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Given that there is currently no clinically approved drug or vaccine for parainfluenza 3 (PIV3), we applied a drug repurposing method based on disease similarity and chemical similarity to screen 2,585 clinically approved chemical drugs using PIV3 potential drugs BCX-2798 and zanamivir as our controls. Twelve candidate drugs were obtained after being screened with good disease similarity and chemical similarity (S > 0.50, T > 0.56). When docking them with the PIV3 target protein, hemagglutinin-neuraminidase (HN), only oseltamivir was docked with a better score than BCX-2798, which indicates that oseltamivir has an inhibitory effect on PIV3. After the distance ( Z d c ) between the drug target of 14 drugs and the PIV3 disease target was measured by the network proximity method based on the PIV3 disease module, it was found that the Z d c values of amikacin, oseltamivir, ribavirin, and streptomycin were less than those of the control. Thus, oseltamivir is the best potential drug because it met all the above screening requirements. Additionally, to explore whether oseltamivir binds to HN stably, molecular dynamics simulation of the binding of oseltamivir to HN was carried out, and the results showed that the RMSD value of the complex tended to be stable within 100 ns, and the binding free energy of the complex was low (−10.60 kcal/mol). It was proved that oseltamivir screened by our drug repurposing method had the potential feasibility of treating PIV3. [ABSTRACT FROM AUTHOR]

Given that there is currently no clinically approved drug or vaccine for parainfluenza 3 (PIV3), we applied a drug repurposing method based on disease similarity and chemical similarity to screen 2,585 clinically approved chemical drugs using PIV3 potential drugs BCX-2798 and zanamivir as our controls. Twelve candidate drugs were obtained after being screened with good disease similarity and chemical similarity (S > 0.50, T > 0.56). When docking them with the PIV3 target protein, hemagglutinin-neuraminidase (HN), only oseltamivir was docked with a better score than BCX-2798, which indicates that oseltamivir has an inhibitory effect on PIV3. After the distance (Zdc) between the drug target of 14 drugs and the PIV3 disease target was measured by the network proximity method based on the PIV3 disease module, it was found that the Zdc values of amikacin, oseltamivir, ribavirin, and streptomycin were less than those of the control. Thus, oseltamivir is the best potential drug because it met all the above screening requirements. Additionally, to explore whether oseltamivir binds to HN stably, molecular dynamics simulation of the binding of oseltamivir to HN was carried out, and the results showed that the RMSD value of the complex tended to be stable within 100 ns, and the binding free energy of the complex was low (-10.60 kcal/mol). It was proved that oseltamivir screened by our drug repurposing method had the potential feasibility of treating PIV3. [ABSTRACT FROM AUTHOR]

The periodic table organizes chemical elements into families based on their similarity, now understood through Quantum Mechanics. However, these families were inferred from limited compounds in the nineteenth century. Since then, the number of compounds has exponentially grown, leading to the discovery of new types unknown to pioneers. This situation prompts the question of whether these families can still be discerned from the amassed data, or if their recognition is confined to specific thermochemical domains. To address this inquiry, we conducted a comprehensive exploration by comparing formulae (1771–2015) as a proxy for chemical similarity. Our findings reveal that stoichiometry not only captures a significant portion of the trends observed within families but also unveils other intriguing features of the formal structure of chemical similarity. These patterns approach equivalence classes independent of thermochemical context and demonstrate high resilience. Temporal analysis demonstrates that, since approximately 1980, similarity is diminishing due to an increasing production of unique formulae for nearly all elements. Nevertheless, chemical families endure over time and they stand out as the most robust similarity patterns. Our analysis offers compelling evidence that any study will reach the same conclusions, provided there is a sufficient diversity in input compound data. [ABSTRACT FROM AUTHOR]

This study aims to investigate the quality consistency between traditional decoction (TD) of Amomum villosum and its dispensing granule decoction (DGD). Fifteen batches of TD and nine batches of dispensing granules (manufactured by A, B, and C) were prepared and evaluated for their consistency. Firstly, The chemical similarity of TD and DGD was examined using GC and HPLC, coupled with hierarchical cluster analysis (HCA), criteria importance though intercrieria correlation(CRITIC) weighting method, and principal component analysis (PCA). Secondly, the gastrointestinal motility experiments in mice, along with the CRITIC weighting method, were employed to assess the bioequivalence of TD and DGD of Amomum villosum. Finally, the entropy weight technique-gray relative analysis(GRA) method was used to compare the quality of Amomum villosum decoctions. ①The CRITIC weighting method indicated significantly higher scores for TD than DGD (p < 0.01). HCA and PCA results demonstrated a clear distinction between TD and DGD. ②Gastrointestinal motility test results revealed no significant difference between TD and DGD in other indicators (p > 0.05).③Gray relative analysis results showed that the relative correlation of TD was more significant than that of DGD. The chemical composition of DGD and TD differed. The biological activity of DGD-A/B was consistent with that of TD, while the difference between DGD-C and TD was significant. A comprehensive evaluation showed that TD exhibited better quality than DGD. DGD manufacturers should optimize the preparation process to enhance product quality. [ABSTRACT FROM AUTHOR]

Ants patrol foliage and exert a strong selective pressure on herbivorous insects, being their primary predators. As ants are chemically oriented, some organisms that interact with them (myrmecophiles) use chemical strategies mediated by their cuticular hydrocarbons (CHCs) to deal with ants. Thus, a better understanding of the ecology and evolution of the mutualistic interactions between myrmecophiles and ants depends on the accurate recognition of these chemical strategies. Few studies have examined whether treehoppers may use an additional strategy called chemical camouflage to reduce ant aggression, and none considered highly polyphagous pest insects. We analyzed whether the chemical similarity of the CHC profiles of three host plants from three plant families (Fabaceae, Malvaceae, and Moraceae) and the facultative myrmecophilous honeydew-producing treehopper Aetalion reticulatum (Hemiptera: Aetalionidae), a pest of citrus plants, may play a role as a proximate mechanism serving as a protection against ant attacks on plants. We found a high similarity (>80%) between the CHCs of the treehoppers and two of their host plants. The treehoppers acquire CHCs through their diet, and the chemical similarity varies according to host plant. Chemical camouflage on host plants plays a role in the interaction of treehoppers with their ant mutualistic partners. [ABSTRACT FROM AUTHOR]

A novel method is introduced for estimating the degree of interactions occurring between two different compounds in a binary mixture resulting in deviations from ideality as predicted by Raoult's law. Metrics of chemical similarity between binary mixture components were used as descriptors and correlated with the Root-Mean Square Error (RMSE) associated with Raoult's law calculations of total vapour pressure prediction, including Abraham descriptors, sigma moments, and several chemical properties. The best correlation was for a quantitative structure-activity relationship (QSAR) equation using differences in Abraham parameters as descriptors (r2 = 0.7585), followed by a QSAR using differences in COSMO-RS sigma moment descriptors (r2 = 0.7461), and third by a QSAR using differences in the chemical properties of log KAW, melting point, and molecular weight as descriptors (r2 = 0.6878). Of these chemical properties, Δlog KAW had the strongest correlation with deviation from Raoult's law (RMSE) and this property alone resulted in an r2 of 0.6630. These correlations are useful for assessing the expected deviation in Raoult's law estimations of vapour pressures, a key property for estimating inhalation exposure. [ABSTRACT FROM AUTHOR]

We describe the Predicting Protein–Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between 6.8 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome‐wide database of structural models based on both traditional modeling techniques and the AlphaFold Protein Structure Database. Sequence‐ and structural similarity‐based metrics are established between template proteins, T, in the Protein Data Bank that bind compounds, C, and query proteins in the model database, Q. When the metrics exceed threshold values, it is assumed that C also binds to Q with a likelihood ratio (LR) derived from machine learning. If the relationship is based on structural similarity, the LR is based on a scoring function that measures the extent to which C is compatible with the binding site of Q as described in the LT‐scanner algorithm. For every predicted complex derived in this way, chemical similarity based on the Tanimoto coefficient identifies other small molecules that may bind to Q. An overall LR for the binding of C to Q is obtained from Naive Bayesian statistics. The PrePCI database can be queried by entering a UniProt ID or gene name for a protein to obtain a list of compounds predicted to bind to it along with associated LRs. Alternatively, entering an identifier for the compound outputs a list of proteins it is predicted to bind. Specific applications of the database to lead discovery, elucidation of drug mechanism of action, and biological function annotation are described. [ABSTRACT FROM AUTHOR]

Ants patrol foliage and exert a strong selective pressure on herbivorous insects, being their primary predators. As ants are chemically oriented, some organisms that interact with them (myrmecophiles) use chemical strategies mediated by their cuticular hydrocarbons (CHCs) to deal with ants. Thus, a better understanding of the ecology and evolution of the mutualistic interactions between myrmecophiles and ants depends on the accurate recognition of these chemical strategies. Few studies have examined whether treehoppers may use an additional strategy called chemical camouflage to reduce ant aggression, and none considered highly polyphagous pest insects. We analyzed whether the chemical similarity of the CHC profiles of three host plants from three plant families (Fabaceae, Malvaceae, and Moraceae) and the facultative myrmecophilous honeydew-producing treehopper Aetalion reticulatum (Hemiptera: Aetalionidae), a pest of citrus plants, may play a role as a proximate mechanism serving as a protection against ant attacks on plants. We found a high similarity (>80%) between the CHCs of the treehoppers and two of their host plants. The treehoppers acquire CHCs through their diet, and the chemical similarity varies according to host plant. Chemical camouflage on host plants plays a role in the interaction of treehoppers with their ant mutualistic partners.

Introduction: Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology., Areas Covered: This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples., Expert Opinion: Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

Sulphur (S) and selenium (Se) are chemically similar. Once Se is taken up, it substitutes S in S-containing amino acids. This study investigated the effect of S on selenite accumulation in the microalga Nannochloropsis oceanica. [0–28 mM] S concentrations and selenite concentrations of 0 and 30 μM were tested. S concentrations of ≤ 3 mM led to decreased cell growth whereas cultures with ≥ 4 mM were not growth limited. Se accumulation increased up to 8-fold when using S ≤ 2 mM and decreased with S 28 mM. The average relative abundance of organic Se species was selenomethionine (SeMet) 98.2 %, selenocystine (SeCys2) 1.4 % and selenomethyl selenocysteine (SeMeSeCys) 0.4 %. Total fatty acids were not affected by S limitation or Se presence. This is the first study on the effect of S on selenite accumulation, organic Se speciation of N. oceanica and its potential as an organic Se-enriched food/feed ingredient.

Screening and prioritization of chemicals is essential to ensure that available evaluation capacity is invested in those substances that are of highest concern. We, therefore, recently developed structural similarity models that evaluate the structural similarity of substances with unknown properties to known Substances of Very High Concern (SVHC), which could be an indication of comparable effects. In the current study the performance of these models is improved by (1) separating known SVHCs in more specific subgroups, (2) (re‐)optimizing similarity models for the various SVHC‐subgroups, and (3) improving interpretability of the predicted outcomes by providing a confidence score. The improvements are directly incorporated in a freely accessible web‐based tool, named the ZZS similarity tool: https://rvszoeksysteem.rivm.nl/ZzsSimilarityTool. Accordingly, this tool can be used by risk assessors, academia and industrial partners to screen and prioritize chemicals for further action and evaluation within varying frameworks, and could support the identification of tomorrow's substances of concern. [ABSTRACT FROM AUTHOR]

Motivation Interaction between transcription factor (TF) and its target genes establishes the knowledge foundation for biological researches in transcriptional regulation, the number of which is, however, still limited by biological techniques. Existing computational methods relevant to the prediction of TF-target interactions are mostly proposed for predicting binding sites, rather than directly predicting the interactions. To this end, we propose here a graph attention-based autoencoder model to predict TF-target gene interactions using the information of the known TF-target gene interaction network combined with two sequential and chemical gene characters, considering that the unobserved interactions between transcription factors and target genes can be predicted by learning the pattern of the known ones. To the best of our knowledge, the proposed model is the first attempt to solve this problem by learning patterns from the known TF-target gene interaction network. Results In this paper, we formulate the prediction task of TF-target gene interactions as a link prediction problem on a complex knowledge graph and propose a deep learning model called GraphTGI, which is composed of a graph attention-based encoder and a bilinear decoder. We evaluated the prediction performance of the proposed method on a real dataset, and the experimental results show that the proposed model yields outstanding performance with an average AUC value of 0.8864 +/- 0.0057 in the 5-fold cross-validation. It is anticipated that the GraphTGI model can effectively and efficiently predict TF-target gene interactions on a large scale. Availability Python code and the datasets used in our studies are made available at https://github.com/YanghanWu/GraphTGI [ABSTRACT FROM AUTHOR]

In this work, a new version of Rényi's divergence is presented. The expression obtained is used as a tool to identify molecules that could share some chemical or structural properties, and a data basis set of 1641 molecules is used in this study. Our results suggest that this new form of Rényi divergence could be a useful tool that will eventually permit complementary studies in which the main goal is to obtain molecules with similar properties. [ABSTRACT FROM AUTHOR]

Small molecule(SM) drugs can affect the expression of miRNAs, which plays crucial roles in many important biological processes. The chemical structure and clinical information of small molecule can simultaneously incorporate information such as anatomical distribution, therapeutic effects and structural characteristics. It is necessary to develop a novel model that incorporates small molecule chemical structure and clinical information to reveal the unknown small molecule-miRNA associations. In this study, we developed a new framework based on non-negative matrix factorization, called SMANMF, to discover the potential small molecules-miRNAs associations. First, the functional similarity of two miRNAs can be obtained by computing the overlap of the target gene sets in which the miRNAs interact together, and we integrated two types of small molecule similarities, including chemical similarity and clinical similarity. Then, we utilized a non-negative matrix factorization model to discover the unknown relationship between small molecules and miRNAs. The evaluation results indicate that our model can achieve superior prediction performance compared with previous approaches in 5-fold cross-validation. At the same time, the results of case studies also reveal that the SMANMF model has good predictive performance for predicting the potential association between small molecules and miRNAs. [ABSTRACT FROM AUTHOR]

Clinical trials of novel therapeutics for Alzheimer’s Disease (AD) have consumed a significant amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA), European Medicines Agency (EMA), or Worldwide for another indication is a more rapid and less expensive option. Therefore, we apply the scaffold searching approach based on known amyloid-beta (Aβ) inhibitor tramiprosate to screen the DrugCentral database (n = 4,642) of clinically tested drugs. As a result, menadione bisulfite and camphotamide substances with protrombogenic and neurostimulation/cardioprotection effects were identified as promising Aβ inhibitors with an improved binding affinity (ΔGbind) and blood-brain barrier permeation (logBB). Finally, the data was also confirmed by molecular dynamics simulations using implicit solvation, in particular as Molecular Mechanics Generalized Born Surface Area (MM-GBSA) model. Overall, the proposed in silico pipeline can be implemented through the early stage rational drug design to nominate some lead candidates for AD, which will be further validated in vitro and in vivo, and, finally, in a clinical trial.

A chemical space of new organic semiconductors was generated by machine learning approach using cheminformatics (in silico) techniques for efficient photodetectors, that also contributes to the big data. [Display omitted] • A chemical space of new 20,000 organic semiconductors (OSCs) is generated. • Machine learning approach is employed to get high performance photodetectors. • RDKit via BRICS method used the selected building blocks to generate OSCs. • Top twenty OSCs were screened based on synthetic accessibility score. • The potential OSCs could lead to excellent organic photodetectors or photosensors. Organic photodetectors (OPDs), the emerging next-generation candidates for light sensing applications, could compensate deficiencies of the traditional inorganic photodetectors. To get OPDs of highly improved performance, and broader applications, machine learning (ML) technique is employed. A chemical space of 20,000 organic semiconductors (OSCs) by RDKit via BRICKS (Breaking Retrosynthetically Interesting Chemical Substructures) method was achieved, from the selected building blocks of the OSC data obtained from the literature, contributing to big data. Out of these, top twenty OSCs were screened based on synthetic accessibility score, chemical similarity analysis, and structural behavior understanding. [ABSTRACT FROM AUTHOR]

Drug similarity studies are driven by the hypothesis that similar drugs should display similar therapeutic actions and thus can potentially treat a similar constellation of diseases. Drug–drug similarity has been derived by variety of direct and indirect sources of evidence and frequently shown high predictive power in discovering validated repositioning candidates as well as other in-silico drug development applications. Yet, existing resources either have limited coverage or rely on an individual source of evidence, overlooking the wealth and diversity of drug-related data sources. Hence, there has been an unmet need for a comprehensive resource integrating diverse drug-related information to derive multi-evidenced drug–drug similarities. We addressed this resource gap by compiling heterogenous information for an exhaustive set of small-molecule drugs (total of 10 367 in the current version) and systematically integrated multiple sources of evidence to derive a multi-modal drug–drug similarity network. The resulting database, 'DrugSimDB' currently includes 238 635 drug pairs with significant aggregated similarity, complemented with an interactive user-friendly web interface (http://vafaeelab.com/drugSimDB.html), which not only enables database ease of access, search, filtration and export, but also provides a variety of complementary information on queried drugs and interactions. The integration approach can flexibly incorporate further drug information into the similarity network, providing an easily extendable platform. The database compilation and construction source-code has been well-documented and semi-automated for any-time upgrade to account for new drugs and up-to-date drug information. [ABSTRACT FROM AUTHOR]

Similarity of compound chemical structures often leads to close pharmacological profiles, including binding to the same protein targets. The opposite, however, is not always true, as distinct chemical scaffolds can exhibit similar pharmacology as well. Therefore, relying on chemical similarity to known binders in search for novel chemicals targeting the same protein artificially narrows down the results and makes lead hopping impossible. In this study we attempt to design a compound similarity/distance measure that better captures structural aspects of their pharmacology and molecular interactions. The measure is based on our recently published method for compound spatial alignment with atomic property fields as a generalized 3D pharmacophoric potential. We optimized contributions of different atomic properties for better discrimination of compound pairs with the same pharmacology from those with different pharmacology using Partial Least Squares regression. Our proposed similarity measure was then tested for its ability to discriminate pharmacologically similar pairs from decoys on a large diverse dataset of 115 protein–ligand complexes. Compared to 2D Tanimoto and Shape Tanimoto approaches, our new approach led to improvement in the area under the receiver operating characteristic curve values in 66 and 58% of domains respectively. The improvement was particularly high for the previously problematic cases (weak performance of the 2D Tanimoto and Shape Tanimoto measures) with original AUC values below 0.8. In fact for these cases we obtained improvement in 86% of domains compare to 2D Tanimoto measure and 85% compare to Shape Tanimoto measure. The proposed spatial chemical distance measure can be used in virtual ligand screening.

Abstract Natural products represent a prominent source of pharmaceutically and industrially important agents. Calculating the chemical similarity of two molecules is a central task in cheminformatics, with applications at multiple stages of the drug discovery pipeline. Quantifying the similarity of natural products is a particularly important problem, as the biological activities of these molecules have been extensively optimized by natural selection. The large and structurally complex scaffolds of natural products distinguish their physical and chemical properties from those of synthetic compounds. However, no analysis of the performance of existing methods for molecular similarity calculation specific to natural products has been reported to date. Here, we present LEMONS, an algorithm for the enumeration of hypothetical modular natural product structures. We leverage this algorithm to conduct a comparative analysis of molecular similarity methods within the unique chemical space occupied by modular natural products using controlled synthetic data, and comprehensively investigate the impact of diverse biosynthetic parameters on similarity search. We additionally investigate a recently described algorithm for natural product retrobiosynthesis and alignment, and find that when rule-based retrobiosynthesis can be applied, this approach outperforms conventional two-dimensional fingerprints, suggesting it may represent a valuable approach for the targeted exploration of natural product chemical space and microbial genome mining. Our open-source algorithm is an extensible method of enumerating hypothetical natural product structures with diverse potential applications in bioinformatics.

Highlights • A totality-of-the-evidence approach was proposed to evaluate the quality consistency of TCMs. • Bioactive marker-guided clustering analysis was performed to assess the similarity. • The bioequivalence of dispensing granules was evaluated based on the bioequivalent margins of traditional decoctions. Abstract There is an increasing focus on the quality consistency evaluation of dispensing granule in traditional Chinese medicines (TCMs). According to the guideline from Chinese Pharmacopoeia Commission, the substantial equivalence of dispensing granule and traditional decoction should be determined, and the chromatographic fingerprint has been recommended as a comprehensive qualitative approach to assess the quality consistency between dispensing granule and traditional decoction. However, a high-degree chemical similarity does not equal a bioequivalence. Attempting to realize the quality evaluation by integrating chemical consistency and bioequivalence, we herein proposed a totality-of-the-evidence approach based on clustering analysis and equivalence evaluation taking the dispensing granule and traditional decoction of Scutellariae Radix (SR) as a typical case. Chemical fingerprints were developed by high performance liquid chromatography coupled with photodiode array detector and quadrupole time-of-flight mass spectrometry (HPLC-PDA/QTOF-MS). Subsequently, a feature selection strategy, integrated linear and nonlinear correlation analysis, was carried out to assess the correlation between chemical profiles and biological activities. Finally, quality consistency between the dispensing granule and the traditional decoction was determined by bioactive marker-guided hierarchical clustering analysis (HCA), k-means clustering method and bioequivalence evaluation. The available evidence suggested that not all the dispensing granule of SR were sufficiently similar to the traditional decoction. This study provides an applicable methodology for quality consistency evaluation of dispensing granule and traditional decoction in TCMs. [ABSTRACT FROM AUTHOR]

Networks, in which nodes represent compounds and edges pairwise similarity relationships, are used as coordinate-free representations of chemical space. So-called chemical space networks (CSNs) provide intuitive access to structural relationships within compound data sets and can be annotated with activity information. However, in such similarity-based networks, distances between compounds are typically determined for layout purposes and clarity and have no chemical meaning. By contrast, inter-compound distances as a measure of dissimilarity can be directly obtained from coordinate-based representations of chemical space. Herein, we introduce a CSN variant that incorporates compound distance relationships and thus further increases the information content of compound networks. The design was facilitated by adapting the Kamada-Kawai algorithm. Kamada-Kawai networks are the first CSNs that are based on numerical similarity measures, but do not depend on chosen similarity threshold values.

Networks, in which nodes represent compounds and edges pairwise similarity relationships, are used as coordinate-free representations of chemical space. So-called chemical space networks (CSNs) provide intuitive access to structural relationships within compound data sets and can be annotated with activity information. However, in such similarity-based networks, distances between compounds are typically determined for layout purposes and clarity and have no chemical meaning. By contrast, inter-compound distances as a measure of dissimilarity can be directly obtained from coordinate-based representations of chemical space. Herein, we introduce a CSN variant that incorporates compound distance relationships and thus further increases the information content of compound networks. The design was facilitated by adapting the Kamada-Kawai algorithm. Kamada-Kawai networks are the first CSNs that are based on numerical similarity measures, but do not depend on chosen similarity threshold values.

Semantic analysis of text collections was used to identify drugs with similar therapeutic activity. Natural language processing methods were applied to analyse > 2.5 mln texts from drug reviews (in English) found on patient forums and discussion boards. In order to obtain distributed word representations form the input data, a continuous bag-of-words type model was used. Such model is one of the word2vec models intended to analyse the natural language semantics. This allowed the assignment of a numeric vector to each drug name. A list of pairs of drugs with similar vectors was formed. An analysis of this list confirmed that similar word vectors correspond to either drugs with the same active compound or to drugs with close therapeutic effects that belong to the same therapeutic group. The chemical similarity in such drug pairs was found to be low. The suggested procedure was used to visualize the chemical drug space and in the search for compounds with potentially similar biological effects among drugs of different therapeutic groups. [ABSTRACT FROM AUTHOR]

Natural products represent a prominent source of pharmaceutically and industrially important agents. Calculating the chemical similarity of two molecules is a central task in cheminformatics, with applications at multiple stages of the drug discovery pipeline. Quantifying the similarity of natural products is a particularly important problem, as the biological activities of these molecules have been extensively optimized by natural selection. The large and structurally complex scaffolds of natural products distinguish their physical and chemical properties from those of synthetic compounds. However, no analysis of the performance of existing methods for molecular similarity calculation specific to natural products has been reported to date. Here, we present LEMONS, an algorithm for the enumeration of hypothetical modular natural product structures. We leverage this algorithm to conduct a comparative analysis of molecular similarity methods within the unique chemical space occupied by modular natural products using controlled synthetic data, and comprehensively investigate the impact of diverse biosynthetic parameters on similarity search. We additionally investigate a recently described algorithm for natural product retrobiosynthesis and alignment, and find that when rule-based retrobiosynthesis can be applied, this approach outperforms conventional two-dimensional fingerprints, suggesting it may represent a valuable approach for the targeted exploration of natural product chemical space and microbial genome mining. Our open-source algorithm is an extensible method of enumerating hypothetical natural product structures with diverse potential applications in bioinformatics. [ABSTRACT FROM AUTHOR]

We describe the Predicting Protein-Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between 6.8 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome-wide database of structural models based on both traditional modeling techniques and the AlphaFold Protein Structure Database. Sequence- and structural similarity-based metrics are established between template proteins, T, in the Protein Data Bank that bind compounds, C, and query proteins in the model database, Q. When the metrics exceed threshold values, it is assumed that C also binds to Q with a likelihood ratio (LR) derived from machine learning. If the relationship is based on structural similarity, the LR is based on a scoring function that measures the extent to which C is compatible with the binding site of Q as described in the LT-scanner algorithm. For every predicted complex derived in this way, chemical similarity based on the Tanimoto coefficient identifies other small molecules that may bind to Q. An overall LR for the binding of C to Q is obtained from Naive Bayesian statistics. The PrePCI database can be queried by entering a UniProt ID or gene name for a protein to obtain a list of compounds predicted to bind to it along with associated LRs. Alternatively, entering an identifier for the compound outputs a list of proteins it is predicted to bind. Specific applications of the database to lead discovery, elucidation of drug mechanism of action, and biological function annotation are described., (© 2023 The Protein Society.)

The discovery of new uses of existing drugs offers the possibility to reduce time and risk because the approved drugs have passed several phases along the drug development pipeline. In this study, we present a computational method for novel drug use prediction based on the idea that similar drugs are indicated for similar diseases. When computing drug pairwise similarities, we considered both chemical structure and drug target similarities. In validation, our new drug use predictions were found to be significantly enriched in both the biomedical literature and clinical trials. These results indicate that our method is able to successfully integrate both biomedical scientific data and literature for drug discovery.