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The exponential growth of data is challenging for humans because their ability to analyze data is limited. Especially in chemistry, there is a demand for tools that can visualize molecular datasets in a convenient graphical way. We propose a new, ready-to-use, multi-tool, and open-source framework for visualizing and navigating chemical space. This framework adheres to the low-code/no-code (LCNC) paradigm, providing a KNIME node, a web-based tool, and a Python package, making it accessible to a broad cheminformatics community. The core technique of the MolCompass framework employs a pre-trained parametric t-SNE model. We demonstrate how this framework can be adapted for the visualisation of chemical space and visual validation of binary classification QSAR/QSPR models, revealing their weaknesses and identifying model cliffs. All parts of the framework are publicly available on GitHub, providing accessibility to the broad scientific community. Scientific contribution We provide an open-source, ready-to-use set of tools for the visualization of chemical space. These tools can be insightful for chemists to analyze compound datasets and for the visual validation of QSAR/QSPR models. [ABSTRACT FROM AUTHOR]

The organic anion transporters OAT1 (SLC22A6) and OAT3 (SLC22A8) are drug transporters that are expressed in the kidney, with well-established roles in the in vivo transport of drugs and endogenous metabolites. A comparatively unexplored potential function of these drug transporters is their contribution to the in vivo regulation of natural products (NPs) and their effects on endogenous metabolism. This is important for the evaluation of potential NP interactions with other compounds at the transporter site. Here, we have analyzed the NPs present in several well-established databases from Asian (Chinese, Indian Ayurvedic) and other traditions. Loss of OAT1 and OAT3 in murine knockouts caused serum alterations of many NPs, including flavonoids, vitamins, and indoles. OAT1- and OAT3-dependent NPs were largely separable based on a multivariate analysis of chemical properties. Direct binding to the transporter was confirmed using in vitro transport assays and protein binding assays. Our in vivo and in vitro results, considered in the context of previous data, demonstrate that OAT1 and OAT3 play a pivotal role in the handling of non-synthetic small molecule natural products, NP-derived antioxidants, phytochemicals, and nutrients (e.g., pantothenic acid, thiamine). As described by remote sensing and signaling theory, drug transporters help regulate redox states by meditating the movement of endogenous antioxidants and nutrients between organs and organisms. Our results demonstrate how dietary antioxidants and other NPs might feed into these inter-organ and inter-organismal pathways. [ABSTRACT FROM AUTHOR]

Designing and developing inhibitors against the epigenetic target DNA methyltransferase (DNMT) is an attractive strategy in epigenetic drug discovery. DNMT1 is one of the epigenetic enzymes with significant clinical relevance. Structure-based de novo design is a drug discovery strategy that was used in combination with similarity searching to identify a novel DNMT inhibitor with a novel chemical scaffold and warrants further exploration. This study aimed to continue exploring the potential of de novo design to build epigenetic-focused libraries targeted toward DNMT1. Herein, we report the results of an in-depth and critical comparison of ligand- and structure-based de novo design of screening libraries focused on DNMT1. The newly designed chemical libraries focused on DNMT1 are freely available on GitHub. [ABSTRACT FROM AUTHOR]

Compound databases of natural products play a crucial role in drug discovery and development projects and have implications in other areas, such as food chemical research, ecology and metabolomics. Recently, we put together the first version of the Latin American Natural Product database (LANaPDB) as a collective effort of researchers from six countries to ensemble a public and representative library of natural products in a geographical region with a large biodiversity. The present work aims to conduct a comparative and extensive profiling of the natural product‐likeness of an updated version of LANaPDB and the individual ten compound databases that form part of LANaPDB. The natural product‐likeness profile of the Latin American compound databases is contrasted with the profile of other major natural product databases in the public domain and a set of small‐molecule drugs approved for clinical use. As part of the extensive characterization, we employed several chemoinformatics metrics of natural product likeness. The results of this study will capture the attention of the global community engaged in natural product databases, not only in Latin America but across the world. [ABSTRACT FROM AUTHOR]

Abstract The exponential growth of data is challenging for humans because their ability to analyze data is limited. Especially in chemistry, there is a demand for tools that can visualize molecular datasets in a convenient graphical way. We propose a new, ready-to-use, multi-tool, and open-source framework for visualizing and navigating chemical space. This framework adheres to the low-code/no-code (LCNC) paradigm, providing a KNIME node, a web-based tool, and a Python package, making it accessible to a broad cheminformatics community. The core technique of the MolCompass framework employs a pre-trained parametric t-SNE model. We demonstrate how this framework can be adapted for the visualisation of chemical space and visual validation of binary classification QSAR/QSPR models, revealing their weaknesses and identifying model cliffs. All parts of the framework are publicly available on GitHub, providing accessibility to the broad scientific community. Scientific contribution We provide an open-source, ready-to-use set of tools for the visualization of chemical space. These tools can be insightful for chemists to analyze compound datasets and for the visual validation of QSAR/QSPR models.

Although big data is publicly available on water quality parameters, virtual simulation has not yet been adequately adapted in environmental chemistry research. Digital twin is different from conventional geospatial modeling approaches and is particularly useful when systematic laboratory/field experiment is not realistic (e.g., climate impact and water-related environmental catastrophe) or difficult to design and monitor in a real time (e.g., pollutant and nutrient cycles in estuaries, soils, and sediments). Data-driven water research could realize early warning and disaster readiness simulations for diverse environmental scenarios, including drinking water contamination.

The placenta is a membrane that separates the fetus from the maternal circulation, and in addition to protecting the fetus, plays a key role in fetal growth and development. With increasing drug use in pregnancy, it is imperative that reliable models of estimating placental permeability and safety be established. In vitro methods and animal models such as rodent placenta are limited in application since the species-specific nature of the placenta prevents meaningful extrapolations to humans. In this regard, in silico approaches such as quantitative structure-property relationships (QSPRs) are useful alternatives. However, despite evidence that drug transport across the placenta is stereoselective (i.e., governed by the spatial arrangement of the atoms in a molecule), many QSPR models for placental transfer have been built using 2D descriptors that do not account for chirality and stereochemistry. In this study, we apply a chirality-sensitive and proven QSPR methodology titled "EigenValue ANalySis" (EVANS) to build QSPR models for placental transfer. We deploy EVANS along with robust machine learning algorithms to build (i) regression models on a dataset of environmental chemicals (dataset PD I) followed by (ii) classification models on a set of drug-like compounds (dataset PD II). The best models were found to achieve state-of-the-art performance, with the support vector machine algorithm returning r train 2 = 0.85 , r test 2 = 0.75 for PD I, and the logistic regression algorithm giving accuracy 0.88 and F1 score 0.93 for PD II. The best models were interpreted with the Shapley Additive Explanations paradigm, and it was found that autocorrelation descriptors are crucial for modelling placental permeability. In conclusion, we demonstrate the need of a chirality-sensitive approach for modelling placental transfer of chemicals, and present two predictive QSPR models that may reliably be used for prediction of placental transfer. • A chirality-sensitive methodology titled "EigenValue ANalySis" (EVANS) is applied to build QSPR models for placental transfer • SVM regression models on a dataset of environmental chemicals returned r train 2 = 0.85 , r test 2 = 0.75 • Logistic regression models on a set of drug-like compounds had accuracy 0.88 and F1 score 0.93 • We demonstrate the need of a chirality-sensitive approach for modelling placental transfer of chemicals [ABSTRACT FROM AUTHOR]

The nonstructural proteins 7 and 8 (nsp7 and nsp8) of SARS-CoV-2 are highly important proteins involved in the RNA-dependent polymerase (RdRp) protein replication complex. In this study, we analyzed the global mutation of nsp7 and nsp8 in 2022 and 2023 and analyzed the effects of mutation on the viral replication protein complex using bio-chemoinformatics. Frequently occurring variants are found to be single amino acid mutations for both nsp7 and nsp8. The most frequently occurring mutations for nsp7 which include L56F, L71F, S25L, M3I, D77N, V33I and T83I are predicted to cause destabilizing effects, whereas those in nsp8 are predicted to cause stabilizing effects, with the threonine to isoleucine mutation (T89I, T145I, T123I, T148I, T187I) being a frequent mutation. A conserved domain database analysis generated critical interaction residues for nsp7 (Lys-7, His-36 and Asn-37) and nsp8 (Lys-58, Pro-183 and Arg-190), which, according to thermodynamic calculations, are prone to destabilization. Trp-29, Phe-49 of nsp7 and Trp-154, Tyr-135 and Phe-15 of nsp8 cause greater destabilizing effects to the protein complex based on a computational alanine scan suggesting them as possible new target sites. This study provides an intensive analysis of the mutations of nsp7 and nsp8 and their possible implications for viral complex stability.

Machine learning models have found numerous successful applications in computational drug discovery. A large body of these models represents molecules as sequences since molecular sequences are easily available, simple, and informative. The sequence‐based models often segment molecular sequences into pieces called chemical words, analogous to the words that make up sentences in human languages, and then apply advanced natural language processing techniques for tasks such as de novo drug design, property prediction, and binding affinity prediction. However, the chemical characteristics and significance of these building blocks, chemical words, remain unexplored. To address this gap, we employ data‐driven SMILES tokenization techniques such as Byte Pair Encoding, WordPiece, and Unigram to identify chemical words and compare the resulting vocabularies. To understand the chemical significance of these words, we build a language‐inspired pipeline that treats high affinity ligands of protein targets as documents and selects key chemical words making up those ligands based on tf–idf weighting. The experiments on multiple protein–ligand affinity datasets show that despite differences in words, lengths, and validity among the vocabularies generated by different subword tokenization algorithms, the identified key chemical words exhibit similarity. Further, we conduct case studies on a number of target to analyze the impact of key chemical words on binding. We find that these key chemical words are specific to protein targets and correspond to known pharmacophores and functional groups. Our approach elucidates chemical properties of the words identified by machine learning models and can be used in drug discovery studies to determine significant chemical moieties. [ABSTRACT FROM AUTHOR]

Virtual screening of large chemical libraries is essential to support computer-aided drug development, providing a rapid and low-cost approach for further experimental validation. However, existing computational packages are often for specialised users or platform limited. Previously, we developed VSpipe, an open-source semi-automated pipeline for structure-based virtual screening. We have now improved and expanded the initial command-line version into an interactive graphical user interface: VSpipe-GUI, a cross-platform open-source Python toolkit functional in various operating systems (e.g., Linux distributions, Windows, and Mac OS X). The new implementation is more user-friendly and accessible, and considerably faster than the previous version when AutoDock Vina is used for docking. Importantly, we have introduced a new compound selection module (i.e., spatial filtering) that allows filtering of docked compounds based on specified features at the target binding site. We have tested the new VSpipe-GUI on the Hepatitis C Virus NS3 (HCV NS3) protease as the target protein. The pocket-based and interaction-based modes of the spatial filtering module showed efficient and specific selection of ligands from the virtual screening that interact with the HCV NS3 catalytic serine 139. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 Main Protease (Mpro) is an enzyme that cleaves viral polyproteins translated from the viral genome, which is critical for viral replication. Mpro is a target for anti-SARS-CoV-2 drug development. Herein, we performed a large-scale virtual screening by comparing multiple structural descriptors of reference molecules with reported anti-coronavirus activity against a library with >17 million compounds. Further filtering, performed by applying two machine learning algorithms, identified eighteen computational hits as anti-SARS-CoV-2 compounds with high structural diversity and drug-like properties. The activities of twelve compounds on Mpro's enzymatic activity were evaluated by fluorescence resonance energy transfer (FRET) assays. Compound 13 (ZINC13878776) significantly inhibited SARS-CoV-2 Mpro activity and was employed as a reference for an experimentally hit expansion. The structural analogues 13a (ZINC4248385), 13b (ZNC13523222), and 13c (ZINC4248365) were tested as Mpro inhibitors, reducing the enzymatic activity of recombinant Mpro with potency as follows: 13c > 13 > 13b > 13a. Then, their anti-SARS-CoV-2 activities were evaluated in plaque reduction assays using Vero CCL81 cells. Subtoxic concentrations of compounds 13a, 13c, and 13b displayed in vitro antiviral activity with IC50 in the mid micromolar range. Compounds 13a–c could become lead compounds for the development of new Mpro inhibitors with improved activity against anti-SARS-CoV-2. [ABSTRACT FROM AUTHOR]

We present a drug design strategy based on structural knowledge of protein-protein interfaces selected through virus-host coevolution and translated into highly potential small molecules. This approach is grounded on Vinland, the most comprehensive atlas of virus-human protein-protein interactions with annotation of interacting domains. From this inspiration, we identified small viral protein domains responsible for interaction with human proteins. These peptides form a library of new chemical entities used to screen for replication modulators of several pathogens. As a proof of concept, a peptide from a KSHV protein, identified as an inhibitor of influenza virus replication, was translated into a small molecule series with low nanomolar antiviral activity. By targeting the NEET proteins, these molecules turn out to be of therapeutic interest in a nonalcoholic steatohepatitis mouse model with kidney lesions. This study provides a biomimetic framework to design original chemistries targeting cellular proteins, with indications going far beyond infectious diseases. [ABSTRACT FROM AUTHOR]

Phenylboronic acids (PBAs) form stable complexes with fluoride. The effect of type (methoxy-, formyl-, and acetyl-) and position (ortho-, meta-, and para-) of electron-donating substitutions on the hydrolytic stability and acidity of PBAs, as well as their spectroscopic and physicochemical properties and their usage in spectrofluorimetric fluoride determination, were investigated. Thermal stabilities, relative predisposition, and resistance to dehydroboronation of related PBA isomers were investigated in detail and compared with the use of thermogravimetric analysis and differential scanning calorimetry profiles. Dehydroboronation reaction leads to the synthesis of related new cyclic anhydric forms — specifically named boroxines — which are clearly distinguished by Fourier transform infrared spectroscopy. PBAs were used for the complexation of fluoride for spectrofluorimetric fluoride determination in dental formulations [toothpastes (TPs) and mouth rinses (MRs)] and beverages [mineral waters (MiWs)]. Determination was done by fluorescence quenching of PBAs in response to increasing fluoride concentration. The regression equation was y = –15.336x + 983.17 (R2 = 0.9931), and was linear in the 1.4–3.0-mM range. Determinations were performed with relative errors (%) in a range of –5.60 to +1.23, –2.01 to +5.69, and –4.16 to +2.54 for MRs, TPs, and MiWs, respectively, relative to fluoride levels of commercial samples. Chemometric analyses (cluster analysis, CA, principal component analysis, PCA) were performed on the same real samples. Raw fluorescence data was investigated by PCA to check their significance in chemometric discrimination. Dendograms and score plots successfully discriminated samples in related groups. This is the first demonstration of spectrofluorometric fluoride determination based on the quenching of related isomers of PBAs thus far, also the potential of raw fluorescence data of these PBAs for chemometric discrimination studies on related pharmaceutical samples was highlighted for the first time. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery. [ABSTRACT FROM AUTHOR]

Introduction: We developed Data Base similarity (DBsimilarity), a user‐friendly tool designed to organize structure databases into similarity networks, with the goal of facilitating the visualization of information primarily for natural product chemists who may not have coding experience. Method: DBsimilarity, written in Jupyter Notebooks, converts Structure Data File (SDF) files into Comma‐Separated Values (CSV) files, adds chemoinformatics data, constructs an MZMine custom database file and an NMRfilter candidate list of compounds for rapid dereplication of MS and 2D NMR data, calculates similarities between compounds, and constructs CSV files formatted into similarity networks for Cytoscape. Results: The Lotus database was used as a source for Ginkgo biloba compounds, and DBsimilarity was used to create similarity networks including NPClassifier classification to indicate biosynthesis pathways. Subsequently, a database of validated antibiotics from natural products was combined with the G. biloba compounds to identify promising compounds. The presence of 11 compounds in both datasets points to possible antibiotic properties of G. biloba, and 122 compounds similar to these known antibiotics were highlighted. Next, DBsimilarity was used to filter the NPAtlas database (selecting only those with MIBiG reference) to identify potential antibacterial compounds using the ChEMBL database as a reference. It was possible to promptly identify five compounds found in both databases and 167 others worthy of further investigation. Conclusion: Chemical and biological properties are determined by molecular structures. DBsimilarity enables the creation of interactive similarity networks using Cytoscape. It is also in line with a recent review that highlights poor biological plausibility and unrealistic chromatographic behaviors as significant sources of errors in compound identification. Introducing DBsimilarity: a user‐friendly tool leveraging Jupyter Notebooks to organize structural databases into similarity networks. This innovative approach aids natural product chemists, even without coding skills, by converting Structure Data File (SDF) files to Comma‐Separated Values (CSV), facilitating dereplication, calculating compound similarities, and generating Cytoscape‐compatible networks. The tool's application is showcased through Ginkgo biloba compound analysis, uncovering potential antibiotic candidates via cross‐database correlations. Furthermore, DBsimilarity efficiently filters NPAtlas using ChEMBL as a reference, unveiling numerous antibacterial leads for exploration. [ABSTRACT FROM AUTHOR]

Molecules are complex dynamic objects that can exist in different molecular forms (conformations, tautomers, stereoisomers, protonation states, etc.) and often it is not known which molecular form is responsible for observed physicochemical and biological properties of a given molecule. This raises the problem of the selection of the correct molecular form for machine learning modeling of target properties. The same problem is common to biological molecules (RNA, DNA, proteins)—long sequences where only key segments, which often cannot be located precisely, are involved in biological functions. Multi‐instance machine learning (MIL) is an efficient approach for solving problems where objects under study cannot be uniquely represented by a single instance, but rather by a set of multiple alternative instances. Multi‐instance learning was formalized in 1997 and motivated by the problem of conformation selection in drug activity prediction tasks. Since then MIL has found a lot of applications in various domains, such as information retrieval, computer vision, signal processing, bankruptcy prediction, and so on. In the given review we describe the MIL framework and its applications to the tasks associated with ambiguity in the representation of small and biological molecules in chemoinformatics and bioinformatics. We have collected examples that demonstrate the advantages of MIL over the traditional single‐instance learning (SIL) approach. Special attention was paid to the ability of MIL models to identify key instances responsible for a modeling property. This article is categorized under:Data Science > ChemoinformaticsData Science > Artificial Intelligence/Machine Learning [ABSTRACT FROM AUTHOR]

In chemoinformatics, artificial intelligence (AI) continues to grow a symbiosis with open science (OS). Such a close AI-OS interaction brings substantial practical benefits in research, scientific dissemination, and education, to name a few areas. The AI-OS symbiosis can be further enhanced by combining sufficient substantive expertise, mathematical and statistical knowledge, and coding skills. This Viewpoint discusses the benefits of the smooth and productive interaction between AI, OS, and open data. We also present a short list of misconceptions and pitfalls surrounding AI-OS and propose correct responses and behaviors agreed upon by field experts. In addition, we provide suggestions to continue enhancing the positive contributions of the AI-OS symbiosis towards chemoinformatics.

Abstract Science and art have been connected for centuries. With the development of new computational methods, new scientific disciplines have emerged, such as computational chemistry, and related fields, such as cheminformatics. Chemoinformatics is grounded on the chemical space concept: a multi-descriptor space in which chemical structures are described. In several practical applications, visual representations of the chemical space of compound datasets are low-dimensional plots helpful in identifying patterns. However, the authors propose that the plots can also be used as artistic expressions. This manuscript introduces an approach to merging art with chemoinformatics through visual and artistic representations of chemical space. As case studies, we portray the chemical space of food chemicals and other compounds to generate visually appealing graphs with twofold benefits: sharing chemical knowledge and developing pieces of art driven by chemoinformatics. The art driven by chemical space visualization will help increase the application of chemistry and art and contribute to general education and dissemination of chemoinformatics and chemistry through artistic expressions. All the code and data sets to reproduce the visual representation of the chemical space presented in the manuscript are freely available at https://github.com/DIFACQUIM/Art-Driven-by-Visual-Representations-of-Chemical-Space- . Scientific contribution: Chemical space as a concept to create digital art and as a tool to train and introduce students to cheminformatics. Graphical Abstract

Peripheral venous hypertension has emerged as a prominent characteristic of venous disease (VD). This disease causes lower limb edema due to impaired blood transport in the veins. The phlebotonic drugs in use showed moderate evidence for reducing edema slightly in the lower legs and little or no difference in the quality of life. To enhance the probability of favorable experimental results, a virtual screening procedure was employed to identify molecules with potential therapeutic activity in VD. Compounds obtained from multiple databases, namely AC Discovery, NuBBE, BIOFACQUIM, and InflamNat, were compared with reference compounds. The examination of structural similarity, targets, and signaling pathways in venous diseases allows for the identification of compounds with potential usefulness in VD. The computational tools employed were rcdk and chemminer from R-Studio and Cytoscape. An extended fingerprint analysis allowed us to obtain 1846 from 41,655 compounds compiled. Only 229 compounds showed pharmacological targets in the PubChem server, of which 84 molecules interacted with the VD network. Because of their descriptors and multi-target capacity, only 18 molecules of 84 were identified as potential candidates for experimental evaluation. We opted to evaluate the berberine compound because of its affordability, and extensive literature support. The experiment showed the proposed activity in an acute venous hypertension model. [ABSTRACT FROM AUTHOR]

Science and art have been connected for centuries. With the development of new computational methods, new scientific disciplines have emerged, such as computational chemistry, and related fields, such as cheminformatics. Chemoinformatics is grounded on the chemical space concept: a multi-descriptor space in which chemical structures are described. In several practical applications, visual representations of the chemical space of compound datasets are low-dimensional plots helpful in identifying patterns. However, the authors propose that the plots can also be used as artistic expressions. This manuscript introduces an approach to merging art with chemoinformatics through visual and artistic representations of chemical space. As case studies, we portray the chemical space of food chemicals and other compounds to generate visually appealing graphs with twofold benefits: sharing chemical knowledge and developing pieces of art driven by chemoinformatics. The art driven by chemical space visualization will help increase the application of chemistry and art and contribute to general education and dissemination of chemoinformatics and chemistry through artistic expressions. All the code and data sets to reproduce the visual representation of the chemical space presented in the manuscript are freely available at https://github.com/DIFACQUIM/Art-Driven-by-Visual-Representations-of-Chemical-Space-.Scientific contribution: Chemical space as a concept to create digital art and as a tool to train and introduce students to cheminformatics. [ABSTRACT FROM AUTHOR]

The number of databases of natural products (NPs) has increased substantially. Latin America is extraordinarily rich in biodiversity, enabling the identification of novel NPs, which has encouraged both the development of databases and the implementation of those that are being created or are under development. In a collective effort from several Latin American countries, herein we introduce the first version of the Latin American Natural Products Database (LANaPDB), a public compound collection that gathers the chemical information of NPs contained in diverse databases from this geographical region. The current version of LANaPDB unifies the information from six countries and contains 12,959 chemical structures. The structural classification showed that the most abundant compounds are the terpenoids (63.2%), phenylpropanoids (18%) and alkaloids (11.8%). From the analysis of the distribution of properties of pharmaceutical interest, it was observed that many LANaPDB compounds satisfy some drug-like rules of thumb for physicochemical properties. The concept of the chemical multiverse was employed to generate multiple chemical spaces from two different fingerprints and two dimensionality reduction techniques. Comparing LANaPDB with FDA-approved drugs and the major open-access repository of NPs, COCONUT, it was concluded that the chemical space covered by LANaPDB completely overlaps with COCONUT and, in some regions, with FDA-approved drugs. LANaPDB will be updated, adding more compounds from each database, plus the addition of databases from other Latin American countries. [ABSTRACT FROM AUTHOR]

In Malawi, ethnobotanical surveys have been carried out on the plants used to treat or manage allergies and their symptoms. However, studies focusing on information analysis such as systematic reviews of anti-allergic plants and natural products are not yet published. This review highlighted and analysed data of potential anti-allergic medicinal plants used in Malawi in order to discover potential treatments for allergies through various approaches and improve well-being of people. Literature search strategy was used to collect data of medicinal plants. Chemoinformatics analysis was applied to compile associated natural products and to profile the physicochemical properties (drug-likeness and fingerprint diversity) with references to 60 antihistamine 1 drugs (H1R drugs) and 277 anti-H1 receptor inhibitors. Sixty (60) medicinal plants from 36 families out of 645 reported plant species had information of diseases or symptoms of allergies. The Malawian Natural Products (MNP) data set composed of 1757 unique compounds was compiled for the first time from 39 of 60 medicinal plants and approximately 48.9 % and 45.3 % of the compounds did not violate Lipinski and Veber drug-like rules respectively. Chemical space mapping revealed that compounds from Malawian medicinal plants are structurally diverse and share a small space with antihistamine compounds and drugs. This information is useful in exploring other anti-allergy biological mechanisms for natural products. Similarity screening identified usambarine (CID442121), which is structurally similar to 2,8-Dimethyl-5-(2-pyrazine-2-ylethyl)-gamma-carboline (CHEMBL1783970) and 29 other antihistamine compounds. The results will be used as a baseline for further studies which may eventually lead to the discovery of potential allergy treatments through various approaches. This could improve health and well-being of people which is in line with goal number 3 of Sustainable Development Goals, Africa's Union's Agenda 2063 and National Research Agenda for the Malawi 2063.

Designing and developing inhibitors against the epigenetic target DNA methyltransferase (DNMT) is an attractive strategy in epigenetic drug discovery. DNMT1 is one of the epigenetic enzymes with significant clinical relevance. Structure-based de novo design is a drug discovery strategy that was used in combination with similarity searching to identify a novel DNMT inhibitor with a novel chemical scaffold and warrants further exploration. This study aimed to continue exploring the potential of de novo design to build epigenetic-focused libraries targeted toward DNMT1. Herein, we report the results of an in-depth and critical comparison of ligand- and structure-based de novo design of screening libraries focused on DNMT1. The newly designed chemical libraries focused on DNMT1 are freely available on GitHub.

Aim. To analyze commercially available fluorine containing compounds for the possibility of their use in the 19F NMR FAXS method. Materials and methods. The selection of fluorine-containing fragments for the study was performed using 3.9 million instock screening compounds and 248,000 in-stock building-blocks from Enamine Ltd library. The selection and classification of the compounds was carried out using the DataWarrior and KNIME software. The Fluorinated Fragments library of Enamine Ltd. containing 6377 compounds, was also analyzed. To analyze the abovementioned sets of substances, the multistep workflows specially designed were used. Results and discussion. As a result of applying the workflow developed to the compound sets (both screening compounds and building blocks), 13 800 compounds were selected and further classified according to the presence of one out of 12 fluorine-containing groups. The Fluorinated Fragments library was also subjected to a similar workflow. For the latter, 8 out of 12 fluorine-containing groups were identified. Additionally, experimental 19F NMR chemical shift values for Fluorinated Fragments library compounds spectra were analyzed. It has been found that some structural classes have areas of chemical shifts intersection. On the other hand, the ranges from –40 to –60 ppm and beyond –160 ppm are free from any group of compounds from the library analyzed. Conclusions. The analysis has shown that commercially available fluorine-containing fragments do not satisfy the needs of the 19F NMR FAXS method, and further expansion of the chemical space of fluorine-containing compounds by increasing their diversity is required.

Aim. DNA-encoded libraries technologies (DELT) are gradually becoming an important part of standard drug discovery toolbox. DELT is looking to find its place between classic low-molecular-weight drug candidates on the one hand, and high-molecular-weight antibodies and peptides on the other hand. On its natural path to overcoming the “childhood diseases” typical for every novel technology, DELT has reached a point where the chemical diversity of DNA-encoded libraries (DELs) becomes an important factor to look out for. In this paper, we aim to take a closer look at the chemical diversity of DELs in their present state and find the ways to improve it. Results and discussion. We have identified the DEL-viable building blocks from the Enamine Ltd. stock collection, as well as from Chemspace Ltd. virtual collection, using the SMARTS set, which takes into account all the necessary structural restrictions. Using modern cheminformatics tools, such as Synt-On, we have analyzed the scaffold diversity of both stock and virtual core bi- and tri-functional building blocks (BBs) suitable for DNA-tolerant reactions. The identification of scaffolds from the most recently published on-DNA heterocyclization reactions and analysis of their inclusion into the existing BBs space have shown that novel DNA-tolerant heterocyclizations are extremely useful for expanding chemical diversity in DEL technologies. Conclusions. The analysis performed allowed us to recognize which functional groups should be prioritized as the most impactful when the new BBs are designed. It is also made clear that the development of new DNA-tolerant reactions, including heterocyclizations, have a significant potential to further expand DEL molecular diversity.

Various sources of information can be used to better understand and predict compound activity and safety-related endpoints, including biological data such as gene expression and cell morphology. In this review, we first introduce types of chemical, in vitro and in vivo information that can be used to describe compounds and adverse effects. We then explore how compound descriptors based on chemical structure or biological perturbation response can be used to predict safety-related endpoints, and how especially biological data can help us to better understand adverse effects mechanistically. Overall, the described applications demonstrate how large-scale biological information presents new opportunities to anticipate and understand the biological effects of compounds, and how this can support predictive toxicology and drug discovery projects.

Abstract Background The increasing amount of chemical reaction data makes traditional ways to navigate its corpus less effective, while the demand for novel approaches and instruments is rising. Recent data science and machine learning techniques support the development of new ways to extract value from the available reaction data. On the one side, Computer-Aided Synthesis Planning tools can predict synthetic routes in a model-driven approach; on the other side, experimental routes can be extracted from the Network of Organic Chemistry, in which reaction data are linked in a network. In this context, the need to combine, compare and analyze synthetic routes generated by different sources arises naturally. Results Here we present LinChemIn, a python toolkit that allows chemoinformatics operations on synthetic routes and reaction networks. Wrapping some third-party packages for handling graph arithmetic and chemoinformatics and implementing new data models and functionalities, LinChemIn allows the interconversion between data formats and data models and enables route-level analysis and operations, including route comparison and descriptors calculation. Object-Oriented Design principles inspire the software architecture, and the modules are structured to maximize code reusability and support code testing and refactoring. The code structure should facilitate external contributions, thus encouraging open and collaborative software development. Conclusions The current version of LinChemIn allows users to combine synthetic routes generated from various tools and analyze them, and constitutes an open and extensible framework capable of incorporating contributions from the community and fostering scientific discussion. Our roadmap envisages the development of sophisticated metrics for routes evaluation, a multi-parameter scoring system, and the implementation of an entire “ecosystem” of functionalities operating on synthetic routes. LinChemIn is freely available at https://github.com/syngenta/linchemin. Graphical Abstract

The volatility of metabolites can influence their biological roles and inform optimal methods for their detection. Yet, volatility information is not readily available for the large number of described metabolites, limiting the exploration of volatility as a fundamental trait of metabolites. Here, we adapted methods to estimate vapor pressure from the functional group composition of individual molecules (SIMPOL.1) to predict the gas-phase partitioning of compounds in different environments. We implemented these methods in a new open pipeline called volcalc that uses chemoinformatic tools to automate these volatility estimates for all metabolites in an extensive and continuously updated pathway database: the Kyoto Encyclopedia of Genes and Genomes (KEGG) that connects metabolites, organisms, and reactions. We first benchmark the automated pipeline against a manually curated data set and show that the same category of volatility (e.g., nonvolatile, low, moderate, high) is predicted for 93% of compounds. We then demonstrate how volcalc might be used to generate and test hypotheses about the role of volatility in biological systems and organisms. Specifically, we estimate that 3.4 and 26.6% of compounds in KEGG have high volatility depending on the environment (soil vs. clean atmosphere, respectively) and that a core set of volatiles is shared among all domains of life (30%) with the largest proportion of kingdom-specific volatiles identified in bacteria. With volcalc, we lay a foundation for uncovering the role of the volatilome using an approach that is easily integrated with other bioinformatic pipelines and can be continually refined to consider additional dimensions to volatility. The volcalc package is an accessible tool to help design and test hypotheses on volatile metabolites and their unique roles in biological systems.

Chemoinformatics is an independent inter-discipline with a broad impact in drug design and discovery, medicinal chemistry, biochemistry, analytical and organic chemistry, natural products, and several other areas in chemistry. Through collaborations, scientific exchanges, and participation in international research networks, Latin American scientists have contributed to the development of this subject. The aim of this perspective is to discuss the status and progress of the chemoinformatic discipline in Latin America. We team up to provide an author´s perspective on the topics that have been investigated and published over the past twelve years, collaborations between Latin America researchers and others worldwide, contributions to open-access chemoinformatic tools such as web servers, and educational-related resources and events, such as scientific conferences. We conclude that linking and fostering collaboration within each nation as well as among other Latin American nations and globally is made possible by open science and the democratization of science. We also outline strategic actions that can boost the development and practice of chemoinformatic in the region and enhance the interaction between Latin American countries and the rest of the world.

Computational de novo drug design is a challenging issue in medicine, and it is desirable to consider all of the relevant information of the biological systems in a disease state. Here, we propose a novel computational method to generate drug candidate molecular structures from patient gene expression profiles via deep learning, which we call DRAGONET. Our model can generate new molecules that are likely to counteract disease‐specific gene expression patterns in patients, which is made possible by exploring the latent space constructed by a transformer‐based variational autoencoder and integrating the substructures of disease‐correlated molecules. We applied DRAGONET to generate drug candidate molecules for gastric cancer, atopic dermatitis, and Alzheimer's disease, and demonstrated that the newly generated molecules were chemically similar to registered drugs for each disease. This approach is applicable to diseases with unknown therapeutic target proteins and will make a significant contribution to the field of precision medicine. [ABSTRACT FROM AUTHOR]

In modern drug discovery, the combination of chemoinformatics and quantitative structure–activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structure–activity relationships (SARs), are pivotal in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. Due to embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences. [ABSTRACT FROM AUTHOR]

Plants of the Phoradendron genus have been traditionally used for their lipid- and glucose-lowering effects. However, the compounds responsible for these effects and the overall chemical profile of these plants have not been thoroughly investigated. We aimed to characterize the metabolome of leaves, stems, and aerial parts of the Phoradendron brachystachyum plant. We used mass spectrometry and colorimetric screening techniques (with various solvents) to identify and characterize the metabolites present. We also evaluated the antioxidant (FRAP, ORAC, TEAC, and DPPH assays) and inhibitory effects on pancreatic lipase and α-glucosidase enzymes of hydrophilic extracts. Furthermore, we compared the molecular fingerprints between the identified metabolites and FDA-approved drugs to gain insights into the metabolites that might be responsible for the observed effects on enzymes. Our findings revealed the presence of 59 putative metabolites, primarily flavonoids. However, we also hint at the presence of peptide and carbohydrate derivatives. The leaf extracts demonstrated the most promising metrics across all assays, exhibiting strong antioxidant and enzyme inhibitory effects as well as high levels of phenolic compounds, flavonoids, and tannins. Fingerprint analysis suggested potential peptide and carbohydrate metabolites as pancreatic lipase and α-glucosidase inhibitors. Overall, our study provides evidence on specific metabolites in Phoradendron brachystachyum that could be responsible for the therapeutic effects noted in obese and type 2 diabetes subjects. [ABSTRACT FROM AUTHOR]

Chemoinformatics involves integrating the principles of physical chemistry with computer-based and information science methodologies, commonly referred to as "in silico techniques", in order to address a wide range of descriptive and prescriptive chemistry issues, including applications to biology, drug discovery, and related molecular areas. On the other hand, the incorporation of machine learning has been considered of high importance in the field of drug design, enabling the extraction of chemical data from enormous compound databases to develop drugs endowed with significant biological features. The present review discusses the field of cheminformatics and proposes the use of virtual chemical libraries in virtual screening methods to increase the probability of discovering novel hit chemicals. The virtual libraries address the need to increase the quality of the compounds as well as discover promising ones. On the other hand, various applications of bioinformatics in disease classification, diagnosis, and identification of multidrug-resistant organisms were discussed. The use of ensemble models and brute-force feature selection methodology has resulted in high accuracy rates for heart disease and COVID-19 diagnosis, along with the role of special formulations for targeting meningitis and Alzheimer's disease. Additionally, the correlation between genomic variations and disease states such as obesity and chronic progressive external ophthalmoplegia, the investigation of the antibacterial activity of pyrazole and benzimidazole-based compounds against resistant microorganisms, and its applications in chemoinformatics for the prediction of drug properties and toxicity—all the previously mentioned—were presented in the current review. [ABSTRACT FROM AUTHOR]

Lithium salts have been the mainstay of treatment for bipolar disorder for more than 50 years, since approval by the FDA in 1970 for the treatment of this pathology. A variety of the molecular mechanisms of action of lithium have been well studied, primarily inhibition of the enzymes glycogen synthase 3β and inositol monophosphatase, with subsequent activation of cascades of cellular reactions, including induction of brain-derived neurotrophic factor and antiapoptotic proteins and suppression of calcium-dependent activation of apoptosis. Research over the last decade has focused on the effects of lithium on the regulation of autophagy and the accumulation of pathological proteins such as amyloid β and tau protein in neurons. Lithium is also thought to induce telomere elongation and to increase telomerase activity. Clinical studies of lithium have addressed the potential for its use for the prevention and treatment of neurodegenerative diseases, primarily Alzheimer's disease, with increasing emphasis on the use of lithium microdoses. A separate scientific problem is the search for safe and effective lithium salts using methods including chemoreactome analysis. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 Main Protease (Mpro) is an enzyme that cleaves viral polyproteins translated from the viral genome, which is critical for viral replication. Mpro is a target for anti-SARS-CoV-2 drug development. Herein, we performed a large-scale virtual screening by comparing multiple structural descriptors of reference molecules with reported anti-coronavirus activity against a library with >17 million compounds. Further filtering, performed by applying two machine learning algorithms, identified eighteen computational hits as anti-SARS-CoV-2 compounds with high structural diversity and drug-like properties. The activities of twelve compounds on Mpro’s enzymatic activity were evaluated by fluorescence resonance energy transfer (FRET) assays. Compound 13 (ZINC13878776) significantly inhibited SARS-CoV-2 Mpro activity and was employed as a reference for an experimentally hit expansion. The structural analogues 13a (ZINC4248385), 13b (ZNC13523222), and 13c (ZINC4248365) were tested as Mpro inhibitors, reducing the enzymatic activity of recombinant Mpro with potency as follows: 13c > 13 > 13b > 13a. Then, their anti-SARS-CoV-2 activities were evaluated in plaque reduction assays using Vero CCL81 cells. Subtoxic concentrations of compounds 13a, 13c, and 13b displayed in vitro antiviral activity with IC50 in the mid micromolar range. Compounds 13a–c could become lead compounds for the development of new Mpro inhibitors with improved activity against anti-SARS-CoV-2.

Virtual screening of large chemical libraries is essential to support computer-aided drug development, providing a rapid and low-cost approach for further experimental validation. However, existing computational packages are often for specialised users or platform limited. Previously, we developed VSpipe, an open-source semi-automated pipeline for structure-based virtual screening. We have now improved and expanded the initial command-line version into an interactive graphical user interface: VSpipe-GUI, a cross-platform open-source Python toolkit functional in various operating systems (e.g., Linux distributions, Windows, and Mac OS X). The new implementation is more user-friendly and accessible, and considerably faster than the previous version when AutoDock Vina is used for docking. Importantly, we have introduced a new compound selection module (i.e., spatial filtering) that allows filtering of docked compounds based on specified features at the target binding site. We have tested the new VSpipe-GUI on the Hepatitis C Virus NS3 (HCV NS3) protease as the target protein. The pocket-based and interaction-based modes of the spatial filtering module showed efficient and specific selection of ligands from the virtual screening that interact with the HCV NS3 catalytic serine 139.

Benzodiazepines are a type of psychotropic drugs with tranquilizing effect, originally used as ansiolytics. However, benzodiazepines are highly addictive, and a person who abuses them faces a host of symptoms. Moreover, the market for new psychoactive substances is growing. The aim of this study was to predict in silico chemical structures on benzodiazepine skeleton with affinity for the GABA A receptor. A set of 50 benzodiazepine-based compounds was analyzed to develop a QSAR training set with respect to published binding values to GABA receptors. To develop a mathematical model, physicochemical properties (partition coefficient, molar refractivity, molar volume) were used to correlate with biological activity (logIC50). We estimated the logIC50 and compared it with the observed values to test our model. To create the ADMET profile, we used the ADMETlab2.0 program. The molecular target for diazepam and nitrazepam molecules was predicted using SwissTargetPrediction. The visualization of the target structure was achieved with the Chimera program. The SeamDock molecular docking program was used to study molecular target interactions with diazepam and nitrazepam drugs. The best results of univariate correlation are shown by molar volume. The best statistics were obtained for univariate correlation. Diazepam and nitrazepam are small molecules with good oral availability that cross the blood-brain barrier, affecting the nervous system. The target predicted most likely to interact with drugs is the alpha-1 subunit of the GABA A receptor. The molecular docking study showed a favorable interaction between the molecular target and the drugs diazepam and nitrazepam. This QSAR model will allow rapid prediction of the binding activity of emerging benzodiazepines in a rapid and economic way, compared with expensive in vitro/in vivo analysis. Diazepam and nitrazepam interact with GABA A receptors, potentiating their activity.

Definition: Efficient chemical library design for high-throughput virtual screening and drug design requires a pre-screening filter pipeline capable of labeling aggregators, pan-assay interference compounds (PAINS), and rapid elimination of swill (REOS); identifying or excluding covalent binders; flagging moieties with specific bio-evaluation data; and incorporating physicochemical and pharmacokinetic properties early in the design without compromising the diversity of chemical moieties present in the library. This adaptation of the chemical space results in greater enrichment of hit lists, identified compounds with greater potential for further optimization, and efficient use of computational time. A number of medicinal chemistry filters have been implemented in the Konstanz Information Miner (KNIME) software and analyzed their impact on testing representative libraries with chemoinformatic analysis. It was found that the analyzed filters can effectively tailor chemical libraries to a lead-like chemical space, identify protein–protein inhibitor-like compounds, prioritize oral bioavailability, identify drug-like compounds, and effectively label unwanted scaffolds or functional groups. However, one should be cautious in their application and carefully study the chemical space suitable for the target and general medicinal chemistry campaign, and review passed and labeled compounds before taking further in silico steps. [ABSTRACT FROM AUTHOR]

PD‐1/PD‐L1 is a critical druggable target for immunotherapy against sepsis. Chemoinformatics techniques involved the structure‐based 3D pharmacophore model development followed by virtual screening of small molecule databases to identify the small molecules against PD‐L1 pathway inhibition. Raltitrexed and Safinamide act as potent repurposed drugs, and three other Specs database compounds using in silico methods. These compounds were screened based on the pharmacophore fit score and binding affinity towards the active site of the PD‐L1 protein. In silico pharmacokinetic profiling of these screened compounds was done to test their biological activity. Next, experimental validation of the best four virtually screened hits was done in vitro for its hemocompatibility and cytotoxicity. Among these, Raltitrexed, Safinamide and Specs compound (AK‐968/40642641) effectively increased the proliferation of immune cells and IFN‐γ production. These compounds can act as potent PDL‐1 inhibitors for adjuvant therapy against sepsis. [ABSTRACT FROM AUTHOR]

Certain bacteria and fungi have been associated with the oxidative degradation of hydrocarbons and hydrocarbon derivatives in soil and water contaminated by crude oil spilling in these ecosystems. The binding affinity of hydrocarbons and hydrocarbon derivatives in a crude oil sample on the cysteine dioxygenase of Bacillus subtilis was studied using computational methods. The study was aimed to validate the claim of the efficient use of this organism in crude oil remediation and to determine the selectivity of the compounds in the crude by this bacterium enzyme. The compounds in the studied crude oil sample were identified by gas chromatography-mass spectrometry. Straight chain hydrocarbons were the least selected class of compounds with binding free energy ranging between -2.9 kcal/mol to -3.1 kcal/mol. The straight-chain hydrocarbon derivatives containing carbonyl and hydroxyl groups formed the middle class of compounds selected by the enzyme with binding free energy ranging between - 3.3 kcal/mol to -3.7 kcal/mol. The best binding free energies (-3.8 kcal/mol to -5.1 kcal/mol) were obtained with the poly-branched hydrocarbons and the cyclic compounds. The amounts of the poly-branched and cyclic compounds in the crude oil sample suggested that cysteine dioxygenase of B. subtilis would show about 28 % efficiency in the bioremediation of environments polluted by crude oil of this composition. [ABSTRACT FROM AUTHOR]

Leishmaniasis, a category 1 neglected protozoan disease caused by a kinetoplastid pathogen called Leishmania, is transmitted through dipteran insect vectors (phlebotomine, sand flies) in three main clinical forms: fatal visceral leishmaniasis, self-healing cutaneous leishmaniasis, and mucocutaneous leishmaniasis. Generic pentavalent antimonials have long been the drug of choice against leishmaniasis; however, their success is plagued with limitations such as drug resistance and severe side effects, which makes them redundant as frontline therapy for endemic visceral leishmaniasis. Alternative therapeutic regimens based on amphotericin B, miltefosine, and paromomycin have also been approved. Due to the unavailability of human vaccines, first-line chemotherapies such as pentavalent antimonials, pentamidine, and amphotericin B are the only options to treat infected individuals. The higher toxicity, adverse effects, and perceived cost of these pharmaceutics, coupled with the emergence of parasite resistance and disease relapse, makes it urgent to identify new, rationalized drug targets for the improvement in disease management and palliative care for patients. This has become an emergent need and more relevant due to the lack of information on validated molecular resistance markers for the monitoring and surveillance of changes in drug sensitivity and resistance. The present study reviewed the recent advances in chemotherapeutic regimens by targeting novel drugs using several strategies including bioinformatics to gain new insight into leishmaniasis. Leishmania has unique enzymes and biochemical pathways that are distinct from those of its mammalian hosts. In light of the limited number of available antileishmanial drugs, the identification of novel drug targets and studying the molecular and cellular aspects of these drugs in the parasite and its host is critical to design specific inhibitors targeting and controlling the parasite. The biochemical characterization of unique Leishmania-specific enzymes can be used as tools to read through possible drug targets. In this review, we discuss relevant metabolic pathways and novel drugs that are unique, essential, and linked to the survival of the parasite based on bioinformatics and cellular and biochemical analyses. [ABSTRACT FROM AUTHOR]

This review presents an analysis of different algorithms for predicting the sensory ability of organic compounds towards metal ions based on their chemical formula. A database of chemosensors containing information on various classes of suitable compounds, including dipyrromethenes, BODIPY, Schiff bases, hydrazones, fluorescein, rhodamine, phenanthroline, coumarin, naphthalimide derivatives, and others (a total of 965 molecules) has been compiled. Additionally, a freely available software has been developed for predicting the sensing ability of chemical compounds, which can be accessed through a Telegram bot. This tool aims to assist researchers in their search for new chemosensors. [ABSTRACT FROM AUTHOR]

Background: The increasing amount of chemical reaction data makes traditional ways to navigate its corpus less effective, while the demand for novel approaches and instruments is rising. Recent data science and machine learning techniques support the development of new ways to extract value from the available reaction data. On the one side, Computer-Aided Synthesis Planning tools can predict synthetic routes in a model-driven approach; on the other side, experimental routes can be extracted from the Network of Organic Chemistry, in which reaction data are linked in a network. In this context, the need to combine, compare and analyze synthetic routes generated by different sources arises naturally. Results: Here we present LinChemIn, a python toolkit that allows chemoinformatics operations on synthetic routes and reaction networks. Wrapping some third-party packages for handling graph arithmetic and chemoinformatics and implementing new data models and functionalities, LinChemIn allows the interconversion between data formats and data models and enables route-level analysis and operations, including route comparison and descriptors calculation. Object-Oriented Design principles inspire the software architecture, and the modules are structured to maximize code reusability and support code testing and refactoring. The code structure should facilitate external contributions, thus encouraging open and collaborative software development. Conclusions: The current version of LinChemIn allows users to combine synthetic routes generated from various tools and analyze them, and constitutes an open and extensible framework capable of incorporating contributions from the community and fostering scientific discussion. Our roadmap envisages the development of sophisticated metrics for routes evaluation, a multi-parameter scoring system, and the implementation of an entire "ecosystem" of functionalities operating on synthetic routes. LinChemIn is freely available at https://github.com/syngenta/linchemin. [ABSTRACT FROM AUTHOR]