features of the periodic system of the 1860s can be detected as early as the 1840s, even when considering the effect of disagreement regarding the determination of atomic weights. The methods presented here become instrumental to study the further evolution of the periodic system and to ponder its current shape.

The periodic system, which intertwines order and similarity among chemical elements, arose from knowledge about substances constituting the chemical space. Little is known, however, about how the expansion of the space contributed to the emergence of the system—formulated in the 1860s. Here, we show by analyzing the space between 1800 and 1869 that after an unstable period culminating around 1826, chemical space led the system to converge to a backbone structure clearly recognizable in the 1840s. Hence, the system was already encoded in the space for about two and half decades before its formulation. Chemical events in 1826 and in the 1840s were driven by the discovery of new forms of combination standing the test of time. Emphasis of the space upon organic chemicals after 1830 prompted the recognition of relationships among elements participating in the organic turn and obscured some of the relationships among transition metals. To account for the role of nineteenth century atomic weights upon the system, we introduced an algorithm to adjust the space according to different sets of weights, which allowed for estimating the resulting periodic systems of chemists using one or the other weights. By analyzing these systems, from Dalton up to Mendeleev, Gmelin’s atomic weights of 1843 produce systems remarkably similar to that of 1869, a similarity that was reinforced by the atomic weights on the years to come. Although our approach is computational rather than historical, we hope it can complement other tools of the history of chemistry.

Screening organic contaminants in soil by two-dimensional gas chromatography high-resolution time-of-flight mass spectrometry: A non-target analysis strategy and contaminated area case study

Thousands of organic substances that are used in industrial applications ultimately enter the soil and may negatively affect human health. Limited numbers of target pollutants are usually monitored in environmental media because of analytical limitations. In this study, a non-target screening method for quickly analyzing multiple soil samples from a contaminated area (a chemical industry park) by two-dimensional gas chromatography high-resolution time-of-flight mass spectrometry was developed. The types of compounds present in the soil samples were preliminarily analyzed through data simplification and visual assessment. A total of 81 organic compounds with detection frequencies ≥40% in the samples from the chemical industry park were selected for identification, including 38 PAHs, 26 oxygenated organic compounds, eight N-containing compounds, and nine other compounds. Potential sources of the organic compounds in the industrial park were investigated. Some pharmaceutical and organic synthetic intermediates in the soil were affected by nearby chemical plants. After assessing the relative abundances and detection frequencies, 36 pollutants that may pose potential risks to the environment were preliminarily identified. The results of the study were helpful for assessing environmental risks around Yangkou industrial park and they will be helpful when assessing risks in other contaminated areas.

Prediction of Compound Synthesis Accessibility Based on Reaction Knowledge Graph

With the increasing application of deep-learning-based generative models for de novo molecule design, the quantitative estimation of molecular synthetic accessibility (SA) has become a crucial factor for prioritizing the structures generated from generative models. It is also useful for helping in the prioritization of hit/lead compounds and guiding retrosynthesis analysis. In this study, based on the USPTO and Pistachio reaction datasets, a chemical reaction network was constructed for the identification of the shortest reaction paths (SRP) needed to synthesize compounds, and different SRP cut-offs were then used as the threshold to distinguish a organic compound as either an easy-to-synthesize (ES) or hard-to-synthesize (HS) class. Two synthesis accessibility models (DNN-ECFP model and graph-based CMPNN model) were built using deep learning/machine learning algorithms. Compared to other existing synthesis accessibility scoring schemes, such as SYBA, SCScore, and SAScore, our results show that CMPNN (ROC AUC: 0.791) performs better than SYBA (ROC AUC: 0.76), albeit marginally, and outperforms SAScore and SCScore. Our prediction models based on historical reaction knowledge could be a potential tool for estimating molecule SA.

Legal obstacles to toxic chemical research

Legislative design impedes study of chemicals in the environment.

Is Organic Chemistry Really Growing Exponentially?

In terms of molecules and specific reaction examples, organic chemistry features an impressive, exponential growth. However, new reaction classes/types that fuel this growth are being discovered at a much slower and only linear (or even sublinear) rate. The proportion of newly discovered reaction types to all reactions being performed keeps decreasing, suggesting that synthetic chemistry becomes more reliant on reusing the well-known methods. The newly discovered chemistries are more complex than decades ago and allow for the rapid construction of complex scaffolds in fewer numbers of steps. We study these and other trends in the function of time, reaction-type popularity and complexity based on the algorithm that extracts generalized reaction class templates. These analyses are useful in the context of computer-assisted synthesis, machine learning (to estimate the numbers of models with sufficient reaction statistics), and identifying erroneous entries in reaction databases.

Artificial intelligence: A powerful paradigm for scientific research

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network

As safety is one of the most important properties of drugs, chemical toxicology prediction has received increasing attentions in the drug discovery research. Traditionally, researchers rely on in vitro and in vivo experiments to test the toxicity of chemical compounds. However, not only are these experiments time consuming and costly, but experiments that involve animal testing are increasingly subject to ethical concerns. While traditional machine learning (ML) methods have been used in the field with some success, the limited availability of annotated toxicity data is the major hurdle for further improving model performance. Inspired by the success of semi-supervised learning (SSL) algorithms, we propose a Graph Convolution Neural Network (GCN) to predict chemical toxicity and trained the network by the Mean Teacher (MT) SSL algorithm. Using the Tox21 data, our optimal SSL-GCN models for predicting the twelve toxicological endpoints achieve an average ROC-AUC score of 0.757 in the test set, which is a 6% improvement over GCN models trained by supervised learning and conventional ML methods. Our SSL-GCN models also exhibit superior performance when compared to models constructed using the built-in DeepChem ML methods. This study demonstrates that SSL can increase the prediction power of models by learning from unannotated data. The optimal unannotated to annotated data ratio ranges between 1:1 and 4:1. This study demonstrates the success of SSL in chemical toxicity prediction; the same technique is expected to be beneficial to other chemical property prediction tasks by utilizing existing large chemical databases. Our optimal model SSL-GCN is hosted on an online server accessible through: https://app.cbbio.online/ssl-gcn/home .

Chemical data intelligence for sustainable chemistry

This study highlights new opportunities for optimal reaction route selection from large chemical databases brought about by the rapid digitalisation of chemical data. The chemical industry requires a transformation towards more sustainable practices, eliminating its dependencies on fossil fuels and limiting its impact on the environment. However, identifying more sustainable process alternatives is, at present, a cumbersome, manual, iterative process, based on chemical intuition and modelling. We give a perspective on methods for automated discovery and assessment of competitive sustainable reaction routes based on renewable or waste feedstocks. Three key areas of transition are outlined and reviewed based on their state-of-the-art as well as bottlenecks: (i) data, (ii) evaluation metrics, and (iii) decision-making. We elucidate their synergies and interfaces since only together these areas can bring about the most benefit. The field of chemical data intelligence offers the opportunity to identify the inherently more sustainable reaction pathways and to identify opportunities for a circular chemical economy. Our review shows that at present the field of data brings about most bottlenecks, such as data completion and data linkage, but also offers the principal opportunity for advancement.

Utilizing a Biology-Driven Approach to Map the Exposome in Health and Disease: An Essential Investment to Drive the Next Generation of Environmental Discovery

BACKGROUND

Recent developments in technologies have offered opportunities to measure the exposome with unprecedented accuracy and scale. However, because most investigations have targeted only a few exposures at a time, it is hypothesized that the majority of the environmental determinants of chronic diseases remain unknown.

OBJECTIVES

We describe a functional exposome concept and explain how it can leverage existing bioassays and high-resolution mass spectrometry for exploratory study. We discuss how such an approach can address well-known barriers to interpret exposures and present a vision of next-generation exposomics.

DISCUSSION

The exposome is vast. Instead of trying to capture all exposures, we can reduce the complexity by measuring the functional exposome-the totality of the biologically active exposures relevant to disease development-through coupling biochemical receptor-binding assays with affinity purification-mass spectrometry. We claim the idea of capturing exposures with functional biomolecules opens new opportunities to solve critical problems in exposomics, including low-dose detection, unknown annotations, and complex mixtures of exposures. Although novel, biology-based measurement can make use of the existing data processing and bioinformatics pipelines. The functional exposome concept also complements conventional targeted and untargeted approaches for understanding exposure-disease relationships.

CONCLUSIONS

Although measurement technology has advanced, critical technological, analytical, and inferential barriers impede the detection of many environmental exposures relevant to chronic-disease etiology. Through biology-driven exposomics, it is possible to simultaneously scale up discovery of these causal environmental factors. https://doi.org/10.1289/EHP8327.

Bioactivity descriptors for uncharacterized chemical compounds

Chemical descriptors encode the physicochemical and structural properties of small molecules, and they are at the core of chemoinformatics. The broad release of bioactivity data has prompted enriched representations of compounds, reaching beyond chemical structures and capturing their known biological properties. Unfortunately, bioactivity descriptors are not available for most small molecules, which limits their applicability to a few thousand well characterized compounds. Here we present a collection of deep neural networks able to infer bioactivity signatures for any compound of interest, even when little or no experimental information is available for them. Our signaturizers relate to bioactivities of 25 different types (including target profiles, cellular response and clinical outcomes) and can be used as drop-in replacements for chemical descriptors in day-to-day chemoinformatics tasks. Indeed, we illustrate how inferred bioactivity signatures are useful to navigate the chemical space in a biologically relevant manner, unveiling higher-order organization in natural product collections, and to enrich mostly uncharacterized chemical libraries for activity against the drug-orphan target Snail1. Moreover, we implement a battery of signature-activity relationship (SigAR) models and show a substantial improvement in performance, with respect to chemistry-based classifiers, across a series of biophysics and physiology activity prediction benchmarks.

Statistics of the Popularity of Chemical Compounds in Relation to the Non-Target Analysis

The idea of popularity/abundance of chemical compounds is widely used in non-target chemical analysis involving environmental studies. To have a clear quantitative basis for this idea, frequency distributions of chemical compounds over indicators of their popularity/abundance are obtained and discussed. Popularity indicators are the number of information sources, the number of chemical vendors, counts of data records, and other variables assessed from two large databases, namely ChemSpider and PubChem. Distributions are approximated by power functions, special cases of Zipf distributions, which are characteristic of the results of human/social activity. Relatively small group of the most popular compounds has been denoted, conventionally accounting for a few percent (several million) of compounds. These compounds are most often explored in scientific research and are practically used. Accordingly, popular compounds have been taken into account as first analyte candidates for identification in non-target analysis.

Understanding Periodic and Non-periodic Chemistry in Periodic Tables

The chemical elements are the "conserved principles" or "kernels" of chemistry that are retained when substances are altered. Comprehensive overviews of the chemistry of the elements and their compounds are needed in chemical science. To this end, a graphical display of the chemical properties of the elements, in the form of a Periodic Table, is the helpful tool. Such tables have been designed with the aim of either classifying real chemical substances or emphasizing formal and aesthetic concepts. Simplified, artistic, or economic tables are relevant to educational and cultural fields, while practicing chemists profit more from "chemical tables of chemical elements." Such tables should incorporate four aspects: (i) typical valence electron configurations of bonded atoms in chemical compounds (instead of the common but chemically atypical ground states of free atoms in physical vacuum); (ii) at least three basic chemical properties (valence number, size, and energy of the valence shells), their joint variation across the elements showing principal and secondary periodicity; (iii) elements in which the (sp)8, (d)10, and (f)14 valence shells become closed and inert under ambient chemical conditions, thereby determining the "fix-points" of chemical periodicity; (iv) peculiar elements at the top and at the bottom of the Periodic Table. While it is essential that Periodic Tables display important trends in element chemistry we need to keep our eyes open for unexpected chemical behavior in ambient, near ambient, or unusual conditions. The combination of experimental data and theoretical insight supports a more nuanced understanding of complex periodic trends and non-periodic phenomena.

Aminations and arylations by direct C–O activation for the design of 7,8-dihydro-6H-5,8-ethanopyrido[3,2-d]pyrimidines

The design of some novel disubstituted 7,8-dihydro-6H-5,8-ethanopyrido[3,2-d]pyrimidine derivatives is reported. The series was developed from quinuclidinone, which afforded versatile platforms bearing one lactam function in position C-2 that were then used to create C–N or C–C bonds for S_NAr or palladium-catalyzed cross-coupling reactions by in situ C–O activation. The reaction conditions were optimized under microwave irradiation, and a wide range of amines or boronic acids were used to determine the scope and limitations of each method. To complete this study, the X-ray crystallographic data of 7,8-dihydro-6H-5,8-ethanopyrido[3,2-d]pyrimidine derivative 49 were used to formally establish the structures of the products.

A convenient design of 7,8-dihydro-6H-5,8-ethanopyrido[3,2-d]pyrimidines is reported from quinuclidinone, which afforded versatile platforms that were then used to access a variety of original heterocycles.

Uncertainty quantification in drug design

Machine learning and artificial intelligence are increasingly being applied to the drug-design process as a result of the development of novel algorithms, growing access, the falling cost of computation and the development of novel technologies for generating chemically and biologically relevant data. There has been recent progress in fields such as molecular de novo generation, synthetic route prediction and, to some extent, property predictions. Despite this, most research in these fields has focused on improving the accuracy of the technologies, rather than on quantifying the uncertainty in the predictions. Uncertainty quantification will become a key component in autonomous decision making and will be crucial for integrating machine learning and chemistry automation to create an autonomous design-make-test-analyse cycle. This review covers the empirical, frequentist and Bayesian approaches to uncertainty quantification, and outlines how they can be used for drug design. We also outline the impact of uncertainty quantification on decision making.

Drug Mimicry: Promiscuous Receptors PXR and AhR, and Microbial Metabolite Interactions in the Intestine

Significant attrition limits drug discovery. The available chemical entities present with drug-like features contribute to this limitation. Using specific examples of promiscuous receptor-ligand interactions, a case is made for expanding the chemical space for drug-like molecules. These ligand-receptor interactions are poor candidates for the drug discovery process. However, provided herein are specific examples of ligand-receptor or transcription-factor interactions, namely, the pregnane X receptor (PXR) and the aryl hydrocarbon receptor (AhR), and itsinteractions with microbial metabolites. Discrete examples of microbial metabolite mimicry are shown to yield more potent and non-toxic therapeutic leads for pathophysiological conditions regulated by PXR and AhR. These examples underscore the opinion that microbial metabolite mimicry of promiscuous ligand-receptor interactions is warranted, and will likely expand the existing chemical space of drugs.

Comprehensive Prediction of Molecular Recognition in a Combinatorial Chemical Space Using Machine Learning

In combinatorial chemical approaches, optimizing the composition and arrangement of building blocks toward a particular function has been done using a number of methods, including high throughput molecular screening, molecular evolution, and computational prescreening. Here, a different approach is considered that uses sparse measurements of library molecules as the input to a machine learning algorithm which generates a comprehensive, quantitative relationship between covalent molecular structure and function that can then be used to predict the function of any molecule in the possible combinatorial space. To test the feasibility of the approach, a defined combinatorial chemical space consisting of ∼10¹² possible linear combinations of 16 different amino acids was used. The binding of a very sparse, but nearly random, sampling of this amino acid sequence space to 9 different protein targets is measured and used to generate a general relationship between peptide sequence and binding for each target. Surprisingly, measuring as little as a few hundred to a few thousand of the ∼10¹² possible molecules provides sufficient training to be highly predictive of the binding of the remaining molecules in the combinatorial space. Furthermore, measuring only amino acid sequences that bind weakly to a target allows the accurate prediction of which sequences will bind 10-100 times more strongly. Thus, the molecular recognition information contained in a tiny fraction of molecules in this combinatorial space is sufficient to characterize any set of molecules randomly selected from the entire space, a fact that potentially has significant implications for the design of new chemical function using combinatorial chemical libraries.

SYBA: Bayesian estimation of synthetic accessibility of organic compounds

SYBA (SYnthetic Bayesian Accessibility) is a fragment-based method for the rapid classification of organic compounds as easy- (ES) or hard-to-synthesize (HS). It is based on a Bernoulli naïve Bayes classifier that is used to assign SYBA score contributions to individual fragments based on their frequencies in the database of ES and HS molecules. SYBA was trained on ES molecules available in the ZINC15 database and on HS molecules generated by the Nonpher methodology. SYBA was compared with a random forest, that was utilized as a baseline method, as well as with other two methods for synthetic accessibility assessment: SAScore and SCScore. When used with their suggested thresholds, SYBA improves over random forest classification, albeit marginally, and outperforms SAScore and SCScore. However, upon the optimization of SAScore threshold (that changes from 6.0 to – 4.5), SAScore yields similar results as SYBA. Because SYBA is based merely on fragment contributions, it can be used for the analysis of the contribution of individual molecular parts to compound synthetic accessibility. SYBA is publicly available at https://github.com/lich-uct/syba under the GNU General Public License.

BESTox: A Convolutional Neural Network Regression Model Based on Binary-Encoded SMILES for Acute Oral Toxicity Prediction of Chemical Compounds

Compound toxicity prediction is a very challenging and critical task in the drug discovery and design field. Traditionally, cell or animal-based experiments are required to confirm the acute oral toxicity of chemical compounds. However, these methods are often restricted by availability of experimental facilities, long experimentation time, and high cost. In this paper, we propose a novel convolutional neural network regression model, named BESTox, to predict the acute oral toxicity (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$LD_{50}$$\end{document}) of chemical compounds. This model learns the compositional and chemical properties of compounds from their two-dimensional binary matrices. Each matrix encodes the occurrences of certain atom types, number of bonded hydrogens, atom charge, valence, ring, degree, aromaticity, chirality, and hybridization along the SMILES string of a given compound. In a benchmark experiment using a dataset of 7413 observations (train/test 5931/1482), BESTox achieved a squared correlation coefficient (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^2$$\end{document}) of 0.619, root-mean-squared error (RMSE) of 0.603, and mean absolute error (MAE) of 0.433. Despite of the use of a shallow model architecture and simple molecular descriptors, our method performs comparably against two recently published models.

Does repeat synthesis in materials chemistry obey a power law?

Finding examples where experimental measurements have been repeated is a powerful strategy for assessing reproducibility of scientific data. Here, we collect quantitative data to assess how often synthesis of a newly reported material is repeated in the scientific literature. We present a simple power-law model for the frequency of repeat syntheses and assess the validity of this model using a specific class of materials, metal-organic frameworks (MOFs). Our data suggest that a power law describes the frequency of repeat synthesis of many MOFs but that a small number of "supermaterials" exist that have been replicated many times more than a power law would predict. Our results also hint that there are many repeat syntheses that have been performed but not reported in the literature, which suggests simple steps that could be taken to greatly increase the number of reports of replicate experiments in materials chemistry.

Physical origin of chemical periodicities in the system of elements

The Periodic Law, one of the great discoveries in human history, is magnificent in the art of chemistry. Different arrangements of chemical elements in differently shaped Periodic Tables serve for different purposes. “Can this Periodic Table be derived from quantum chemistry or physics?” can only be answered positively, if the internal structure of the Periodic Table is explicitly connected to facts and data from chemistry. Quantum chemical rationalization of such a Periodic Tables is achieved by explaining the details of energies and radii of atomic core and valence orbitals in the leading electron configurations of chemically bonded atoms. The coarse horizontal pseudo-periodicity in seven rows of 2, 8, 8, 18, 18, 32, 32 members is triggered by the low energy of and large gap above the 1s and nsp valence shells (2 ≤ n ≤ 6 !). The pseudo-periodicity, in particular the wavy variation of the elemental properties in the four longer rows, is due to the different behaviors of the s and p vs. d and f pairs of atomic valence shells along the ordered array of elements. The so-called secondary or vertical periodicity is related to pseudo-periodic changes of the atomic core shells. The Periodic Law of the naturally given System of Elements describes the trends of the many chemical properties displayed inside the Chemical Periodic Tables. While the general physical laws of quantum mechanics form a simple network, their application to the unlimited field of chemical materials under ambient ‘human’ conditions results in a complex and somewhat accidental structure inside the Table that fits to some more or less symmetric outer shape. Periodic Tables designed after some creative concept for the overall appearance are of interest in non-chemical fields of wisdom and art.

Challenges for the Periodic Systems of Elements: Chemical, Historical and Mathematical Perspectives

We celebrate 150 years of periodic systems that reached their maturity in the 1860s. They began as pedagogical efforts to project corpuses of substances on the similarity and order relationships of the chemical elements. However, these elements are not the canned substances wrongly displayed in many periodic tables, but rather the abstract preserved entities in compound transformations. We celebrate the systems, rather than their tables or ultimate table. The periodic law, we argue, is not an all-encompassing achievement, as it does not apply to every property of all elements and compounds. Periodic systems have been generalised as ordered hypergraphs, which solves the long-lasting question on the mathematical structure of the systems. In this essay, it is shown that these hypergraphs may solve current issues such as order reversals in super-heavy elements and lack of system predictive power. We discuss research in extending the limits of the systems in the super-heavy-atom region and draw attention to other limits: the antimatter region and the limit arising from compounds under extreme conditions. As systems depend on the known chemical substances (chemical space) and such a space grows exponentially, we wonder whether systems still aim at projecting knowledge of compounds on the relationships among the elements. We claim that systems are not based on compounds anymore, rather on 20th century projections of the 1860s systems of elements on systems of atoms. These projections bring about oversimplifications based on entities far from being related to compounds. A linked oversimplification is the myth of vertical group similarity, which raises questions on the approaches to locate new elements in the system. Finally, we propose bringing back chemistry to the systems by exploring similarity and order relationships of elements using the current information of the chemical space. We ponder whether 19th century periodic systems are still there or whether they have faded away, leaving us with an empty 150^th celebration.