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Toll-like receptors (TLRs) act as receptors for bacterial lipopolysaccharides (LPS) and participate in innate immunity. Excessive TLRs activity leads to chronic inflammation and the development of several comorbid pathologies, including osteoarthritis (OA). Computer analysis of 52,312 publications devoted to TLRs was made to systematically describe the mechanisms of biological action of TLRs and their influence on the OA pathophysiology. In addition, we identified the mechanisms by which slow-acting drugs based on standardized forms of chondroitin sulfate (CS), which have symptom-modifying and structural-modifying effects, can decrease TLRs activity and be used in the treatment of OA. Our conclusions are supported by the results of TLRs chemoreactom analysis, as well as by clinical and experimental data.

In olfactory systems, convergence of sensory neurons onto glomeruli generates a map of odorant receptor identity. How glomerular maps relate to sensory space remains unclear. We sought to better characterize this relationship in the mouse olfactory system by defining glomeruli in terms of the odorants to which they are most sensitive. Using high-throughput odorant delivery and ultrasensitive imaging of sensory inputs, we imaged responses to 185 odorants presented at concentrations determined to activate only one or a few glomeruli across the dorsal olfactory bulb. The resulting datasets defined the tuning properties of glomeruli - and, by inference, their cognate odorant receptors - in a low-concentration regime, and yielded consensus maps of glomerular sensitivity across a wide range of chemical space. Glomeruli were extremely narrowly tuned, with ~25% responding to only one odorant, and extremely sensitive, responding to their effective odorants at sub-picomolar to nanomolar concentrations. Such narrow tuning in this concentration regime allowed for reliable functional identification of many glomeruli based on a single diagnostic odorant. At the same time, the response spectra of glomeruli responding to multiple odorants was best predicted by straightforward odorant structural features, and glomeruli sensitive to distinct odorants with common structural features were spatially clustered. These results define an underlying structure to the primary representation of sensory space by the mouse olfactory system.

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.

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.

Keywords: Atom-atom mapping; automated synthesis; cheminformatics; chemoinformatics; document markup; reaction center; reactions; semantic format; structured product labeling (SPL); XML EN Atom-atom mapping automated synthesis cheminformatics chemoinformatics document markup reaction center reactions semantic format structured product labeling (SPL) XML 657 675 19 08/09/22 20220601 NES 220601 Article note: A collection of invited papers on Cheminformatics: Data and Standards. We should want a format that support the full life cycle without barriers from R&D experiment, publications, patents, documentation and control of the production process, regulatory applications, quality monitoring, to trade and logistics; all types of reactions including tautomeric interconversions, catabolic reactions and chemical degradation; reactions executed in the fume hood or synthetic machinery as well as in living systems including entire reaction pathways; descriptive and prescriptive reaction information; single-step and multi-step reactions; and the "mood" of the reaction, such as whether it was successfully executed, attempted but failed, or a computer-aided prediction. Automated synthesis, reaction center, reactions, structured product labeling (SPL), XML, Atom-atom mapping, cheminformatics, chemoinformatics, document markup, semantic format This concept is extended here to reactions and reaction-related data since reactions in the real world often have annotations that go beyond chemistry-type data. [Extracted from the article]

The concept of chemical space is a cornerstone in chemoinformatics, and it has broad conceptual and practical applicability in many areas of chemistry, including drug design and discovery. One of the most considerable impacts is in the study of structure–property relationships where the property can be a biological activity or any other characteristic of interest to a particular chemistry discipline. The chemical space is highly dependent on the molecular representation that is also a cornerstone concept in computational chemistry. Herein, we discuss the recent progress on chemoinformatic tools developed to expand and characterize the chemical space of compound data sets using different types of molecular representations, generate visual representations of such spaces, and explore structure–property relationships in the context of chemical spaces. We emphasize the development of methods and freely available tools focusing on drug discovery applications. We also comment on the general advantages and shortcomings of using freely available and easy-to-use tools and discuss the value of using such open resources for research, education, and scientific dissemination. [ABSTRACT FROM AUTHOR]

Hepatitis C virus (HCV) is a serious disease that threatens human health. Despite consistent efforts to inhibit the virus, it has infected more than 58 million people, with 300,000 deaths per year. The HCV nonstructural protein NS5A plays a critical role in the viral life cycle, as it is a major contributor to the viral replication and assembly processes. Therefore, its importance is evident in all currently approved HCV combination treatments. The present study identifies new potential compounds for possible medical use against HCV using the quantitative structure–activity relationship (QSAR). In this context, a set of 36 NS5A inhibitors was used to build QSAR models using genetic algorithm multiple linear regression (GA-MLR) and Monte Carlo optimization and were implemented in the software CORAL. The Monte Carlo method was used to build QSAR models using SMILES-based optimal descriptors. Four splits were performed and 24 QSAR models were developed and verified through internal and external validation. The model created for split 3 produced a higher value of the determination coefficients using the validation set (R2 = 0.991 and Q2 = 0.943). In addition, this model provides interesting information about the structural features responsible for the increase and decrease of inhibitory activity, which were used to develop eight novel NS5A inhibitors. The constructed GA-MLR model with satisfactory statistical parameters (R2 = 0.915 and Q2 = 0.941) confirmed the predicted inhibitory activity for these compounds. The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) predictions showed that the newly designed compounds were nontoxic and exhibited acceptable pharmacological properties. These results could accelerate the process of discovering new drugs against HCV. [ABSTRACT FROM AUTHOR]

• Metallodrugs have a significant role in drug discovery and medicinal inorganic chemistry. • Several metal-based compounds are approved for clinical use and are in clinical development. • The number of publications focused on metallodrug development is increasing. • There is a need for a database of metal-based compounds with biological activity. • A metallodrugs database would accelerate computational medicinal inorganic chemistry. Metallodrug discovery has evolved in recent years, yielding several compounds in the clinic for therapeutic and medical imaging diagnostic applications. As reviewed here, several research groups in well-established medicinal inorganic chemistry groups are consistently generating high-quality SAR data representing an ideal starting point in the use of computational methods to advance the development of new drugs. Although there are representative chemical structures of metallodrugs in public databases annotated with biological activity, there is currently no public compound database dedicated to metallodrugs. Here, we also discuss the significance, viability, applications and challenges of developing a public compound database of metallodrugs – with consistent representation of metallodrug structure being a crucial obstacle. A curated metallo-compound database would substantially benefit metallodrug discovery and development. [ABSTRACT FROM AUTHOR]

Given the observable toxicity of lithium carbonate, neuropharmacology requires effective and non-toxic lithium salts. In particular, these salts can be employed as neuroprotective agents since lithium ions demonstrate neuroprotective properties through inhibition of glycogen synthetase kinase-3β and other target proteins, increasing concentrations of endogenous neurotrofic factors. The results of theoretical and experimental studies of organic lithium salts presented here indicate their potential as neuroprotectors. Chemoreactomic modeling of lithium salts made it possible to select lithium ascorbate as a suitable candidate for further research. A neurocytological study on cerebellar granular neurons in culture under conditions of moderate glutamate stress showed that lithium ascorbate was more effective in supporting neuronal survival than chloride or carbonate, i.e., inorganic lithium salts. Biodistribution studies indicated accumulation of lithium ions in a sort of "depot", potentially consisting of the brain, aorta, and femur. Lithium ascorbate is characterized by extremely low acute and chronic toxicity (LD50 > 5000 mg/kg) and also shows a moderate antitumor effect when used in doses studied (5 or 10 mg/kg). Studies on the model of alcohol intoxication in rats have shown that intake of lithium ascorbate in doses either 5, 10 or 30 mg/kg did not only reduced brain damage due to ischemia, but also improved the preservation of myelin sheaths of neurons. [ABSTRACT FROM AUTHOR]

Current pandemics propelled research efforts in unprecedented fashion, primarily triggering computational efforts towards new vaccine and drug development as well as drug repurposing. There is an urgent need to design novel drugs with targeted biological activity and minimum adverse reactions that may be useful to manage viral outbreaks. Hence an attempt has been made to develop Machine Learning based predictive models that can be used to assess whether a compound has the potency to be antiviral or not. To this end, a set of 2358 antiviral compounds were compiled from the CAS COVID‐19 antiviral SAR dataset whose activity was reported based on IC50 value. A total 1157 two‐dimensional molecular descriptors were computed among which, the most highly correlated descriptors were selected using Tree‐based, Correlation‐based and Mutual information‐based feature selection methods. Seven Machine Learning algorithms i. e., Random Forest, XGBoost, Support Vector Machine, KNN, Decision Tree, MLP Classifier and Logistic Regression were benchmarked. The best performance was achieved by the models developed using Random Forest and XGBoost algorithms in all the feature selection methods. The maximum predictive accuracy of both these models was 88 % with internal validation. Whereas, with an external dataset, a maximum accuracy of 93.10 % for XGBoost and 100 % for Random Forest based model was achievable. Furthermore, the study demonstrated scaffold analysis of the molecules as a pragmatic approach to explore the importance of structurally diverse compounds in data driven studies. [ABSTRACT FROM AUTHOR]

In‐silico compound‐protein interaction prediction addresses prioritization of drug candidates for experimental biochemical validation because the wet‐lab experiments are time‐consuming, laborious and costly. Most machine learning methods proposed to that end approach this problem with supervised learning strategies in which known interactions are labeled as positive and the rest are labeled as negative. However, treating all unknown interactions as negative instances may lead to inaccuracies in real practice since some of the unknown interactions are bound to be positive interactions waiting to be identified as such. In this study, we propose to address this problem using the Quasi‐Supervised Learning (QSL) algorithm. In this framework, potential interactions are predicted by estimating the overlap between a true positive dataset of compound‐protein pairs with known interactions and an unknown dataset of all the remaining compound‐protein pairs. The potential interactions are then identified as those in the unknown dataset that overlap with the interacting pairs in the true positive dataset in terms of the associated similarity structure. We also address the class‐imbalance problem by modifying the conventional cost function of the QSL algorithm. Experimental results on GPCR and Nuclear Receptor datasets show that the proposed method can identify actual interactions from all possible combinations. [ABSTRACT FROM AUTHOR]

Bruno O Villoutreix,1 Rajagopal Krishnamoorthy,2 Ryad Tamouza,2 Marion Leboyer,2 Philippe Beaune3 1INSERM U1141, NeuroDiderot, Université de Paris, Hôpital Robert-Debré, Paris, F-75019, France; 2Université Paris Est Créteil, INSERM U955, IMRB, Laboratoire Neuropsychiatrie Translationnelle, AP-HP, Département Medico-Universitaire de Psychiatrie et d’Addictologie (DMU ADAPT), Hôpital Henri Mondor, Fondation FondaMental, Créteil, F-94010, France; 3INSERM U1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, 75006, FranceCorrespondence: Bruno O Villoutreix Email bruno.villoutreix@inserm.frIntroduction: There is an urgent need to identify therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients.Objective: Based upon clinical observations, we proposed that some psychotropic and antihistaminic drugs could protect psychiatric patients from SARS-CoV-2 infection. This observation is investigated in the light of experimental in vitro data on SARS-CoV-2.Methods: SARS-CoV-2 high-throughput screening results are available at the NCATS COVID-19 portal. We investigated the in vitro anti-viral activity of many psychotropic and antihistaminic drugs using chemoinformatics approaches.Results and Discussion: We analyze our clinical observations in the light of SARS-CoV-2 experimental screening results and propose that several cationic amphiphilic psychotropic and antihistaminic drugs could protect people from SARS-CoV-2 infection; some of these molecules have very limited adverse effects and could be used as prophylactic drugs. Other cationic amphiphilic drugs used in other disease areas are also highlighted. Recent analyses of patient electronic health records reported by several research groups indicate that some of these molecules could be of interest at different stages of the disease progression. In addition, recently reported drug combination studies further suggest that it might be valuable to associate several cationic amphiphilic drugs. Taken together, these observations underline the need for clinical trials to fully evaluate the potentials of these molecules, some fitting in the so-called category of broad-spectrum antiviral agents. Repositioning orally available drugs that have moderate side effects and should act on molecular mechanisms less prone to drug resistance would indeed be of utmost importance to deal with COVID-19.Keywords: chemoinformatics, COVID-19, high-throughput screening, phenothiazine, prophylaxis, antihistamine, drug repurposing

Within the realm of pharmacological strategies for cardiovascular diseases (CVD) like hypertension, stroke, and heart failure, targeting the angiotensin-converting enzyme I (ACE-I) stands out as a significant treatment approach. This study employs QSAR modeling using Monte Carlo optimization techniques to investigate a range of compounds known for their ACE-I inhibiting properties. The modeling process involved leveraging local molecular graph invariants and SMILES notation as descriptors to develop conformation-independent QSAR models. The dataset was segmented into distinct sets for training, calibration, and testing to ensure model accuracy. Through the application of various statistical analyses, the efficacy, reliability, and predictive capability of the models were evaluated, showcasing promising outcomes. Additionally, molecular fragments derived from SMILES notation descriptors were identified to elucidate the activity changes observed in the compounds. The validation of the QSAR model and designed inhibitors was carried out via molecular docking, aligning well with the QSAR results. To ascertain the drug-worthiness of the designed molecules, their physicochemical properties were computed, aiding in the prediction of ADME parameters, pharmacokinetic attributes, drug-likeness, and medicinal chemistry compatibility. [Display omitted] • QSAR models for ACE-I inhibition were developed. • Monte Carlo optimization method was employed to construct QSAR models. • SMILES notation descriptors and local molecular graph invariants were employed. • Different methods were applied for QSPR models predictability determination. • Molecular fragments with influence on studied activity were determined. [ABSTRACT FROM AUTHOR]

Understanding of the capacity of the natural world to produce secondary metabolites is important to a broad range of fields, including drug discovery, ecology, biosynthesis, and chemical biology, among others. Both the absolute number and the rate of discovery of natural products have increased significantly in recent years. However, there is a perception and concern that the fundamental novelty of these discoveries is decreasing relative to previously known natural products. This study presents a quantitative examination of the field from the perspective of both number of compounds and compound novelty using a dataset of all published microbial and marine-derived natural products. This analysis aimed to explore a number of key questions, such as how the rate of discovery of new natural products has changed over the past decades, how the average natural product structural novelty has changed as a function of time, whether exploring novel taxonomic space affords an advantage in terms of novel compound discovery, and whether it is possible to estimate how close we are to having described all of the chemical space covered by natural products. Our analyses demonstrate that most natural products being published today bear structural similarity to previously published compounds, and that the range of scaffolds readily accessible from nature is limited. However, the analysis also shows that the field continues to discover appreciable numbers of natural products with no structural precedent. Together, these results suggest that the development of innovative discovery methods will continue to yield compounds with unique structural and biological properties.

Abstract Two-dimensional (2D) chemical fingerprints are widely used as binary features for the quantification of structural similarity of chemical compounds, which is an important step in similarity-based virtual screening (VS). Here, using an eigenvalue-based entropy approach, we identified 2D fingerprints with little to no contribution to shaping the eigenvalue distribution of the feature matrix as related ones and examined the degree to which these related 2D fingerprints influenced molecular similarity scores calculated with the Tanimoto coefficient. Our analysis identified many related fingerprints in publicly available fingerprint schemes and showed that their presence in the feature set could have substantial effects on the similarity scores and bias the outcome of molecular similarity analysis. Our results have implication in the optimal selection of 2D fingerprints for compound similarity analysis and the identification of potential hits for compounds with target biological activity in VS.

• Systematic cheminformatic and machine learning analyses were conducted to study the chemical space, scaffolds, SAR, and landscape of human BRAF-inhibitors. • Scaffold visualization has identified 12 representative Murcko scaffolds. • Out of 14 machine learning models for BRAF-inhibitors, the Extra-trees QSAR classification model has been identified as the most significant. • Through a detailed analysis of the structure–activity landscapes, a total of sixteen significant consensus activity cliff (AC) generators were identified. • Findings from this study provide new insights and guidelines for hit identification and lead optimization in developing novel BRAF inhibitors. BRAF kinase inhibitors have shown promise in treating melanoma patients with BRAF-V600 mutations. However, their clinical benefits are limited due to the development of acquired resistance. To address this issue, we conducted a systematic cheminformatic analysis and machine learning modeling to study the chemical space, scaffolds, structure–activity relationship, and landscape of human BRAF inhibitors. The final dataset comprised 3,952 molecules. Physicochemical property visualization for chemical space visualization has demonstrated that molecules from Group 1 (potent/active class) generally have slightly higher MW, RB, NumHAcceptors, and TPSA than molecules from Group 2 (intermediate/inactive class). The principal component analysis (PCA) shows that Group 1 data spreads widely and overlaps with the other group, which occupies only the left area on the plot. This suggests that molecules' chemical structure or scaffolds are more diverse for Group 1 (Potent and Active) compounds than for Group 2 (Intermediate and Inactive) compounds. Murcko scaffold analysis has shown a greater scaffold diversity in the Active, Intermediate, and Inactive classes than in the Potent class. However, in the four bioactivity-defined classes for our BRAF dataset, molecules are uniformly distributed, with a small number of highly populated scaffolds and a large number of singletons. Furthermore, scaffold visualization has identified 12 representative Murcko scaffolds. Scaffolds 1 S1C1, S2C1, S2C2, S3C2, and S4C2 are highly favorable due to their high scaffold enrichment factor values. Based on scaffold analysis, the study investigated and summarized the local structure–activity relationships (SARs). In addition, the global SAR landscape was explored through quantitative structure–activity relationship (QSAR) modeling and structure–activity landscape visualization. Out of a total of 14 candidate models for BRAF inhibitors, a QSAR classification model that includes all 3952 molecules has been identified as the best model. The model was built using the PubChem fingerprint and extra trees algorithm and achieved an accuracy of 0.920 for the training set, 0.699 for the 10-fold cross-validation set, and 0.733 for the test set. Through a detailed analysis of the structure–activity landscapes, a total of sixteen significant consensus activity cliff (AC) generators were identified (ChEMBL molecule IDs: 4795335, 1822247, 3665863, 5083036, 5086749, 4061139, 1822242, 3665859, 1822250, 3641129, 3641043, 514688,3641178, 3697934, 3641046, and 368991). These generators offer valuable information on the SAR (Structure-Activity Relationship) for medicinal chemistry. The findings from this study provide new insights and guidelines for hit identification and lead optimization in developing novel BRAF inhibitors. [ABSTRACT FROM AUTHOR]

The first line of prevention of surgical site infection relies on the timely administration of antibiotic prophylaxis. First- and second-generation cephalosporins are the most recommended antibiotics in elective surgery. The incidence of cefazolin allergy has increased worldwide over the years. The sensitization mechanism of cefazolin is currently unknown, and data supporting cross-reactivity between penicillins and cephalosporins are lacking. Sensitization could occur through previous exposure either to cefazolin or to structurally related chemical agents. The objective of this study was to evaluate sensitization agents towards cefazolin. The OpenBabel chemoinformatics toolbox was used to search for similarities between cefazolin and other molecules in an extensive drug database. Using the pholcodine-rocuronium similarity score as a threshold, we selected drugs with the most similar structure to that of cefazolin. Exposure to those drugs and cefazolin was assessed in a cohort of patients with skin test-proven cefazolin allergy at a specialized allergy centre via a self-administered anonymous questionnaire. Using the pholcodine-rocuronium similarity score as a threshold (score≥0.7), 42 molecules were found to be similar to cefazolin (all cephalosporins). Only 8 were marketed in France. None of the 14 cefazolin-allergic patients who answered the questionnaire (65% female, median age 56 years) reported exposure to any identified antibiotics. In contrast, 11 (78%) had at least one previous surgery requiring cefazolin before the index case. Direct previous cefazolin exposure was identified in 78% of cefazolin-allergic patients. Cefazolin started to take a central place in antibiotic prophylaxis after 2010, when cefamandole usage decreased drastically. Changes in antibiotic prophylaxis over the past 14 years in France could have been the turning point for the increased incidence of cefazolin allergy. [ABSTRACT FROM AUTHOR]

Prostate cancer (PC) is one of the most common types of cancer in males. Although early stages of PC are generally associated with favorable outcomes, advanced phases of the disease present a significantly poorer prognosis. Moreover, currently available therapeutic options for the treatment of PC are still limited, being mainly focused on androgen deprivation therapies and being characterized by low efficacy in patients. As a consequence, there is a pressing need to identify alternative and more effective therapeutics. In this study, we performed large-scale 2D and 3D similarity analyses between compounds reported in the DrugBank database and ChEMBL molecules with reported anti-proliferative activity on various PC cell lines. The analyses included also the identification of biological targets of ligands with potent activity on PC cells, as well as investigations on the activity annotations and clinical data associated with the more relevant compounds emerging from the ligand-based similarity results. The results led to the prioritization of a set of drugs and/or clinically tested candidates potentially useful in drug repurposing against PC.

With the growing amount of chemical data stored digitally, it has become crucial to represent chemical compounds accurately and consistently. Harmonized representations facilitate the extraction of insightful information from datasets, and are advantageous for machine learning applications. To achieve consistent representations throughout datasets, one relies on molecule standardization, which is typically accomplished using rule-based algorithms that modify descriptions of functional groups. Here, we present the first deep-learning model for molecular standardization. We enable custom standardization schemes based solely on data, which, as additional benefit, support standardization options that are difficult to encode into rules. Our model achieves over $98\%$ accuracy in learning two popular rule-based standardization protocols. We then follow a transfer learning approach to standardize metal-organic compounds (for which there is currently no automated standardization practice), based on a human-curated dataset of 1512 compounds. This model predicts the expected standardized molecular format with a test accuracy of 80.7%. As standardization can be considered, more broadly, a transformation from undesired to desired representations of compounds, the same data-driven architecture can be applied to other tasks. For instance, we demonstrate the application to compound canonicalization and to the determination of major tautomers in solution, based on computed and experimental data.

Drug-induced liver injury (DILI) is the principal reason for failure in developing drug candidates. It is the most common reason to withdraw from the market after a drug has been approved for clinical use. In this context, data from animal models, liver function tests, and chemical properties could complement each other to understand DILI events better and prevent them. Since the chemical space concept improves decision-making drug design related to the prediction of structure–property relationships, side effects, and polypharmacology drug activity (uniquely mentioning the most recent advances), it is an attractive approach to combining different phenomena influencing DILI events (e.g., individual “chemical spaces”) and exploring all events simultaneously in an integrated analysis of the DILI-relevant chemical space. However, currently, no systematic methods allow the fusion of a collection of different chemical spaces to collect different types of data on a unique chemical space representation, namely “consensus chemical space.” This study is the first report that implements data fusion to consider different criteria simultaneously to facilitate the analysis of DILI-related events. In particular, the study highlights the importance of analyzing together in vitro and chemical data (e.g., topology, bond order, atom types, presence of rings, ring sizes, and aromaticity of compounds encoded on RDKit fingerprints). These properties could be aimed at improving the understanding of DILI events.

Parkinson’s disease (PD) is the second most common neurodegenerative disease in older individuals worldwide. Pharmacological treatment for such a disease consists of drugs such as monoamine oxidase B (MAO-B) inhibitors to increase dopamine concentration in the brain. However, such drugs have adverse reactions that limit their use for extended periods; thus, the design of less toxic and more efficient compounds may be explored. In this context, cheminformatics and computational chemistry have recently contributed to developing new drugs and the search for new therapeutic targets. Therefore, through a data-driven approach, we used cheminformatic tools to find and optimize novel compounds with pharmacological activity against MAO-B for treating PD. First, we retrieved from the literature 3316 original articles published between 2015–2021 that experimentally tested 215 natural compounds against PD. From such compounds, we built a pharmacological network that showed rosmarinic acid, chrysin, naringenin, and cordycepin as the most connected nodes of the network. From such compounds, we performed fingerprinting analysis and developed evolutionary libraries to obtain novel derived structures. We filtered these compounds through a docking test against MAO-B and obtained five derived compounds with higher affinity and lead likeness potential. Then we evaluated its antioxidant and pharmacokinetic potential through a docking analysis (NADPH oxidase and CYP450) and physiologically-based pharmacokinetic (PBPK modeling). Interestingly, only one compound showed dual activity (antioxidant and MAO-B inhibitors) and pharmacokinetic potential to be considered a possible candidate for PD treatment and further experimental analysis.

The retrospective evaluation of virtual screening approaches and activity prediction models are important for methodological development. However, for fair comparison, evaluation data sets must be carefully prepared. In this research, we compiled structure–activity–relationship matrix-based data sets for 15 biological targets along with many diverse inactive compounds, assuming the early stage of structure–activity–relationship progression. To use a large number of diverse inactive compounds and a limited number of active compounds, similarity profiles (SPs) are proposed as a set of molecular descriptors. Using these highly imbalanced data sets, we evaluated various approaches including SPs, under-sampling, support vector machine (SVM), and message passing neural networks. We found that for the under-sampling approaches, cluster-based sampling is better than random sampling. For virtual screening, SPs with inactive reference compounds and the under-sampling SVM also perform well. For classification, SPs with many inactive references performed as well as the under-sampling SVM trained on a balanced data set. Although the performance of SPs and the under-sampling SVM were comparable, SPs with many inactive references were preferable for selecting structurally distinct compounds from the active training compounds. [ABSTRACT FROM AUTHOR]

Ionic liquids (ILs) have great potential for application in energy storage and conversion devices. They have been identified as promising electrolytes candidates in various battery systems. However, the practical application of many ionic liquids remains limited due to the unfavorable melting points (Tm) which constrain the operating temperatures of the batteries and exhibit unfavorable transport property. To fine tune the Tm of ILs, a systematic study and accurate prediction of Tm of ILs is highly desirable. However, the Tm of an IL can change considerably depending on the molecular structures of the anion and cation and their combination. Thus, a fine control in Tm of ILs can be challenging. In this study, we employed a deep-learning model to predict the Tm of various ILs that consist of different cation and anion classes. Based on this model, a prediction of the melting point of ILs can be made with a reasonably high accuracy, achieving an R2 score of 0.90 with RMSE of ~32 K, and the Tm of ILs are mostly dictated by some important molecular descriptors, which can be used as a set of useful design rules to fine tune the Tm of ILs. [ABSTRACT FROM AUTHOR]

The development of sophisticated artificial intelligence algorithms and the massive availability of biomedical data have driven accelerated research to discover novel treatments using drugs previously developed and approved for other uses. This process is known as drug repositioning (DR), and it can be addressed by a branch of artificial intelligence (AI) known as machine learning (ML). Machine learning is based on a set of algorithms that, combined with well-established computational techniques in the field of drug discovery, have been able to infer new previously unknown pharmacological properties and relationships with high efficiency. Thus, new targets and potential treatments against various diseases such as cancer and neurodegenerative and infectious diseases have been identified. The objective of this review is to contribute to the literature in Spanish on the use of Artificial Intelligence and machine learning in drug repositioning research. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common diseases whose seriousness has raised concern and anxiety worldwide with over 7 million cases and around 685 thousand deaths globally (according to the World Health Organization(WHO)), making it one of the world’s most prevailing diseases. Scientists and doctors tried to cure breast cancer disease using many types of therapeutic treatment, the most common of them being Endo-therapy. Unfortunately however, Endo-therapy did ot have much better results than the other types of treatments. Some breast cancer cells have estrogen receptors which can attract estrogen proteins and make a cancer cell grow. In this study, we introduce deep neural networks on QSAR model to find inhibitors for those estrogen receptors and thus stop the breast cancer cells from growing. We present a comprehensive comparison between our model (the DNNR model) and the Random forest model, in which the DNNR outperformed the RF algorithm (an increase of 21.2% in the accuracy of the algorithm). [ABSTRACT FROM AUTHOR]

Modern drug discovery and pharmaceutics benefit from nature. Natural products (NPs) are used as a source of therapeutic agents with beneficial uses. Currently, there is considerable interest in the exploration of NPs for drug discovery and continuous investigations on the therapeutic claims and mechanisms of herbal medicines. To date, approximately one million NPs have been isolated and subjected to experimental assays to evaluate quantitative biological activities. This renders the use of an integrated database to assemble and correlate this valuable information from the literature, experimental studies and databases necessary. Although databases contain a large volume of information, it is frequently difficult and complex, even in well-organized databases, to extract the required information. Novel databases must be accompanied by efficient algorithms and techniques in order to extract beneficial knowledge by a simple query. The Hippo(crates) database aims to fill this gap in the field of chemoinformatics and natural products by providing retrieval not only linked to the Hippo(crates) database, but also to other worldwide chemical and biological databases. Part of the OPENSCREEN-GR project, the Hippo(crates) Database Graphical User Interface (HDGUI) web server was developed to provide a user-friendly access interface, integrating annotated information of NP origin (sources and species), biological activities, physicochemical properties, linear and 3D chemical structure, as well as relative terms that correlate chemical compounds and their use. In its current version (V1.0), the Hippo database provides 45,300 NPs, NP derivatives and synthetic compounds, which are separated into 32 major categories, including biological or medicinal properties. In the database, 22,830 NP source organisms are correlated, with >100,000 terms, including biological pathways, target organisms, target diseases, target types, target proteins and pathogens, and 6,070 three-dimensional structures of NP target proteins. For each entry, a cluster with similar compounds and a ligand-based or structure-based pharmacophore model is provided. The portal is designed as an easy-to-use web tool where the user can easily search, extract and correlate information and data for natural product chemical compounds through various fields, such as categories, keywords, targets, species, or two-dimensional or three-dimensional similarity structure in the Hippo(crates) atlas of the NP database. [ABSTRACT FROM AUTHOR]

Artificial neural networks are applied to quantify the properties of organic molecules by introducing a new descriptor, a core‐loss spectrum, which is typically observed experimentally using electron or X‐ray spectroscopy. Using the calculated C K‐edge core‐loss spectra of organic molecules as the descriptor, the neural network models quantitatively predict both intensive and extensive properties, such as the gap between highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) (HOMO–LUMO gap) and internal energy. The prediction accuracy estimated by the mean absolute errors for the HOMO–LUMO gap and internal energy is 0.205 and 97.3 eV, respectively, which are comparable with those of previously reported chemical descriptors. This study indicates that the neural network approach using the core‐loss spectra as the descriptor has the potential to deconvolute the abundant information available in core‐loss spectra for both prediction and experimental characterization of many physical properties. The study shows the practical potential of machine‐learning‐based material property measurements taking advantage of experimental core‐loss spectra, which can be measured with high sensitivity, high spatial resolution, and high temporal resolution. [ABSTRACT FROM AUTHOR]

Aim. To develop a pharmacophore model suitable for the antistaphylococcal activity screening among thiazolidinone, thiopyrano[2,3-d]thiazole and thiazolo[4,5-b]pyridine derivatives. Results and discussion. The best pharmacophore model in the series of models developed has a planar structure and consists of an aromatic ring (or cycle with π-bonds), a hydrophobic region, a projection of a hydrogen bond donor and two projections of a hydrogen bond acceptor. Its classification accuracy is 72.4 %. The diameter line of the model is formed by the projection of the hydrogen bond donor and the projection of the hydrogen bond acceptor, and its length is 8.05 Å. The analysis of conformations of active compounds consistent with the pharmacophore mode revealed two different ways of spatial arrangement of active molecules, in which the conditions of the pharmacophore model are fully met. Experimental part. The antistaphylococcal activity was determined by the agar diffusion method against methicillin-resistant strain of Staphylococcus aureus (MRSA) and evaluated by measuring the diameter of the microbial growth inhibition zone. The plates were incubated for 24 h at 37 °C. Compounds with the growth inhibition diameter of more than 7.5 mm were considered to be active. Computer processing of the results of the microbiological experiment and modeling of the probable pharmacophore were performed in the MOE software environment version 2007.09. The geometry of the compounds was optimized by molecular mechanics using the MMFF94x force field. Accuracy of classification was used as the main quality criterion of the pharmacophore model. Conclusions. The pharmacophore model developed can be used for virtual screening of the antistaphylococcal activity for the compounds similar to the training sample. When applying it to the in-home database of compounds the enrichment factor is EF = 2.05. Received: 27.09.2020 Revised: 23.10.2020 Accepted: 03.11.2020

Introduction. The pharmaceutical drugs used in the treatment of osteoarthritis (OA) differ not only in the mechanisms of anti-inflammatory action but also in the effects on the human microbiome.Purpose. Evaluation of the influence of some drugs used in the therapy of OA on the human microbiome by the method of chemoinformation analysis.Materials and methods. Сomparative chemomicrobiome analysis of glucosamine sulfate (GS), diclofenac, acetylsalicylic acid (ASA), and three prebiotics (lactose, lactulose, fructose) as molecules of comparison. For each substance, estimates of the area under the curve (AUC) were obtained for a representative sampling of human microbiota (38 commensal bacteria). The minimum inhibitory concentrations (MIC) were established for more than 120 pathogenic bacteria.Results. On average, according to a representative sampling of microbiota, the profile of the action of GS on the microbiome was almost identical to the profile of the action of lactose (AUC=0.23±0.18). The most effective growth of the microbiome was provided by fructose and lactulose (AUC=0.58±0.21). The effects of diclofenac and ASA on the commensals of microbiome were comparable to the effects of GS (AUC=0.27±0.22). However, the analysis of the obtained MIC values for pathogenic bacteria showed that diclofenac supported the growth of the pathogenic flora (MIC=35±1.4 μg/ml) to a greater extent than GS (MIC=16±1.5 μg/ml) and ASA (MIC=23±2.2 μg/ml).Conclusion. The effects of GS on the microbiome are comparable to the effects of the prebiotic lactose whereas the inhibitory effect of GS and ASA on pathogenic bacteria is more pronounced than that of diclofenac. The inhibition of pathogenic bacteria by the GS helps to reduce inflammation.

Abstract Virtual compound libraries are increasingly being used in computer-assisted drug discovery applications and have led to numerous successful cases. This paper aims to examine the fundamental concepts of library design and describe how to enumerate virtual libraries using open source tools. To exemplify the enumeration of chemical libraries, we emphasize the use of pre-validated or reported reactions and accessible chemical reagents. This tutorial shows a step-by-step procedure for anyone interested in designing and building chemical libraries with or without chemoinformatics experience. The aim is to explore various methodologies proposed by synthetic organic chemists and explore affordable chemical space using open-access chemoinformatics tools. As part of the tutorial, we discuss three examples of design: a Diversity-Oriented-Synthesis library based on lactams, a bis-heterocyclic combinatorial library, and a set of target-oriented molecules: isoindolinone based compounds as potential acetylcholinesterase inhibitors. This manuscript also seeks to contribute to the critical task of teaching and learning chemoinformatics.

Artificial neural networks are applied to quantify the properties of organic molecules by introducing a new descriptor, a core‐loss spectrum, which is typically observed experimentally using electron or X‐ray spectroscopy. Using the calculated C K‐edge core‐loss spectra of organic molecules as the descriptor, the neural network models quantitatively predict both intensive and extensive properties, such as the gap between highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) (HOMO–LUMO gap) and internal energy. The prediction accuracy estimated by the mean absolute errors for the HOMO–LUMO gap and internal energy is 0.205 and 97.3 eV, respectively, which are comparable with those of previously reported chemical descriptors. This study indicates that the neural network approach using the core‐loss spectra as the descriptor has the potential to deconvolute the abundant information available in core‐loss spectra for both prediction and experimental characterization of many physical properties. The study shows the practical potential of machine‐learning‐based material property measurements taking advantage of experimental core‐loss spectra, which can be measured with high sensitivity, high spatial resolution, and high temporal resolution.

The ChemSpider and PubChem chemical databases used in non-target mass spectrometry analysis in order to outline the candidates for identification are described. Relevant compounds are searched by molecule-containing ion masses (only ChemSpider) or by molecular formulas before determined (both databases). The compounds that are highly cited and information-rich are candidates for identification. PubChem contains more direct information on the properties and use of chemical compounds than that in the other database. These data give evidence of the likelihood of identification. Final decisions on identification are made with the traditional use of reference analytical data (mass spectra and chromatography retention parameters) and analytical reference materials. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the era of big data in Chemistry, the need for automated tools for virtual screening is compelling. Here, we present PrepFlow a toolkit for chemical library preparation and management. Starting from a list of compounds in SMILES or 2D molecular format, PrepFlow outputs a set of 3D molecular structures ready for use in subsequent drug discovery projects. Our development stands out for speed and robustness of execution, the efficient exploitation of HPC resources, and the implementation of an archiving strategy to save computer time, storage, and human intervention. Using a random selection of 600 compounds from available drug banks, we show that the preparation time per ligand on a desktop computer is 6.6 s. Thanks to these performances and the automatic parallelization on HPC, a chemical library of the size of ChEMBL (2 M) was prepared in around 3 days on a computer cluster. PrepFlow is freely distributed at the following link: https://ifm.chimie.unistra.fr/prepflow. [ABSTRACT FROM AUTHOR]

Here, we report an application of the multi-instance learning approach to predictive modeling of enantioselectivity of chiral catalysts. Catalysts were represented by ensembles of conformations encoded- by the pmapper physicochemical descriptors capturing stereoconfiguration of the molecule. Each catalyzed chemical reaction was transformed to a condensed graph of reaction for which ISIDA fragment descriptors were generated. This approach does not require any conformations' alignment and can potentially be used for a diverse set of catalysts bearing different scaffolds. Its efficiency has been demonstrated in predicting the selectivity of BINOL-derived phosphoric acid catalysts in asymmetric thiol addition to N -acylimines and benchmarked with previously reported models. [ABSTRACT FROM AUTHOR]

[Display omitted] The synthesis of the desired chemical compound is the main task of synthetic organic chemistry. The predictions of reaction conditions and some important quantitative characteristics of chemical reactions as yield and reaction rate can substantially help in the development of optimal synthetic routes and assessment of synthesis cost. Theoretical assessment of these parameters can be performed with the help of modern machine-learning approaches, which use available experimental data to develop predictive models called quantitative or qualitative structure–reactivity relationship (QSRR) modelling. In the article, we review the state-of-the-art in the QSRR area and give our opinion on emerging trends in this field. [ABSTRACT FROM AUTHOR]

Isothiocyanates (ITCs) have a significant impact on food and natural product chemistry. Several dietary components and food chemicals contain the isothiocyanate moiety. In addition, many ITCs interact with macromolecules of biological relevance, making these compounds relevant for potential therapeutic applications and disease prevention. However, there is a lack of systematic analysis of ITCs in chemical and biological databases. Herein, we conducted a comprehensive analysis of ITCs present in public domain databases, including natural products, food chemicals, macromolecular targets of drugs, and the Protein Data Bank. A total of 154 ITCs were found, which can be classified into seven categories: acyclic, cyclic, polycyclic, aromatic, polyaromatic, indolic, and glycosylated. 24 ITCs were reported in 18 vegetable sources, mainly in cruciferous vegetables (Brassica oleracea L.). Calculated properties of pharmaceutical relevance indicated that 11 % of the 154 ITCs would be suitable to be orally absorbed and 48 % permeate the blood‐brain‐barrier. It was also found that seven molecular targets have been co‐crystallized with ITCs and the most frequent is the macrophage migration inhibitory factor. It is expected that this work will contribute to the sub‐disciplines of natural products and food informatics. [ABSTRACT FROM AUTHOR]

Despite advancements in the areas of omics and chemoinformatics, potent novel biotherapeutic molecules with new modes of actions are needed for leishmaniasis. The socioeconomic burden of leishmaniasis remains alarming in endemic regions. Currently, reports from existing endemic areas such as Nepal, Iran, Brazil, India, Sudan and Afghanistan, as well as newly affected countries such as Peru, Bolivia and Somalia indicate concerns of chemoresistance to the classical antimonial treatment. As a result, effective antileishmanial agents which are safe and affordable are urgently needed. Natural products from both flora and fauna have contributed immensely to chemotherapeutics and serve as vital sources of new chemical agents. This review focuses on a systematic cross-sectional view of all characterized anti-leishmanial compounds from natural sources over the last decade. Furthermore, IC50/EC50, cytotoxicity and suggested mechanisms of action of some of these natural products are provided. The natural product classification includes alkaloids, terpenes, terpenoids, and phenolics. The plethora of reported mechanisms involve calcium channel inhibition, immunomodulation and apoptosis. Making available enriched data pertaining to bioactivity and mechanisms of natural products complement current efforts geared towards unraveling potent leishmanicides of therapeutic relevance. [ABSTRACT FROM AUTHOR]

Introduction: The combined use of non-steroidal anti-inflammatory drugs (NSAIDs), muscle relaxants and group B vitamins is a promising direction for combined pharmacotherapy of peripheral pain syndromes.Purpose: to analyze the molecular mechanisms of the synergism of the muscle relaxant tolperisone, a non-steroidal anti-inflammatory drug meloxicam and vitamins B1, B6 and B12 as part of complex pharmacotherapy of pain.Materials and methods: differential chemoreactome analysis of NSAID molecules and muscle relaxants, proteomic analysis of the effects of B vitamins.Results: Synergistic interactions are maintained by (1) inhibiting cyclooxygenase-2 and leukotriene A4 hydrolases; (2) inhibition of the effects of IL-1β, TNFα, NF-kB, TLR4, RANKL, matrix metalloproteinases; (3) antioxidant effect (increased expression of superoxide dismutase-1 and glutathione peroxidase). Group B vitamins enhance the effects of tolperisone and meloxicam due to the manifestation of independent anti-inflammatory (neutralization of homocysteine, decreased expression of pro-inflammatory cytokines TNFα, IL-1b and factor NF-kB activity), analgesic (modulation of opioidergic pathways) and neuroprotective effects (support of amino acid expression, metabolism and neuro lipids, ATP synthesis and remyelination).Conclusions: The triple scheme “tolperisone + meloxicam + vitamins B1 / B6 / B12” is characterized by a number of undeniable advantages: (1) lack of dependence, (2) anti-inflammatory effect, (3) neuroprotective and remyelinating effects, (4) elimination of muscle hypertonicity. Such combination therapy can be used in patients of various age groups with comorbid conditions (diabetes mellitus, arterial hypertension, cerebrovascular diseases, gastrointestinal diseases) and does not require an increase in NSAIDs and significantly reduces the risk of side effects.

The paper presents a simple method of determining iteratively the progression curve asymptote for first and pseudo-first order reactions. For selected student exercises, thus obtained results were compared (see Supplementary Material) with those found by means of the method of determining asymptotes experimentally. A nonlinear fitting method was additionally employed to assess the accuracy.

The Cu-catalyzed multicomponent ketone–amine–alkyne (KA2) reaction was combined with a Pauson–Khand cycloaddition to give access of unprecedented constrained spirocyclic pyrrolocyclopentenone derivatives following a DOS couple-pair approach. The polyfunctional molecular scaffolds were tested on the cyclopentenone reactivity to further expand the skeletal diversity, demonstrating the utility of this combined approach in generating novel spiro compounds as starting material for the generation of chemical libraries. The chemoinformatics characterization of the newly-synthesized molecules gave evidence about structural and physicochemical properties with respect to a set of blockbuster drugs, and showed that such scaffolds are drug-like but more spherical and three-dimensional in character than the drugs.