Chemical Similarity Searching

Machine learning - Predicting Ames mutagenicity of small molecules

In modern drug discovery, detection of a compound's potential mutagenicity is crucial. However, the traditional method of mutagenicity detection using the Ames test is costly and time consuming as the compounds need to be synthesised and then tested and the results are not always accurate and reproducible. Therefore, it would be advantageous to develop robust in silico models which can accurately predict the mutagenicity of a compound prior to synthesis to overcome the inadequacies of the Ames test. After curation of a previously defined compound mutagenicity library, over 5000 molecules had their chemical fingerprints and molecular properties calculated. Using 8 classification modelling algorithms, including support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGB), a total of 112 predictive models have been constructed. Their performance has been assessed using 10-fold cross validation and a hold-out test set and some of the top performing models have been assessed using the y-randomisation approach. As a result, we have found SVM and XGB models to have good performance during the 10-fold cross validation (AUROC >0.90, sensitivity >0.85, specificity >0.75, balanced accuracy >0.80, Kappa >0.65) and on the test set (AUROC >0.65, sensitivity >0.65, specificity >0.60, balanced accuracy >0.65, Kappa >0.30). We have also identified molecular properties that are the most influential for mutagenicity prediction when combined with chemical molecular fingerprints. Using the Class A mutagenic compounds from the Ames/QSAR International Challenge Project, we were able to verify our models perform better, predicting more mutagens correctly then the StarDrop Ames mutagenicity prediction and TEST mutagenicity prediction.

In Silico Identification and Evaluation of Natural Products as Potential Tumor Necrosis Factor Function Inhibitors Using Advanced Enalos Asclepios KNIME Nodes

Tumor necrosis factor (TNF) is a regulator of several chronic inflammatory diseases, such as rheumatoid arthritis. Although anti-TNF biologics have been used in clinic, they render several drawbacks, such as patients' progressive immunodeficiency and loss of response, high cost, and intravenous administration. In order to find new potential anti-TNF small molecule inhibitors, we employed an in silico approach, aiming to find natural products, analogs of Ampelopsin H, a compound that blocks the formation of TNF active trimer. Two out of nine commercially available compounds tested, Nepalensinol B and Miyabenol A, efficiently reduced TNF-induced cytotoxicity in L929 cells and production of chemokines in mice joints' synovial fibroblasts, while Nepalensinol B also abolished TNF-TNFR1 binding in non-toxic concentrations. The binding mode of the compounds was further investigated by molecular dynamics and free energy calculation studies, using and advancing the Enalos Asclepios pipeline. Conclusively, we propose that Nepalensinol B, characterized by the lowest free energy of binding and by a higher number of hydrogen bonds with TNF, qualifies as a potential lead compound for TNF inhibitors' drug development. Finally, the upgraded Enalos Asclepios pipeline can be used for improved identification of new therapeutics against TNF-mediated chronic inflammatory diseases, providing state-of-the-art insight on their binding mode.

Automatic Identification of Analogue Series from Large Compound Data Sets: Methods and Applications

Analogue series play a key role in drug discovery. They arise naturally in lead optimization efforts where analogues are explored based on one or a few core structures. However, it is much harder to accurately identify and extract pairs or series of analogue molecules in large compound databases with no predefined core structures. This methodological review outlines the most common and recent methodological developments to automatically identify analogue series in large libraries. Initial approaches focused on using predefined rules to extract scaffold structures, such as the popular Bemis-Murcko scaffold. Later on, the matched molecular pair concept led to efficient algorithms to identify similar compounds sharing a common core structure by exploring many putative scaffolds for each compound. Further developments of these ideas yielded, on the one hand, approaches for hierarchical scaffold decomposition and, on the other hand, algorithms for the extraction of analogue series based on single-site modifications (so-called matched molecular series) by exploring potential scaffold structures based on systematic molecule fragmentation. Eventually, further development of these approaches resulted in methods for extracting analogue series defined by a single core structure with several substitution sites that allow convenient representations, such as R-group tables. These methods enable the efficient analysis of large data sets with hundreds of thousands or even millions of compounds and have spawned many related methodological developments.

QSAR-Based Computational Approaches to Accelerate the Discovery of Sigma-2 Receptor (S2R) Ligands as Therapeutic Drugs

S2R overexpression is associated with various forms of cancer as well as both neuropsychiatric disorders (e.g., schizophrenia) and neurodegenerative diseases (Alzheimer’s disease: AD). In the present study, three ligand-based methods (QSAR modeling, pharmacophore mapping, and shape-based screening) were implemented to select putative S2R ligands from the DrugBank library comprising 2000+ entries. Four separate optimization algorithms (i.e., stepwise regression, Lasso, genetic algorithm (GA), and a customized extension of GA called GreedGene) were adapted to select descriptors for the QSAR models. The subsequent biological evaluation of selected compounds revealed that three FDA-approved drugs for unrelated therapeutic indications exhibited sub-1 uM binding affinity for S2R. In particular, the antidepressant drug nefazodone elicited a S2R binding affinity Ki = 140 nM. A total of 159 unique S2R ligands were retrieved from 16 publications for model building, validation, and testing. To our best knowledge, the present report represents the first case to develop comprehensive QSAR models sourced by pooling and curating a large assemblage of structurally diverse S2R ligands, which should prove useful for identifying new drug leads and predicting their S2R binding affinity prior to the resource-demanding tasks of chemical synthesis and biological evaluation.

Recent Advances in In Silico Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

Clustering of Synthetic Routes Using Tree Edit Distance

We present a novel algorithm to compute the distance between synthetic routes based on tree edit distances. Such distances can be used to cluster synthesis routes generated using a retrosynthesis prediction tool. We show that the clustering of selected routes from a retrosynthesis analysis is performed in less than 10 s on average and only constitutes seven percent of the total time (prediction + clustering). Furthermore, we are able to show that representative routes from each cluster can be used to reduce the set of predicted routes. Finally, we show with a number of examples that the algorithm gives intuitive clusters that can be easily rationalized and that the routes in a cluster tend to use similar chemistry. The algorithm is included in the latest version of open-source AiZynthFinder software (https://github.com/MolecularAI/aizynthfinder) and as a separate package (https://github.com/MolecularAI/route-distances).

Improving Measures of Chemical Structural Similarity Using Machine Learning on Chemical-Genetic Interactions

A common strategy for identifying molecules likely to possess a desired biological activity is to search large databases of compounds for high structural similarity to a query molecule that demonstrates this activity, under the assumption that structural similarity is predictive of similar biological activity. However, efforts to systematically benchmark the diverse array of available molecular fingerprints and similarity coefficients have been limited by a lack of large-scale datasets that reflect biological similarities of compounds. To elucidate the relative performance of these alternatives, we systematically benchmarked 11 different molecular fingerprint encodings, each combined with 13 different similarity coefficients, using a large set of chemical–genetic interaction data from the yeast Saccharomyces cerevisiae as a systematic proxy for biological activity. We found that the performance of different molecular fingerprints and similarity coefficients varied substantially and that the all-shortest path fingerprints paired with the Braun-Blanquet similarity coefficient provided superior performance that was robust across several compound collections. We further proposed a machine learning pipeline based on support vector machines that offered a fivefold improvement relative to the best unsupervised approach. Our results generally suggest that using high-dimensional chemical–genetic data as a basis for refining molecular fingerprints can be a powerful approach for improving prediction of biological functions from chemical structures.

Systems pharmacology dissection of action mechanisms for herbs in osteoporosis treatment

Objective

Osteoporosis has become the biggest cause of non-fatal health issue. Currently, the limitations of traditional anti-osteoporosis drugs such as long-term ill-effects and drug resistance, have raised concerns toward complementary and alternative therapies, particularly herbal medicines and their natural active compounds. Thus, this study aimed to provide an integrative analysis of active chemicals, drug targets and interacting pathways of the herbs for osteoporosis treatment.

Methods

Here, we introduced a systematic pharmacology model, combining the absorption, distribution, metabolism, and excretion (ADME) screening model, drug targeting and network pharmacology, to probe into the therapeutic mechanisms of herbs in osteoporosis.

Results

We obtained 86 natural compounds with favorable pharmacokinetic profiles and their 58 targets from seven osteoporosis-related herbs. Network analysis revealed that they probably synergistically work through multiple mechanisms, such as suppressing inflammatory response, maintaining bone metabolism or improving organism immunity, to benefit patients with osteoporosis. Furthermore, experimental results showed that all the five compounds (calycosin, asperosaponin VI, hederagenin, betulinic acid and luteolin) enhanced osteoblast proliferation and differentiation in vitro, which corroborated the validity of this system pharmacology approach. Notably, gentisin and aureusidin among the identified compounds were first predicted to be associated with osteoporosis.

Conclusion

Herbs and their natural compounds, being characterized as the classical combination therapies, might be engaged in multiple mechanisms to coordinately improve the osteoporosis symptoms. This work may contribute to offer novel strategies and clues for the therapy and drug discovery of osteoporosis and other complex diseases.

Systematic exploration of Astragalus membranaceus and Panax ginseng as immune regulators: Insights from the comparative biological and computational analysis

BACKGROUND

Immune system plays a decisive role for defending various pathogenic microorganisms. Astragalus membranaceus (AM) and Panax ginseng (PG) are two tonic herbs used in traditional Chinese medicine (TCM) as immune booster and help to control diseases with their healthy synergistic effect on immune system.

PURPOSE

This study was aimed to investigate the promote effect and molecular mechanisms of AM and PG on immune system as booster and to control the target diseases using animal and computational systematic study.

METHODS

Computational models including absorption, distribution, metabolism, and elimination (ADME) with weighted ensemble similarity (WES) algorithm-based models and ClueGo network analysis were used to find the potential bioactive compounds targets and pathways, which were responsible for immune regulation. Viscera index analysis, proliferation activity of splenic lymphocytes and cytotoxic activity of NK cells assays were performed to validate the effect of AM and PG on immune system of long-term administrated mice. Metabonomic study of mice plasma was conducted to investigate effect of AM and PG on the endogenous metabolic perturbations, together with correlation analysis.

RESULTS

AM and PG simultaneously showed the ability to strengthen the immune system function including enhancement of spleen and thymus index, proliferation of splenic lymphocytes and cytotoxic activity of NK cells. Besides, the different molecular mechanisms of AM and PG on immune regulation were also investigated by analyzing the potential bioactive compounds, enzymes actions and pathways. Quercetin, formononetin and kaempferol were the main immune-related compounds in AM, while ginsenoside Ra1, ginsenoside Rh1 and kaempferol in PG. About 10 target proteins were found close to immune regulation, including acetylcholinesterase (ACHE, common target in AM and PG), sphingosine kinase 1(SPHK1), cytidine deaminase (CDA), and Choline O-acetyltransferase (CHAT). Glycerophospholipid metabolism was regulated in both AM and PG groups. Pyrimidine metabolism and sphingolipid metabolism were considered as the special pathway in AM groups. Energy metabolism and glycerolipid metabolism were the special pathways in PG groups.

CONCLUSION

A novel comprehensive molecular mechanism analysis method was established and applied to clarify the scientific connotation of AM and PG as immune regulation, with similar herbal tonic effect provided in clinical practice of TCM, which can provide a new line of research for drug development (immune booster) using AM and PG.

Predictive Models to Identify Small Molecule Activators and Inhibitors of Opioid Receptors

Opioid receptors (OPRs) are the main targets for the treatment of pain and related disorders. The opiate compounds that activate these receptors are effective analgesics but their use leads to adverse effects, and they often are highly addictive drugs of abuse. There is an urgent need for alternative chemicals that are analgesics and to reduce/avoid the unwanted effects in order to relieve the public health crisis of opioid addiction. Here, we aim to develop computational models to predict the OPR activity of small molecule compounds based on chemical structures and apply these models to identify novel OPR active compounds. We used four different machine learning algorithms to build models based on quantitative high throughput screening (qHTS) data sets of three OPRs in both agonist and antagonist modes. The best performing models were applied to virtually screen a large collection of compounds. The model predicted active compounds were experimentally validated using the same qHTS assays that generated the training data. Random forest was the best classifier with the highest performance metrics, and the mu OPR (OPRM)-agonist model achieved the best performance measured by AUC-ROC (0.88) and MCC (0.7) values. The model predicted actives resulted in hit rates ranging from 2.3% (delta OPR-agonist) to 15.8% (OPRM-agonist) after experimental confirmation. Compared to the original assay hit rate, all models enriched the hit rate by ≥2-fold. Our approach produced robust OPR prediction models that can be applied to prioritize compounds from large libraries for further experimental validation. The models identified several novel potent compounds as activators/inhibitors of OPRs that were confirmed experimentally. The potent hits were further investigated using molecular docking to find the interactions of the novel ligands in the active site of the corresponding OPR.

Discovery of Novel eEF2K Inhibitors Using HTS Fingerprint Generated from Predicted Profiling of Compound-Protein Interactions

Background: Eukaryotic elongation factor 2 kinase (eEF2K) regulates the elongation stage of protein synthesis by phosphorylating eEF2, a process related to various diseases including cancer and cardiovascular and neurodegenerative diseases. In this study, we describe the identification of novel eEF2K inhibitors using high-throughput screening fingerprints (HTSFP) generated from predicted profiling of compound-protein interactions (CPIs). Methods: We utilized computationally generated HTSFPs referred to as chemical genomics-based fingerprint (CGBFP). Generally, HTSFPs are generated from multiple biochemical or cell-based assay data. On the other hand, CGBFPs are generated from computational prediction of CPIs using the Chemical Genomics-Based Virtual Screening (CGBVS) method. Therefore, CGBFPs do not have missing information mainly caused by the absence of assay data. Results: Chemogenomics-Based Similarity Profiling (CGBSP) of the screening library (2.6 million compounds) yielded 27 compounds which were evaluated for in vitro eEF2K inhibitory activity. Three compounds with interesting results were identified. Compounds 2 (IC50 = 11.05 μM) and 4 (IC50 = 43.54 μM) are thieno[2,3-b]pyridine derivatives that have the same scaffolds with a known eEF2K inhibitor, while compound 13 (IC50 = 70.13 μM) was a new thiophene-2-amine-type eEF2K inhibitor. Conclusions: CGBSP supplied an efficient strategy in the identification of novel eEF2K inhibitors and provided useful scaffolds for optimization.

Fentanyl-related substance scheduling as an effective drug control strategy

Fentanyl is now the primary driver of the current opioid crisis. Fentanyl and its analogues are subject to the Controlled Substances Act of 1970, the Controlled Substances Analogue Enforcement Act of 1986 (Federal Analogue Act), state laws, international treaties, and the laws of foreign countries. The appearance of novel psychoactive substances led to further legislative developments in scheduling. New fentanyl analogues proliferated in a manner previously unseen since about 2016. Overdose deaths of these fentanyl analogues prompted the Drug Enforcement Administration to reactively emergency schedule each new fentanyl analogue as it appeared. The international community also acted. Finally, on February 6, 2018, a proactive temporary (emergency) class-wide scheduling of fentanyl-related substances was implemented based upon the fentanyl core structure to save lives. This action spurred a similar action in China. Fentanyl analogues fell dramatically in the marketplace, despite further increases in fentanyl itself. Congress temporarily extended this scheduling, but it will soon expire. Opposition to permanent class-wide was lodged due to concerns over law enforcement overreach, inadequate Health and Human Services input, and hindrance of research. This paper reaffirms the importance of a class-based scheduling strategy while also arguing for increased research of schedule I controlled substances.

Potent sialic acid inhibitors that target influenza A virus hemagglutinin

Eradicating influenza A virus (IAV) is difficult, due to its genetic drift and reassortment ability. As the infectious cycle is initiated by the influenza glycoprotein, hemagglutinin (HA), which mediates the binding of virions to terminal sialic acids moieties, HA is a tempting target of anti-influenza inhibitors. However, the complexity of the HA structure has prevented delineation of the structural characterization of the HA protein-ligand complex. Our computational strategy efficiently analyzed > 200,000 records of compounds held in the United States National Cancer Institute (NCI) database and identified potential HA inhibitors, by modeling the sialic acid (SA) receptor binding site (RBS) for the HA structure. Our modeling revealed that compound NSC85561 showed significant antiviral activity against the IAV H1N1 strain with EC50 values ranging from 2.31 to 2.53 µM and negligible cytotoxicity (CC50 > 700 µM). Using the NSC85561 compound as the template to generate 12 derivatives, robust bioassay results revealed the strongest antiviral efficacies with NSC47715 and NSC7223. Virtual screening clearly identified three SA receptor binding site inhibitors that were successfully validated in experimental data. Thus, our computational strategy has identified SA receptor binding site inhibitors against HA that show IAV-associated antiviral activity.

Drought Exposed Burkholderia seminalis JRBHU6 Exhibits Antimicrobial Potential Through Pyrazine-1,4-Dione Derivatives Targeting Multiple Bacterial and Fungal Proteins

The present study aimed to explore the antimicrobial potentials of soil bacteria and identify the bioactive compounds and their likely targets through in silico studies. A total 53 bacterial isolates were screened for their antimicrobial potential of which the strain JRBHU6 showing highest antimicrobial activity was identified as Burkholderia seminalis (GenBank accession no. MK500868) based on 16S ribosomal RNA (rRNA) gene sequencing and phylogenetic analysis. B. seminalis JRBHU6 also produced hydrolytic enzymes chitinases and cellulase of significance in accrediting its antimicrobial nature. The bioactive metabolites produced by the isolate were extracted in different organic solvents among which methanolic extract showed best growth-suppressing activities toward multidrug resistant Staphylococcus aureus and fungal strains, viz Fusarium oxysporum, Aspergillus niger, Microsporum gypseum, Trichophyton mentagrophytes, and Trichoderma harzianum. The antimicrobial compounds were purified using silica gel thin layer chromatography and high-performance liquid chromatography (HPLC). On the basis of spectroscopic analysis, the bioactive metabolites were identified as pyrrolo(1,2-a)pyrazine-1,4-dione,hexahydro (PPDH) and pyrrolo(1,2-a)pyrazine-1,4-dione, hexahydro-3(2-methylpropyl) (PPDHMP). In silico molecular docking studies showed the bioactive compounds targeting fungal and bacterial proteins, among which PPDHMP was multitargeting in nature as reported for the first time through this study.

Structure Driven Prediction of Chromatographic Retention Times: Applications to Pharmaceutical Analysis

Pharmaceutical drug development relies heavily on the use of Reversed-Phase Liquid Chromatography methods. These methods are used to characterize active pharmaceutical ingredients and drug products by separating the main component from related substances such as process related impurities or main component degradation products. The results presented here indicate that retention models based on Quantitative Structure Retention Relationships can be used for de-risking methods used in pharmaceutical analysis and for the identification of optimal conditions for separation of known sample constituents from postulated/hypothetical components. The prediction of retention times for hypothetical components in established methods is highly valuable as these compounds are not usually readily available for analysis. Here we discuss the development and optimization of retention models, selection of the most relevant structural molecular descriptors, regression model building and validation. We also present a practical example applied to chromatographic method development and discuss the accuracy of these models on selection of optimal separation parameters.

Identification of a series of hair-cell MET channel blockers that protect against aminoglycoside-induced ototoxicity

To identify small molecules that shield mammalian sensory hair cells from the ototoxic side effects of aminoglycoside antibiotics, 10,240 compounds were initially screened in zebrafish larvae, selecting for those that protected lateral-line hair cells against neomycin and gentamicin. When the 64 hits from this screen were retested in mouse cochlear cultures, 8 protected outer hair cells (OHCs) from gentamicin in vitro without causing hair-bundle damage. These 8 hits shared structural features and blocked, to varying degrees, the OHC's mechano-electrical transducer (MET) channel, a route of aminoglycoside entry into hair cells. Further characterization of one of the strongest MET channel blockers, UoS-7692, revealed it additionally protected against kanamycin and tobramycin and did not abrogate the bactericidal activity of gentamicin. UoS-7692 behaved, like the aminoglycosides, as a permeant blocker of the MET channel; significantly reduced gentamicin-Texas red loading into OHCs; and preserved lateral-line function in neomycin-treated zebrafish. Transtympanic injection of UoS-7692 protected mouse OHCs from furosemide/kanamycin exposure in vivo and partially preserved hearing. The results confirmed the hair-cell MET channel as a viable target for the identification of compounds that protect the cochlea from aminoglycosides and provide a series of hit compounds that will inform the design of future otoprotectants.

LINGO-DL: a text-based approach for molecular similarity searching

The line notations of chemical structures are more compact than those of graphs and connection tables, so they can be useful for storing and transferring a large number of molecular structures. The simplified molecular input line system (SMILES) representation is the most extensively used, as it is much easier to utilise and comprehend than others, and it can be generated automatically from connection tables. A SMILES represents and encodes the molecule structure. It has been used by an existing method, LINGO, to calculate the molecular similarities and predict the structure-related properties. The LINGO method decomposes a canonical SMILES into a set of substrings of four characters referred to as LINGOs. The purpose of LINGO method is to measure the similarity between a pair of molecules by comparing the LINGOs that occur in each molecule. This paper aims to introduce an alternative version of the LINGO method using LINGOs of different lengths, called LINGO-DL. LINGO-DL is based on the fragmentation of canonical SMILES into substrings of three different lengths rather than one in LINGO method. Retrospective virtual screening experiments with MDDR, DUD, and MUV datasets show that the LINGO-DL outperforms the LINGO method, especially when the active molecules being sought have a high degree of structural heterogeneity.

Prediction of activity and selectivity profiles of human Carbonic Anhydrase inhibitors using machine learning classification models

The development of selective inhibitors of the clinically relevant human Carbonic Anhydrase (hCA) isoforms IX and XII has become a major topic in drug research, due to their deregulation in several types of cancer. Indeed, the selective inhibition of these two isoforms, especially with respect to the homeostatic isoform II, holds great promise to develop anticancer drugs with limited side effects. Therefore, the development of in silico models able to predict the activity and selectivity against the desired isoform(s) is of central interest. In this work, we have developed a series of machine learning classification models, trained on high confidence data extracted from ChEMBL, able to predict the activity and selectivity profiles of ligands for human Carbonic Anhydrase isoforms II, IX and XII. The training datasets were built with a procedure that made use of flexible bioactivity thresholds to obtain well-balanced active and inactive classes. We used multiple algorithms and sampling sizes to finally select activity models able to classify active or inactive molecules with excellent performances. Remarkably, the results herein reported turned out to be better than those obtained by models built with the classic approach of selecting an a priori activity threshold. The sequential application of such validated models enables virtual screening to be performed in a fast and more reliable way to predict the activity and selectivity profiles against the investigated isoforms.

Comparison of Chemical Structure and Cell Morphology Information for Multitask Bioactivity Predictions

The understanding of the mechanism-of-action (MoA) of compounds and the prediction of potential drug targets play an important role in small-molecule drug discovery. The aim of this work was to compare chemical and cell morphology information for bioactivity prediction. The comparison was performed using bioactivity data from the ExCAPE database, image data (in the form of CellProfiler features) from the Cell Painting data set (the largest publicly available data set of cell images with ∼30,000 compound perturbations), and extended connectivity fingerprints (ECFPs) using the multitask Bayesian matrix factorization (BMF) approach Macau. We found that the BMF Macau and random forest (RF) performance were overall similar when ECFPs were used as compound descriptors. However, BMF Macau outperformed RF in 159 out of 224 targets (71%) when image data were used as compound information. Using BMF Macau, 100 (corresponding to about 45%) and 90 (about 40%) of the 224 targets were predicted with high predictive performance (AUC > 0.8) with ECFP data and image data as side information, respectively. There were targets better predicted by image data as side information, such as β-catenin, and others better predicted by fingerprint-based side information, such as proteins belonging to the G-protein-Coupled Receptor 1 family, which could be rationalized from the underlying data distributions in each descriptor domain. In conclusion, both cell morphology changes and chemical structure information contain information about compound bioactivity, which is also partially complementary, and can hence contribute to in silico MoA analysis.

Incorporating structural similarity into a scoring function to enhance the prediction of binding affinities

In this study, we developed a novel algorithm to improve the screening performance of an arbitrary docking scoring function by recalibrating the docking score of a query compound based on its structure similarity with a set of training compounds, while the extra computational cost is neglectable. Two popular docking methods, Glide and AutoDock Vina were adopted as the original scoring functions to be processed with our new algorithm and similar improvement performance was achieved. Predicted binding affinities were compared against experimental data from ChEMBL and DUD-E databases. 11 representative drug receptors from diverse drug target categories were applied to evaluate the hybrid scoring function. The effects of four different fingerprints (FP2, FP3, FP4, and MACCS) and the four different compound similarity effect (CSE) functions were explored. Encouragingly, the screening performance was significantly improved for all 11 drug targets especially when CSE = S⁴ (S is the Tanimoto structural similarity) and FP2 fingerprint were applied. The average predictive index (PI) values increased from 0.34 to 0.66 and 0.39 to 0.71 for the Glide and AutoDock vina scoring functions, respectively. To evaluate the performance of the calibration algorithm in drug lead identification, we also imposed an upper limit on the structural similarity to mimic the real scenario of screening diverse libraries for which query ligands are general-purpose screening compounds and they are not necessarily structurally similar to reference ligands. Encouragingly, we found our hybrid scoring function still outperformed the original docking scoring function. The hybrid scoring function was further evaluated using external datasets for two systems and we found the PI values increased from 0.24 to 0.46 and 0.14 to 0.42 for A2AR and CFX systems, respectively. In a conclusion, our calibration algorithm can significantly improve the virtual screening performance in both drug lead optimization and identification phases with neglectable computational cost.

How μ-opioid receptor recognizes fentanyl

Roughly half of the drug overdose-related deaths in the United States are related to synthetic opioids represented by fentanyl which is a potent agonist of mu-opioid receptor (mOR). In recent years, X-ray crystal structures of mOR in complex with morphine derivatives have been determined; however, structural basis of mOR activation by fentanyl-like opioids remains lacking. Exploiting the X-ray structure of BU72-bound mOR and several molecular simulation techniques, we elucidated the detailed binding mechanism of fentanyl. Surprisingly, in addition to the salt-bridge binding mode common to morphinan opiates, fentanyl can move deeper and form a stable hydrogen bond with the conserved His2976.52, which has been suggested to modulate mOR's ligand affinity and pH dependence by previous mutagenesis experiments. Intriguingly, this secondary binding mode is only accessible when His2976.52 adopts a neutral HID tautomer. Alternative binding modes may represent a general mechanism in G protein-coupled receptor-ligand recognition.

Decomposition of the Simulant 2-Chloroethyl Ethyl Sulfide Blister Agent under Ambient Conditions Using Metal-Organic Frameworks

Metal organic frameworks (MOFs) have been suggested as promising materials for application in the degradation of chemical warfare agents, with the majority of studies to date focusing on nerve agents. One of the most prominent MOFs used in the detoxification of nerve agents is UiO-66, which is of interest as a future nerve agent decontaminant. However, blister agents, which constitute one of the most toxic and highly reactive categories of chemical agents, are yet to be examined as gas-phase decontamination targets using MOF structures. In this study, a novel type of UiO-66 with a smaller particle size, namely, UiO-66S, was used as a decontaminant for the blister agent simulant, 2-chloroethyl ethyl sulfide (2-CEES). The gas-phase chemical adsorption and decomposition of 2-CEES were demonstrated for the first time, with an estimated t_1/2 of 1.34 h. This value is the highest reported value for an MOF in gas-phase reaction conditions. The obtained nontoxic degradation products were identified, and the reaction mechanism was studied using density functional theory calculations. Furthermore, the synthesized UiO-66S catalyst also exhibits superior catalytic ability toward nerve agent simulants (diisopropyl fluorophosphate).The results of the study provide a firm basis for the use of UiO-66S as a future decontaminant for both nerve and blister agents.

DockCoV2: a drug database against SARS-CoV-2

The current state of the COVID-19 pandemic is a global health crisis. To fight the novel coronavirus, one of the best-known ways is to block enzymes essential for virus replication. Currently, we know that the SARS-CoV-2 virus encodes about 29 proteins such as spike protein, 3C-like protease (3CLpro), RNA-dependent RNA polymerase (RdRp), Papain-like protease (PLpro), and nucleocapsid (N) protein. SARS-CoV-2 uses human angiotensin-converting enzyme 2 (ACE2) for viral entry and transmembrane serine protease family member II (TMPRSS2) for spike protein priming. Thus in order to speed up the discovery of potential drugs, we develop DockCoV2, a drug database for SARS-CoV-2. DockCoV2 focuses on predicting the binding affinity of FDA-approved and Taiwan National Health Insurance (NHI) drugs with the seven proteins mentioned above. This database contains a total of 3,109 drugs. DockCoV2 is easy to use and search against, is well cross-linked to external databases, and provides the state-of-the-art prediction results in one site. Users can download their drug-protein docking data of interest and examine additional drug-related information on DockCoV2. Furthermore, DockCoV2 provides experimental information to help users understand which drugs have already been reported to be effective against MERS or SARS-CoV. DockCoV2 is available at https://covirus.cc/drugs/.

Profiling and analysis of chemical compounds using pointwise mutual information

Pointwise mutual information (PMI) is a measure of association used in information theory. In this paper, PMI is used to characterize several publicly available databases (DrugBank, ChEMBL, PubChem and ZINC) in terms of association strength between compound structural features resulting in database PMI interrelation profiles. As structural features, substructure fragments obtained by coding individual compounds as MACCS, PubChemKey and ECFP fingerprints are used. The analysis of publicly available databases reveals, in accord with other studies, unusual properties of DrugBank compounds which further confirms the validity of PMI profiling approach. Z-standardized relative feature tightness (ZRFT), a PMI-derived measure that quantifies how well the given compound's feature combinations fit these in a particular compound set, is applied for the analysis of compound synthetic accessibility (SA), as well as for the classification of compounds as easy (ES) and hard (HS) to synthesize. ZRFT value distributions are compared with these of SYBA and SAScore. The analysis of ZRFT values of structurally complex compounds in the SAVI database reveals oligopeptide structures that are mispredicted by SAScore as HS, while correctly predicted by ZRFT and SYBA as ES. Compared to SAScore, SYBA and random forest, ZRFT predictions are less accurate, though by a narrow margin (AccZRFT = 94.5%, AccSYBA = 98.8%, AccSAScore = 99.0%, AccRF = 97.3%). However, ZRFT ability to distinguish between ES and HS compounds is surprisingly high considering that while SYBA, SAScore and random forest are dedicated SA models, ZRFT is a generic measurement that merely quantifies the strength of interrelations between structural feature pairs. The results presented in the current work indicate that structural feature co-occurrence, quantified by PMI or ZRFT, contains a significant amount of information relevant to physico-chemical properties of organic compounds.

Deciphering the multi-scale mechanisms of Tephrosia purpurea against polycystic ovarian syndrome (PCOS) and its major psychiatric comorbidities: Studies from network pharmacological perspective

Tephrosia purpurea (T. purpurea), a plant belonging to Fabaceae (pea) family, is a well-known Ayurvedic herb and commonly known as Sarapunkha in traditional Indian medicinal system. Described as "Sarwa wranvishapaka", i.e. having a capability to heal all types of wounds, it is particularly recognized for its usage in splenomegaly. Towards exploring the comprehensive effects of T. purpurea against polycystic ovarian syndrome (PCOS) and three comorbid neuropsychiatric diseases (anxiety, depression, and bipolar disorder), its constituent phytochemicals (PCs) were extensively reviewed and their network pharmacology evaluation was carried out in this study. The complex regulatory potential of its 76 PCs against PCOS is enquired by developing and analyzing high confidence tripartite networks of protein targets of each phytochemical at both pathway and disease association scales. We also developed a high-confidence human Protein-Protein Interaction (PPI) sub-network specific to PCOS, explored its modular architecture, and probed 30 drug-like phytochemicals (DPCs) having multi-module regulatory potential. The phytochemicals showing good binding affinity towards their protein targets were also evaluated for similarity against currently available approved drugs present in DrugBank. Multi-targeting and synergistic capacities of 12 DPCs against 10 protein targets were identified and evaluated using molecular docking and interaction analyses. Eight DPCs as a potential source of PCOS and its comorbidity regulators are reported in T. purpurea. The results of network-pharmacology study highlight the therapeutic relevance of T. purpurea as PCOS-regulator and demonstrate the effectiveness of the approach in revealing action-mechanism of Ayurvedic herbs from holistic perspective.

Exploiting cheminformatic and machine learning to navigate the available chemical space of potential small molecule inhibitors of SARS-CoV-2

The current life-threatening and tenacious pandemic eruption of coronavirus disease in 2019 (COVID-19) has posed a significant global hazard concerning high mortality rate, economic meltdown, and everyday life distress. The rapid spread of COVID-19 demands countermeasures to combat this deadly virus. Currently, there are no drugs approved by the FDA to treat COVID-19. Therefore, discovering small molecule therapeutics for treating COVID-19 infection is essential. So far, only a few small molecule inhibitors are reported for coronaviruses. There is a need to expand the small chemical space of coronaviruses inhibitors by adding potent and selective scaffolds with anti-COVID activity. In this context, the huge antiviral chemical space already available can be analysed using cheminformatic and machine learning to unearth new scaffolds. We created three specific datasets called "antiviral dataset" (N = 38,428) "drug-like antiviral dataset" (N = 20,963) and "anticorona dataset" (N = 433) for this purpose. We analyzed the 433 molecules of "anticorona dataset" for their scaffold diversity, physicochemical distributions, principal component analysis, activity cliffs, R-group decomposition, and scaffold mapping. The scaffold diversity of the "anticorona dataset" in terms of Murcko scaffold analysis demonstrates a thorough representation of diverse chemical scaffolds. However, physicochemical descriptor analysis and principal component analysis demonstrated negligible drug-like features for the "anticorona dataset" molecules. The "antiviral dataset" and "drug-like antiviral dataset" showed low scaffold diversity as measured by the Gini coefficient. The hierarchical clustering of the "antiviral dataset" against the "anticorona dataset" demonstrated little molecular similarity. We generated a library of frequent fragments and polypharmacological ligands targeting various essential viral proteins such as main protease, helicase, papain-like protease, and replicase polyprotein 1ab. Further structural and chemical features of the "anticorona dataset" were compared with SARS-CoV-2 repurposed drugs, FDA-approved drugs, natural products, and drugs currently in clinical trials. Using machine learning tool DCA (DMax Chemistry Assistant), we converted the "anticorona dataset" into an elegant hypothesis with significant functional biological relevance. Machine learning analysis uncovered that FDA approved drugs, Tizanidine HCl, Cefazolin, Raltegravir, Azilsartan, Acalabrutinib, Luliconazole, Sitagliptin, Meloxicam (Mobic), Succinyl sulfathiazole, Fluconazole, and Pranlukast could be repurposed as effective drugs for COVID-19. Fragment-based scaffold analysis and R-group decomposition uncovered pyrrolidine and the indole molecular scaffolds as the potent fragments for designing and synthesizing the novel drug-like molecules for targeting SARS-CoV-2. This comprehensive and systematic assessment of small-molecule viral therapeutics' entire chemical space realised critical insights to potentially privileged scaffolds that could aid in enrichment and rapid discovery of efficacious antiviral drugs for COVID-19.

Quantifying Mineral-Ligand Structural Similarities: Bridging the Geological World of Minerals with the Biological World of Enzymes

Metal compounds abundant on Early Earth are thought to play an important role in the origins of life. Certain iron-sulfur minerals for example, are proposed to have served as primitive metalloenzyme cofactors due to their ability to catalyze organic synthesis processes and facilitate electron transfer reactions. An inherent difficulty with studying the catalytic potential of many metal compounds is the wide range of data and parameters to consider when searching for individual minerals and ligands of interest. Detecting mineral-ligand pairs that are structurally analogous enables more relevant selections of data to study, since structural affinity is a key indicator of comparable catalytic function. However, current structure-oriented approaches tend to be subjective and localized, and do not quantify observations or compare them with other potential targets. Here, we present a mathematical approach that compares structural similarities between various minerals and ligands using molecular similarity metrics. We use an iterative substructure search in the crystal lattice, paired with benchmark structural similarity methods. This structural comparison may be considered as a first stage in a more advanced analysis tool that will include a range of chemical and physical factors when computing mineral-ligand similarity. This approach will seek relationships between the mineral and enzyme worlds, with applications to the origins of life, ecology, catalysis, and astrobiology.

How μ-Opioid Receptor Recognizes Fentanyl

In 2019, drug overdose has claimed over 70,000 lives in the United States. More than half of the deaths are related to synthetic opioids represented by fentanyl which is a potent agonist of mu-opioid receptor (mOR). In recent years, the crystal structures of mOR in complex with morphine derivatives have been determined; however, structural basis of mOR activation by fentanyl-like synthetic opioids remains lacking. Exploiting the X-ray structure of mOR bound to a morphinan ligand and several state-of-the-art simulation techniques, including weighted ensemble and continuous constant pH molecular dynamics, we elucidated the detailed binding mechanism of fentanyl with mOR. Surprisingly, in addition to forming a salt-bridge with Asp1473.32 in the orthosteric site common to morphinan opiates, fentanyl can move deeper and bind mOR through hydrogen bonding with a conserved histidine His2976.52, which has been shown to modulate mOR's ligand affinity and pH dependence in mutagenesis experiments, but its precise role remains unclear. Intriguingly, the secondary binding mode is only accessible when His297 adopts a neutral HID tautomer. Alternative binding modes and involvement of tautomer states may represent general mechanisms in G protein-coupled receptor (GPCR)-ligand recognition. Our work provides a starting point for understanding the molecular basis of mOR activation by fentanyl which has many analogs emerging at a rapid pace. The knowledge may also inform the design of safer analgesics to combat the opioid crisis. Current protein simulation studies employ standard protonation and tautomer states; our work demonstrates the need to move beyond the practice to advance our understanding of protein-ligand recognition.

Repurposing Known Drugs as Covalent and Non-covalent Inhibitors of the SARS-CoV-2 Papain-Like Protease

In the absence of an approved vaccine, developing effective severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antivirals is essential to tackle the current pandemic health crisis due to the coronavirus disease 2019 (COVID-19) spread. As any traditional drug discovery program is a time-consuming and costly process requiring more than one decade to be completed, in silico repurposing of existing drugs is the preferred way for rapidly selecting promising clinical candidates. We present a virtual screening campaign to identify covalent and non-covalent inhibitors of the SARS-CoV-2 papain-like protease (PLpro) showing potential multitarget activities (i.e., a desirable polypharmacology profile) for the COVID-19 treatment. A dataset including 688 phase III and 1,702 phase IV clinical trial drugs was downloaded from ChEMBL (version 27.1) and docked to the recently released crystal structure of PLpro in complex with a covalently bound peptide inhibitor. The obtained results were analyzed by combining protein-ligand interaction fingerprint similarities, conventional docking scores, and MM-GBSA-binding free energies and allowed the identification of some interesting candidates for further in vitro testing. To the best of our knowledge, this study represents the first attempt to repurpose drugs for a covalent inhibition of PLpro and could pave the way for new therapeutic strategies against COVID-19.

Chemoinformatics-based enumeration of chemical libraries: a tutorial

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.

Systems pharmacology in combination with proteomics reveals underlying mechanisms of Xihuang pill against triple-negative breast cancer

Xihuang pill (XHP), a traditional Chinese herbal formula, has been clinically used as an adjuvant therapy against triple-negative breast cancer (TNBC) via inhibiting cancer cell invasion and proliferation, as well as promoting cancer cell apoptosis. However, its anti-TNBC bio-active ingredients and possible mechanisms are still unclear. Herein, the hub bio-active compounds and underlying mechanisms of XHP against TNBC were systematically elucidated by integrating systems pharmacology approach and in vitro proteomics analysis. Using systems pharmacology analysis and molecular docking evaluation, 28 bio-active compounds and 10 potential therapeutic targets of XHP were identified. Functional analysis showed that the core therapeutic targets against TNBC were mainly involved in epidermal growth factor receptor (EGFR)-phosphatidylinositol 3-kinase (PI3K)-AKT signaling pathway to prevent cancer cell proliferation and angiogenesis, as well as to enhance cancer cell apoptosis. The in vitro proteomics analysis identified 153 differentially expressed proteins (DEPs), including HASP90AA1, AKT1, and EGFR, which were also identified as therapeutic targets against TNBC through systems pharmacology analysis. Protein function analysis showed that the DEPs were mainly involved in PI3K-AKT signaling pathway, which was consistent with the result of systems pharmacology, suggesting the reliability of systems pharmacology analysis. Taken together, these findings uncover the underlying mechanism of XHP against TNBC, and provide a scientific method for the rational development of traditional Chinese medicine.

Systematic analyses on the potential immune and anti-inflammatory mechanisms of Shufeng Jiedu Capsule against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)-caused pneumonia

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A systematic study integrated is proposed to illustrate the potential immune and anti-inflammatory mechanisms of SFJDC against SARS-CoV-2.

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SFJDC regulates related targets, showing the potential anti-novel coronavirus effect.

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The work can provide a better understanding of the therapeutic mechanism of SFJDC for treating SARS-CoV-2.

The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)-caused pneumonia (Coronavirus disease −19, COVID-19), has resulted in a global health emergency. However, there is no vaccine or effective antiviral treatment against the newly emerged coronavirus and identifying the available therapeutics as soon as possible is critical for the response to the spread of SARS-CoV-2. Shufeng Jiedu Capsule (SFJDC), a well-known prescription of Traditional Chinese Medicine (TCM) in China, has been widely used in treating upper respiratory tract infections and acute lung injury, owing to its immunomodulatory and anti-inflammatory effects. Despite the definite evidence of effective use of SFJDC in the diagnosis and treatment of pneumonia caused by SARS-CoV-2, the underlying action mechanism remains unknown. Currently, a systematic study integrated with absorption, distribution, metabolism and excretion (ADME) evaluation, target prediction, network construction and functional bioinformatics analyses is proposed to illustrate the potential immune and anti-inflammatory mechanisms of SFJDC against SARS-CoV-2. Additionally, to further validate the reliability of the interactions and binding affinities between drugs and targets, docking, Molecular dynamics Simulations (MD) simulations and Molecular Mechanics/Poisson-Boltzmann Surface Area approach (MM-PBSA) calculations were carried out. The results demonstrate that SFJDC regulates the immunomodulatory and anti-inflammatory related targets on multiple pathways through its active ingredients, showing the potential anti-novel coronavirus effect. Overall, the work can provide a better understanding of the therapeutic mechanism of SFJDC for treating SARS-CoV-2 pneumonia from multi-scale perspectives, and may also offer a valuable clue for developing novel pharmaceutical strategies to control the current coronavirus.

Genetic Algorithm-based Feature Selection Approach for Enhancing the Effectiveness of Similarity Searching in Ligand-based Virtual Screening

Background:

In the last years, similarity searching has gained wide popularity as a method for performing Ligand-Based Virtual Screening (LBVS). This screening technique functions by making a comparison of the target compound’s features with that of each compound in the database of compounds. It is well known that none of the individual similarity measures could provide the best performances each time pertaining to an active compound structure, representing all types of activity classes. In the literature, we find several techniques and strategies that have been proposed to improve the overall effectiveness of ligand-based virtual screening approaches.

Objective:

In this work, our main objective is to propose a features selection approach based on genetic algorithm (FSGASS) to improve similarity searching pertaining to ligand-based virtual screening.

Methods:

Our contribution allows us to identify the most important and relevant characteristics of chemical compounds and to minimize their number in their representations. This will allow the reduction of features space, the elimination of redundancy, the reduction of training execution time, and the increase of the performance of the screening process.

Results:

The obtained results demonstrate superiority in the performance compared with these obtained with Tanimoto coefficient, which is considered as the most widely coefficient to quantify the similarity in the domain of LBVS.

Conclusion:

Our results show that significant improvements can be obtained by using molecular similarity research methods at the basis of features selection.

Management of pharmaceutical ICH M7 (Q)SAR predictions - The impact of model updates

Pharmaceutical applicants conduct (Q)SAR assessments on identified and theoretical impurities to predict their mutagenic potential. Two complementary models—one rule-based and one statistical-based—are used, followed by expert review. (Q)SAR models are continuously updated to improve predictions, with new versions typically released on a yearly basis. Numerous releases of (Q)SAR models will occur during the typical 6–7 years of drug development until new drug registration. Therefore, it is important to understand the impact of model updates on impurity mutagenicity predictions over time. Compounds representative of pharmaceutical impurities were analyzed with three rule- and three statistical-based models covering a 4–8 year period, with the individual time frame being dependent on when the individual models were initially made available. The largest changes in the combined outcome of two complementary models were from positive or equivocal to negative and from negative to equivocal. Importantly, the cumulative change of negative to positive predictions was small in all models (<5%) and was further reduced when complementary models were combined in a consensus fashion. We conclude that model updates of the type evaluated in this manuscript would not necessarily require re-running a (Q)SAR prediction unless there is a specific need. However, original (Q)SAR predictions should be evaluated when finalizing the commercial route of synthesis for marketing authorization.

Robust Distance Measures for kNN Classification of Cancer Data

The k-Nearest Neighbor (kNN) classifier represents a simple and very general approach to classification. Still, the performance of kNN classifiers can often compete with more complex machine-learning algorithms. The core of kNN depends on a "guilt by association" principle where classification is performed by measuring the similarity between a query and a set of training patterns, often computed as distances. The relative performance of kNN classifiers is closely linked to the choice of distance or similarity measure, and it is therefore relevant to investigate the effect of using different distance measures when comparing biomedical data. In this study on classification of cancer data sets, we have used both common and novel distance measures, including the novel distance measures Sobolev and Fisher, and we have evaluated the performance of kNN with these distances on 4 cancer data sets of different type. We find that the performance when using the novel distance measures is comparable to the performance with more well-established measures, in particular for the Sobolev distance. We define a robust ranking of all the distance measures according to overall performance. Several distance measures show robust performance in kNN over several data sets, in particular the Hassanat, Sobolev, and Manhattan measures. Some of the other measures show good performance on selected data sets but seem to be more sensitive to the nature of the classification data. It is therefore important to benchmark distance measures on similar data prior to classification to identify the most suitable measure in each case.

De Novo Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization

Artificial intelligence and multiobjective optimization represent promising solutions to bridge chemical and biological landscapes by addressing the automated de novo design of compounds as a result of a humanlike creative process. In the present study, we conceived a novel pair-based multiobjective approach implemented in an adapted SMILES generative algorithm based on recurrent neural networks for the automated de novo design of new molecules whose overall features are optimized by finding the best trade-offs among relevant physicochemical properties (MW, logP, HBA, HBD) and additional similarity-based constraints biasing specific biological targets. In this respect, we carried out the de novo design of chemical libraries targeting neuraminidase, acetylcholinesterase, and the main protease of severe acute respiratory syndrome coronavirus 2. Several quality metrics were employed to assess drug-likeness, chemical feasibility, diversity content, and validity. Molecular docking was finally carried out to better evaluate the scoring and posing of the de novo generated molecules with respect to X-ray cognate ligands of the corresponding molecular counterparts. Our results indicate that artificial intelligence and multiobjective optimization allow us to capture the latent links joining chemical and biological aspects, thus providing easy-to-use options for customizable design strategies, which are especially effective for both lead generation and lead optimization. The algorithm is freely downloadable at https://github.com/alberdom88/moo-denovo and all of the data are available as Supporting Information.

Tautomer Standardization in Chemical Databases: Deriving Business Rules from Quantum Chemistry

Databases of small, potentially bioactive molecules are ubiquitous across the industry and academia. Designed such that each unique compound should appear only once, the multiplicity of ways in which many compounds can be represented means that these databases require methods for standardizing the representation of chemistry. This is commonly achieved through the use of "Chemistry Business Rules", sets of predefined rules that describe the "house style" of the database in question. At Syngenta, the historical approach to the design of chemistry business rules has been to focus on consistency of representation, with chemical relevance given secondary consideration. In this work, we overturn that convention. Through the use of quantum chemistry calculations, we define a set of chemistry business rules for tautomer standardization that reproduces gas-phase energetic preferences. We go on to show that, compared to our historic approach, this method yields tautomers that are in better agreement with those observed experimentally in condensed phases and that are better suited for use in predictive models.

A cheminformatic study on chemical space characterization and diversity analysis of 5-LOX inhibitors

The process of blocking 5-lipoxygenase (5-LOX) catalyzed leukotriene biosynthesis has been recognized for the past few decades as a promising therapeutic strategy for acute inflammatory, allergic, and respiratory diseases. Due to the toxicity effect of FDA approved 5-LOX inhibitor zileuton, novel 5-LOX inhibitors have been sought by the scientific community. As a result, a significant and relevant amount of information on the structure-activity of 5-LOX inhibitors has been released and stored in public databases. In this study, we aimed at the comprehensive cheminformatic characterization of the diversity and complexity of the chemical space of 5-LOX inhibitors and its activating protein FLAP inhibitors by comparing it with the Approved drug space and virtual LOX library. The visual representation of the property space indicates some compounds in the 5-LOX inhibitors space broaden the traditional medicinal space. The structural diversity of the databases is computed using complementary approaches, including Physicochemical Property (PCP) descriptors, molecular fingerprints, and molecular scaffold. With the apparent exception of approved drugs, the 5-LOX dataset shows more diversity compared to FLAP and LOX virtual library set. This study was able to identify the underlying patterns in the chemical and pharmacological properties space that were decisive for the drug discovery and development of 5-LOX inhibitors.

Structural insight into TNF-α inhibitors through combining pharmacophore-based virtual screening and molecular dynamic simulation

Tumor Necrosis Factor-alpha (TNF-α), a multifunctional cytokine responsible for providing resistance against infections, inflammation, and cancers. TNF-α has emerged as a promising drug target against several autoimmune and inflammatory disorders. Several synthetic antibodies (Infliximab, Etanercept, and Adalimumab) are available, but their potential to cause severe side effects has prompted them to develop alternative small molecules-based therapies for inhibition of TNF-α. In the present study, combined in silico approaches based on pharmacophore modeling, virtual screening, molecular docking, and molecular dynamics studies were employed to understand significant direct interactions between TNF-α protein and small molecule inhibitors. Initially, four different small molecule libraries (∼17.5 million molecules) were virtually screened against the selected pharmacophore model. The identified hits were further subjected to molecular docking studies. The three potent lead compounds (ZINC05848961, ZINC09402309, ZINC04502991) were further subjected to 100 ns molecular dynamic studies to examine their stability. Our docking and molecular dynamic analysis revealed that the selected lead compounds target the TNF receptor (TNFR) and efficiently block the production of TNF. Moreover, in silico ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) analysis revealed that all the predicted compounds have good pharmacokinetic properties with high gastrointestinal absorption and a decent bioavailability score. Furthermore, toxicity profiles further evidenced that these compounds have no risk of being mutagenic, tumorigenic, reproductive and irritant except ZINC11915498. In conclusion, the present study could serve as the starting point to develop new therapeutic regimens to treat various TNF- related diseases. Communicated by Ramaswamy H. Sarma.

Repurposing drugs against the main protease of SARS-CoV-2: mechanism-based insights supported by available laboratory and clinical data

The ongoing global pandemic of COVID-19 has brought life to almost a standstill with the implementation of lockdowns and social distancing as some of the preventive measures in the absence of any approved specific therapeutic interventions. To combat this crisis, research communities worldwide are falling back on the existing repertoire of approved/investigational drugs to probe into their anti-coronavirus properties. In this report, we describe our unique efforts in identifying potential drugs that could be repurposed against the main protease of SARS-CoV-2 (SARS-CoV-2 M^pro). To achieve this goal, we have primarily exploited the principles of 'neighbourhood behaviour' in the protein 3D (workflow-I) and chemical 2D structural space (workflow-II) coupled with docking simulations and insights into the possible modes of action of the selected candidates from the available literature. This integrative approach culminated in prioritizing 29 potential repurpose-able agents (20 approved drugs and 9 investigational molecules) against SARS-CoV-2 M^pro. Apart from the approved/investigational anti-viral drugs, other notable hits include anti-bacterial, anti-inflammatory, anti-cancer and anti-coagulant drugs. Our analysis suggests that some of these drugs have the potential to simultaneously modulate the functions of viral proteins and the host response system. Interestingly, many of these identified candidates (12 molecules from workflow-I and several molecules, belonging to the chemical classes of alkaloids, tetracyclines, peptidomimetics, from workflow-II) are suggested to possess anti-viral properties, which is supported by laboratory and clinical data. Furthermore, this work opens a new avenue of research to probe into the molecular mechanism of action of many drugs, which are known to demonstrate anti-viral activity but are so far not known to target viral proteases.

Multi-objective de novo molecular design of organic structure-directing agents for zeolites using nature-inspired ant colony optimization

Organic structure-directing agents (OSDAs) are often employed for synthesis of zeolites with desired frameworks. A priori prediction of such OSDAs has mainly relied on the interaction energies between OSDAs and zeolite frameworks, without cost considerations. For practical purposes, the cost of OSDAs becomes a critical issue. Therefore, the development of a computational de novo prediction methodology that can speed up the trial-and-error cycle in the search for less expensive OSDAs is desired. This study utilized a nature-inspired ant colony optimization method to predict physicochemically and/or economically preferable OSDAs, while also taking molecular similarity and heuristics of zeolite synthesis into consideration. The prediction results included experimentally known OSDAs, candidates having structures closely related to known OSDAs, and novel ones, suggesting the applicability of this approach.

Inspired by the exploratory methods of ant colonies, adaptive optimization was employed to explore the chemical space for organic molecules that guide zeolite crystallization, giving both physicochemically and economically promising molecules.

A systematic analysis of protein-carbohydrate interactions in the Protein Data Bank

Protein-carbohydrate interactions underlie essential biological processes. Elucidating the mechanism of protein-carbohydrate recognition is a prerequisite for modeling and optimizing protein-carbohydrate interactions, which will help in discovery of carbohydrate-derived therapeutics. In this work, we present a survey of a curated database consisting of 6,402 protein-carbohydrate complexes in the Protein Data Bank (PDB). We performed an all-against-all comparison of a subset of nonredundant binding sites, and the result indicates that the interaction pattern similarity is not completely relevant to the binding site structural similarity. Investigation of both binding site and ligand promiscuities reveals that the geometry of chemical feature points is more important than local backbone structure in determining protein-carbohydrate interactions. A further analysis on the frequency and geometry of atomic interactions shows that carbohydrate functional groups are not equally involved in binding interactions. Finally, we discuss the usefulness of protein-carbohydrate complexes in the PDB with acknowledgement that the carbohydrates in many structures are incomplete.

PhyteByte: identification of foods containing compounds with specific pharmacological properties

Background

Phytochemicals and other molecules in foods elicit positive health benefits, often by poorly established or unknown mechanisms. While there is a wealth of data on the biological and biophysical properties of drugs and therapeutic compounds, there is a notable lack of similar data for compounds commonly present in food. Computational methods for high-throughput identification of food compounds with specific biological effects, especially when accompanied by relevant food composition data, could enable more effective and more personalized dietary planning. We have created a machine learning-based tool (PhyteByte) to leverage existing pharmacological data to predict bioactivity across a comprehensive molecular database of foods and food compounds.

Results

PhyteByte uses a cheminformatic approach to structure-based activity prediction and applies it to uncover the putative bioactivity of food compounds. The tool takes an input protein target and develops a random forest classifier to predict the effect of an input molecule based on its molecular fingerprint, using structure and activity data available from the ChEMBL database. It then predicts the relevant bioactivity of a library of food compounds with known molecular structures from the FooDB database. The output is a list of food compounds with high confidence of eliciting relevant biological effects, along with their source foods and associated quantities in those foods, where available. Applying PhyteByte to the human PPARG gene, we identified irigenin, sesamin, fargesin, and delta-sanshool as putative agonists of PPARG, along with previously identified agonists of this important metabolic regulator.

Conclusions

PhyteByte identifies food-based compounds that are predicted to interact with specific protein targets. The identified relationships can be used to prioritize food compounds for experimental or epidemiological follow-up and can contribute to the rapid development of precision approaches to new nutraceuticals as well as personalized dietary planning.

Learning the Edit Costs of Graph Edit Distance Applied to Ligand-Based Virtual Screening

BACKGROUND

Graph edit distance is a methodology used to solve error-tolerant graph matching. This methodology estimates a distance between two graphs by determining the minimum number of modifications required to transform one graph into the other. These modifications, known as edit operations, have an edit cost associated that has to be determined depending on the problem.

OBJECTIVE

This study focuses on the use of optimization techniques in order to learn the edit costs used when comparing graphs by means of the graph edit distance.

METHODS

Graphs represent reduced structural representations of molecules using pharmacophore-type node descriptions to encode the relevant molecular properties. This reduction technique is known as extended reduced graphs. The screening and statistical tools available on the ligand-based virtual screening benchmarking platform and the RDKit were used.

RESULTS

In the experiments, the graph edit distance using learned costs performed better or equally good than using predefined costs. This is exemplified with six publicly available datasets: DUD-E, MUV, GLL&GDD, CAPST, NRLiSt BDB, and ULS-UDS.

CONCLUSION

This study shows that the graph edit distance along with learned edit costs is useful to identify bioactivity similarities in a structurally diverse group of molecules. Furthermore, the target-specific edit costs might provide useful structure-activity information for future drug-design efforts.

NMR-based metabolomics approach reveals effects of antioxidant nutrients in sepsis-induced changes in rat liver injury

Oxidative stress and the production of intracellular reactive oxygen species (ROS) have been implicated in the pathogenesis of sepsis. In excess, oxidative stress is not deemed an unbalanced biochemical reaction in the critically ill rats, but it is a key pathological factor in driving systemic inflammatory response that can result in multiple organ failure in sepsis. Thus, we aimed to explore whether antioxidant nutrients could reduce or delay the oxidative stress condition of sepsis rats, and then play a prospective role in the oxidative stress condition of critical disease. In this investigation, the ability of exogenous and endogenous antioxidant nutrients (ascorbate, taurine and glutathione) to prevent sepsis-induced changes in liver injury was examined using a rat model of sepsis induced by cecal ligation and puncture (CLP), and the underlying mechanisms were also investigated. The effects of three antioxidants on sepsis were assessed based on biochemical assays in combination with an NMR-based metabolomics approach and correlation network analysis. Our results suggested that ascorbate, taurine and glutathione had broadly similar protective effects on reducing oxidative stress. Compared with CLP rats, antioxidant-treated rats exhibited alleviated (P<.05) organ dysfunction and improved liver pathology. Moreover, taurine showed a better efficacy compared with ascorbate and glutathione, evidenced by significantly reversed metabolomics profiles toward normal state. Under conditions of sepsis, antioxidants suppressed inflammatory responses by restraining key signaling pathways, including the redox-sensitive transcription factor pathways of NF-κB and MAPK. Collectively, our findings suggested that antioxidant nutrients exerted beneficial effects on septic rats via protecting mitochondrial.

Assessing the information content of structural and protein-ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and active learning

For kinase inhibitors, X-ray crystallography has revealed different types of binding modes. Currently, more than 2000 kinase inhibitors with known binding modes are available, which makes it possible to derive and test machine learning models for the prediction of inhibitors with different binding modes. We have addressed this prediction task to evaluate and compare the information content of distinct molecular representations including protein–ligand interaction fingerprints (IFPs) and compound structure-based structural fingerprints (i.e., atom environment/fragment fingerprints). IFPs were designed to capture binding mode-specific interaction patterns at different resolution levels. Accurate predictions of kinase inhibitor binding modes were achieved with random forests using both representations. The performance of IFPs was consistently superior to atom environment fingerprints, albeit only by less than 10%. An active learning strategy applying information entropy-based selection of training instances was applied as a diagnostic approach to assess the relative information content of distinct representations. IFPs were found to capture more binding mode-relevant information than atom environment fingerprints, leading to highly predictive models even when training instances were randomly selected. By contrast, for atom environment fingerprints, the derivation of accurate models via active learning depended on entropy-based selection of informative training compounds. Notably, higher information content of IFPs confirmed by active learning only resulted in small improvements in global prediction accuracy compared to models derived using atom environment fingerprints. For practical applications, prediction of binding modes of new kinase inhibitors on the basis of chemical structure is highly attractive.

TrypInDB: A searchable online resource of small molecule inhibitors against Trypanosoma sp

African Trypanosomiasis and American Trypanosomiasis are the diseases affecting more than thousands of people yearly and more than twenty-five million people risk acquiring the disease. The treatment for the disease is generally expensive, and most of the available drugs are of high-toxicity and cause fatal side-effects. Hence, there is a constant need for finding new treatment strategies for Trypanosomiasis. Combination therapy and repurposing or redesigning of existing inhibitors for new drugs are of high importance to address these hurdles, particularly the drug resistance. Hence, here we report TrypInDB, a searchable online resource of small molecule inhibitors having a varying degree of activity towards Trypanosoma sp. Information of about >14,000 small molecules from >700 published research articles was collected and made as an easy-to-search database. Four major sets of information were made available for each collected inhibitors viz., General information (activity values; source of the inhibitors; enzyme targets; etc.,), Structural information, Toxicity information, and Literature information. More than 25 different information about each inhibitor were collected or predicted and made accessible for searching. The database is designed to be queried easily with multiple-field filters with the provisions to perform sub-structure search and similar FDA approved drug searches. The database supports the easy export of queried records and structure in multiple formats. In addition, the TrypInDB is actively integrated into LeishInDB. We believe that the scope of TrypInDB permits the research community to exploit the available data for repurposing the inhibitors as well as for the investigation of new therapeutics. Database URL: http://trypindb.biomedinformri.com/.