Virtual Chemical Library Research Articles

The IMS Library: from IN-Stock to Virtual.

A chemical library is a key element in the early stages of pharmaceutical research. Its design encompasses various factors, such as diversity, size, ease of synthesis, aimed at increasing the likelihood of success in drug discovery. This article explores the collaborative efforts of computational and synthetic chemists in tailoring chemical libraries for cost-effective and resource-efficient use, particularly in the context of academic research projects. It proposes chemoinformatics methodologies that address two pivotal questions: first, crafting a diverse panel of under 1000 compounds from an existing pool through synthetic efforts, leveraging the expertise of organic chemists; and second, expanding pharmacophoric diversity within this panel by creating a highly accessible virtual chemical library. Chemoinformatics tools were developed to analyse initial panel of about 10,000 compounds into two tailored libraries: eIMS and vIMS. The eIMS Library comprises 578 diverse in-stock compounds ready for screening. Its virtual counterpart, vIMS, features novel compounds guided by chemists, ensuring synthetic accessibility. vIMS offers a broader array of binding motifs and improved drug-like characteristics achieved through the addition of diverse functional groups to eIMS scaffolds followed by filtering of reactive or unusual structures. The uniqueness of vIMS is emphasized through a comparison with commercial suppliers' virtual chemical space.

In Silico Design and In Vitro Assessment of Bicyclic Trifluoromethylated Pyrroles as New Antibacterial and Antifungal Agents.

QSAR studies on the number of compounds tested as S. aureus inhibitors were performed using an interactive Online Chemical Database and Modeling Environment (OCHEM) web platform. The predictive ability of the developed consensus QSAR model was q2=0.79±0.02. The consensus prediction for the external evaluation set afforded high predictive power (q2=0.82±0.03). The models were applied to screen a virtual chemical library with anti-S. aureus activity. Six promising new bicyclic trifluoromethylated pyrroles were identified, synthesized and evaluated in vitro against S. aureus, E. coli, and A. baumannii for their antibacterial activity and against C. albicans, C. krusei and C. glabrata for their antifungal activity. The synthesized compounds were characterized by 1H, 19F, and 13C NMR and elemental analysis. The antimicrobial activity assessment indicated that trifluoromethylated pyrroles 9 and 11 demonstrated the greatest antibacterial and antifungal effects against all the tested pathogens, especially against multidrug-resistant strains. The acute toxicity of the compounds to Daphnia magna ranged from 1.21 to 33.39 mg/L (moderately and slightly toxic). Based on the docking results, it can be suggested that the antibacterial and antifungal effects of the compounds can be explained by the inhibition of bacterial wall component synthesis.

Prediction of viral protease inhibitors using proteochemometrics approach

Being widely accepted tools in computational drug search, the (Q)SAR methods have limitations related to data incompleteness. The proteochemometrics (PCM) approach expands the applicability area by using description for both protein and ligand structures. The PCM algorithms are urgently required for the development of new antiviral agents. We suggest the PCM method using the TLMNA descriptors, combining the MNA descriptors of ligands and protein sequence N-grams. Our method was validated on the viral chymotrypsin-like proteases and their ligands. We have developed an original protocol allowing us to collect a comprehensive set of 15 protein sequences and more than 9000 ligands from the ChEMBL database. The N-grams were derived from the 3D-based alignment, accurately superposing ligand-binding regions. In testing the ligand set in SAR mode with MNA descriptors, an accuracy above 0.95 was determined that shows the perspective of the antiviral drug search in virtual chemical libraries. The effective PCM models were built with the TLMNA descriptor. The strong validation procedure with pair exclusion simulated the prediction of interactions between the new ligands and new targets, resulting in accuracy estimation up to 0.89. The PCM approach shows slightly lower accuracy caused by more uncertainty compared with SAR, but it overcomes the problem of data incompleteness.

New QSTR models to evaluation of imidazolium- and pyridinium-contained ionic liquids toxicity

We present machine learning studies devoted to the creation of predictive models for toxicity evaluation of imidazolium- and pyridinium-containing ionic liquids. New created predictive models were developed using the OCHEM. The predictive ability of the models was tested by cross-validation, giving a coefficient of determination q2 = 0.77–0.82. The models were applied to screen a virtual chemical library to the toxicity of ILs in Danio rerio and Daphnia magna bioassays. Models were used to predict toxicity for 25 ILs, which were then synthesized and tested in vivo. The in vivo toxicity studies found that D. magna is a more sensitive aquatic test organism than D. rerio – 67 % of the studied ILs are classified as extremely toxic with an LC50 range from 0.005 to 0.01 mg/l. At the same time, only one IL 1-dodecylpyridinium bromide with an LC50 of 0.08 mg/l is classified as extremely toxic, and 76 % are classified as slightly and moderately toxic compounds using D. rerio as a test organism. The most toxic ILs 5 and 19 were docked into the human AChE active center and demonstrated calculated binding energy values −9.5 and −9.3 kcal/mol that is comparable with the complexation of the human AChE inhibitor Donepezil, which provides insight into the potential molecular mechanisms of ILs toxicity. The created QSTR models are a successful tool for the toxicity analysis of new promising ILs. QSTR models demonstrated not only high predictive indicators but also a high percentage of correctly predicted toxicity values in vivo studies.

In Silico, in Vitro and in Vivo Study of Substituted Imidazolidinone Sulfonamides as Antibacterial Agents.

New substituted imidazolidinone sulfonamides have been developed using a rational drug design strategy. Predictive QSAR models for the search of new antibacterials were created using the OCHEM platform. Regression models were applied to verify a virtual chemical library of new imidazolidinone derivatives designed to have antibacterial activity. A number of substituted imidazolidinone sulfonamides as effective antibacterial agents were identified by QSAR prediction, synthesized and characterized by spectral and elemental, and tested in vitro. Six studied compounds have shown the highest in vitro antibacterial activity against Gram-negative E. coli and Gram-positive S. aureus multidrug-resistant strains. The in vivo acute toxicity of these imidazolidinone sulfonamides based on the LC50 value ranged from 16.01 to 44.35 mg/L (slightly toxic compounds class). The results of molecular docking suggest that the antibacterial mechanism of the compounds can be associated with the inhibition of post-translational modification processes of bacterial peptides and proteins.

DIY Virtual Chemical Libraries - Novel Starting Points for Drug Discovery.

The advancement of in silico technologies such as library enumeration and synthetic feasibility prediction has made drug discovery pipelines rely more and more on virtual libraries, which provide a significantly larger pool of compounds than in-stock supplier catalogs. Virtual libraries from external sources, however, may be associated with long delivery time and high cost. In this study, we present a Do-It-Yourself (DIY) combinatorial chemistry library containing over 14 million almost completely novel products built from 1000 low-cost building blocks based on robust reactions frequently applied at medicinal chemistry laboratories. The applicability of the DIY library for various drug discovery approaches is demonstrated by extensive physicochemical property, structural diversity profiling, and the generation of focused libraries. We found that internally built DIY chemical libraries present a viable alternative of external virtual catalogs by providing access to a large number of low-cost and quickly accessible potential chemical starting points for drug discovery.

Advances in the Applications of Bioinformatics and Chemoinformatics.

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

Targeting the Human Influenza a Virus: The Methods, Limitations, and Pitfalls of Virtual Screening for Drug-like Candidates Including Scaffold Hopping and Compound Profiling.

In this study, we describe the input data and processing steps to find antiviral lead compounds by a virtual screen. Two-dimensional and three-dimensional filters were designed based on the X-ray crystallographic structures of viral neuraminidase co-crystallized with substrate sialic acid, substrate-like DANA, and four inhibitors (oseltamivir, zanamivir, laninamivir, and peramivir). As a result, ligand-receptor interactions were modeled, and those necessary for binding were utilized as screen filters. Prospective virtual screening (VS) was carried out in a virtual chemical library of over half a million small organic substances. Orderly filtered moieties were investigated based on 2D- and 3D-predicted binding fingerprints disregarding the "rule-of-five" for drug likeness, and followed by docking and ADMET profiling. Two-dimensional and three-dimensional screening were supervised after enriching the dataset with known reference drugs and decoys. All 2D, 3D, and 4D procedures were calibrated before execution, and were then validated. Presently, two top-ranked substances underwent successful patent filing. In addition, the study demonstrates how to work around reported VS pitfalls in detail.

In Silico Design and Experimental Validation of Novel Oxazole Derivatives Against Varicella zoster virus.

Varicella zoster virus (VZV) infection causes severe disease such as chickenpox, shingles, and postherpetic neuralgia, often leading to disability. Reactivation of latent VZV is associated with a decrease in specific cellular immunity in the elderly and in patients with immunodeficiency. However, due to the limited efficacy of existing therapy and the emergence of antiviral resistance, it has become necessary to develop new and effective antiviral drugs for the treatment of diseases caused by VZV, particularly in the setting of opportunistic infections. The goal of this work is to identify potent oxazole derivatives as anti-VZV agents by machine learning, followed by their synthesis and experimental validation. Predictive QSAR models were developed using the Online Chemical Modeling Environment (OCHEM). Data on compounds exhibiting antiviral activity were collected from the ChEMBL and uploaded in the OCHEM database. The predictive ability of the models was tested by cross-validation, giving coefficient of determination q2 = 0.87-0.9. The validation of the models using an external test set proves that the models can be used to predict the antiviral activity of newly designed and known compounds with reasonable accuracy within the applicability domain (q2 = 0.83-0.84). The models were applied to screen a virtual chemical library with expected activity of compounds against VZV. The 7 most promising oxazole derivatives were identified, synthesized, and tested. Two of them showed activity against the VZV Ellen strain upon primary in vitro antiviral screening. The synthesized compounds may represent an interesting starting point for further development of the oxazole derivatives against VZV. The developed models are available online at OCHEM http://ochem.eu/article/145978 and can be used to virtually screen for potential compounds with anti-VZV activity.

RedDB, a computational database of electroactive molecules for aqueous redox flow batteries

An increasing number of electroactive compounds have recently been explored for their use in high-performance redox flow batteries for grid-scale energy storage. Given the vast and highly diverse chemical space of the candidate compounds, it is alluring to access their physicochemical properties in a speedy way. High-throughput virtual screening approaches, which use powerful combinatorial techniques for systematic enumerations of large virtual chemical libraries and respective property evaluations, are indispensable tools for an agile exploration of the designated chemical space. Herein, RedDB: a computational database that contains 31,618 molecules from two prominent classes of organic electroactive compounds, quinones and aza-aromatics, has been presented. RedDB incorporates miscellaneous physicochemical property information of the compounds that can potentially be employed as battery performance descriptors. RedDB’s development steps, including: (i) chemical library generation, (ii) molecular property prediction based on quantum chemical calculations, (iii) aqueous solubility prediction using machine learning, and (iv) data processing and database creation, have been described.

Calculating and Optimizing Physicochemical Property Distributions of Large Combinatorial Fragment Spaces.

The distributions of physicochemical property values, like the octanol-water partition coefficient, are routinely calculated to describe and compare virtual chemical libraries. Traditionally, these distributions are derived by processing each member of a library individually and summarizing all values in a distribution. This process becomes impractical when operating on chemical spaces which surpass billions of compounds in size. In this work, we present a novel algorithmic method called SpaceProp for the property distribution calculation of large nonenumerable combinatorial fragment spaces. The novel method follows a combinatorial approach and is able to calculate physicochemical property distributions of prominent spaces like Enamine's REAL Space, WuXi's GalaXi Space, and OTAVA's CHEMriya Space for the first time. Furthermore, we present a first approach of optimizing property distributions directly in combinatorial fragment spaces.

Data-driven discovery of small electroactive molecules for energy storage in aqueous redox flow batteries

A high-throughput virtual screening (HTVS)-guided experimental study is applied for the large-scale exploration of quinone-like anolytes for aqueous redox flow batteries (ARFBs). This includes the design of a focused virtual chemical library inspired by stable molecules, quantum chemical prediction of redox properties, machine learning prediction of aqueous solubility, automated search for commercial availability on vendor databases, and electrochemical characterization of the promising compounds. Screening in a chemical space of 3257 redox pairs led to 205 predicted candidates with higher solubility and lower redox potential than that of anthraquinone-2,7-disulfonic acid (AQDS) anolyte used in ARFBs. Through electrochemical studies on commercially available compounds, the molecules that show good performance in a practical ARFB setup are identified. Among them, indigo trisulfonate [Indigo-3(SO3H)] showed higher solubility, capacity retention, and coulombic efficiency than AQDS and its predecessors. The data-driven material design approach is therefore highly effective for the future explorations of electrochemical energy storage compounds.

Cytisine derivatives as new anti-Escherichia coli agents: in silico and in vitro studies

QSAR analysis of a 5143 compounds set of previously synthesized compounds tested against multi-drug resistant (MDR) clinical isolate Escherichia coli strains was done by using Online Chemical Modeling Environment (OCHEM).The predictive ability of the regression models was tested through cross-validation, giving coefficient of determination q2=0.72-0.8. The validation of the models using an external test set proved that the models can be used to predict the activity of newly designed compounds with reasonable accuracy within the applicability domain (q2=0.74-0.8). The models were applied to screen a virtual chemical library of cytisine derivatives, which was designed to have antibacterial activity. The QSAR modeling results allowed to identify a number of cytisine derivatives as effective antibacterial agents against antibiotic-resistant E. coli strains. Seven compounds were selected for synthesis and biological testing. In vitro investigation of the selected cytisine derivatives have shown that all studied compounds are potential antibacterial agents against MDR E. coli strains

Predictive modeling and therapeutic repurposing of natural compounds against the receptor-binding domain of SARS-CoV-2

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a member of the Coronaviridae family, causing major destructions to human life directly and indirectly to the economic crisis around the world. Although there is significant reporting on the whole genome sequences and updated data for the different receptors are widely analyzed and screened to find a proper medication. Only a few bioassay experiments were completed against SARS-CoV-2 spike protein. We collected the compounds dataset from the PubChem Bioassay database having 1786 compounds and split it into the ratio of 80–20% for model training and testing purposes, respectively. Initially, we have created 11 models and validated them using a fivefold validation strategy. The hybrid consensus model shows a predictive accuracy of 95.5% for training and 94% for the test dataset. The model was applied to screen a virtual chemical library of Natural products of 2598 compounds. Our consensus model has successfully identified 75 compounds with an accuracy range of 70–100% as active compounds against SARS-CoV-2 RBD protein. The output of ML data (75 compounds) was taken for the molecular docking and dynamics simulation studies. In the complete analysis, the Epirubicin and Daunorubicin have shown the docking score of −9.937 and −9.812, respectively, and performed well in the molecular dynamics simulation studies. Also, Pirarubicin, an analogue of anthracycline, has widely been used due to its lower cardiotoxicity. It shows the docking score of −9.658, which also performed well during the complete analysis. Hence, after the following comprehensive pipeline-based study, these drugs can be further tested in vivo for further human utilization. Communicated by Ramaswamy H. Sarma

Design of new imidazole derivatives with anti-HCMV activity: QSAR modeling, synthesis and biological testing.

The problem of designing new antiviral drugs against Human Cytomegalovirus (HCMV) was addressed using the Online Chemical Modeling Environment (OCHEM). Data on compound antiviral activity to human organisms were collected from the literature and uploaded in the OCHEM database. The predictive ability of the regression models was tested through cross-validation, giving coefficient of determination q2 = 0.71-0.76. The validation of the models using an external test set proved that the models can be used to predict the activity of newly designed compounds with reasonable accuracy within the applicability domain (q2 = 0.70-0.74). The models were applied to screen a virtual chemical library of imidazole derivatives, which was designed to have antiviral activity. The six most promising compounds were identified, synthesized and their antiviral activities against HCMV were evaluated in vitro. However, only two of them showed some activity against the HCMV AD169 strain.

LibINVENT: Reaction-based Generative Scaffold Decoration for in Silico Library Design.

Because of the strong relationship between the desired molecular activity and its structural core, the screening of focused, core-sharing chemical libraries is a key step in lead optimization. Despite the plethora of current research focused on in silico methods for molecule generation, to our knowledge, no tool capable of designing such libraries has been proposed. In this work, we present a novel tool for de novo drug design called LibINVENT. It is capable of rapidly proposing chemical libraries of compounds sharing the same core while maximizing a range of desirable properties. To further help the process of designing focused libraries, the user can list specific chemical reactions that can be used for the library creation. LibINVENT is therefore a flexible tool for generating virtual chemical libraries for lead optimization in a broad range of scenarios. Additionally, the shared core ensures that the compounds in the library are similar, possess desirable properties, and can also be synthesized under the same or similar conditions. The LibINVENT code is freely available in our public repository at https://github.com/MolecularAI/Lib-INVENT. The code necessary for data preprocessing is further available at: https://github.com/MolecularAI/Lib-INVENT-dataset.

Structure-Activity Relationship Modeling and Experimental Validation of the Imidazolium and Pyridinium Based Ionic Liquids as Potential Antibacterials of MDR Acinetobacter baumannii and Staphylococcus aureus

Online Chemical Modeling Environment (OCHEM) was used for QSAR analysis of a set of ionic liquids (ILs) tested against multi-drug resistant (MDR) clinical isolate Acinetobacter baumannii and Staphylococcus aureus strains. The predictive accuracy of regression models has coefficient of determination q2 = 0.66 − 0.79 with cross-validation and independent test sets. The models were used to screen a virtual chemical library of ILs, which was designed with targeted activity against MDR Acinetobacter baumannii and Staphylococcus aureus strains. Seven most promising ILs were selected, synthesized, and tested. Three ILs showed high activity against both these MDR clinical isolates.

Neutral ceramidase regulates ferroptosis in myeloid-derived suppressor cells

Abstract Myeloid-derived suppressor cells (MDSCs) are key targets in cancer immunotherapy. Neutral ceramidase (nCDase), a key enzyme that hydrolyzes ceramide into sphingosine, is highly expressed in colon carcinoma and regulates apoptosis and autophagy in colon carcinoma cells. We report here that nCDase is also highly expressed in tumor-infiltrating immune cells in mouse and human colon carcinoma in vivo. To target nCDase, we performed molecular docking based on human nCDase structure aganist a virtual chemical library. In vitro enzymatic inhibition analysis of top hits identified nCDC06 as a potent nCDase inhibitor. Chemical and NMR analysis determined nCDC06 structure and molecular weight (325.1 Dalton). nCDC06 has an IC50 of 10.16 to 25.91 mM for human nCDase and 18.6–30.2 mM for mouse nCDase. nCDC06 induces MDSC cell death in a dose-dependent mechanism. Inhibition of ferroptosis decreased nCDC06-induced MDSC cell death. Analysis of ultracellular structure, ROS accumulation, and glutathione revealed that nCDC06 suppresses MDSCs at least in part through a ferroptosis-dependent mechanism. Gene expression profiling revealed that nCDC06 activates the p53 pathway in MDSCs. Furthermore, nCDC06 therapy significantly inhibited tumor growth in an experimental lung metastasis mouse model. We determined that nCDase regulates MDSC ferroptosis and nCDC06 is potent in activating the p53 pathway to induce MDSC ferroptosis to suppress tumor growth in vivo.

Abstract A32: The SUV39H1-H3K9me3 pathway represses cytotoxic T lymphocyte effector expression to confer colon carcinoma immune escape

Abstract Despite the presence of cytotoxic T lymphocytes (CTLs) in the tumor microenvironment, the majority of immunogenic human colon cancers do not respond to immune checkpoint inhibitor immunotherapy and microsatellite instable (MSI) tumors are not naturally eliminated. The molecular mechanism underlying the inactivity of tumor-infiltrating CTLs is unknown. We report here that CTLs are present in both MSI and microsatellite stable (MSS) colon tumors. The expression of the H3K9me3-specific histone methyltransferase SUV39H1 is significantly elevated in human colon carcinoma as compared to normal colon tissues. Using a mouse colon carcinoma model, we further determined that tumor-infiltrating CTLs in the colon tumor microenvironment express high levels of SUV39H1. To target SUV39H1 in the tumor microenvironment, a virtual chemical library was screened based on the SET domain structure of human SUV39H1 protein. Functional enzymatic activity assays identified a small molecule that inhibits SUV39H1 enzymatic activity. Based on the structure of this small molecule, we modified it and chemically synthesized a novel small molecule, termed F5446, which has an EC50 of 0.496 μM for SUV39H1 enzymatic activity. H3K9me3 is enriched in the promoters of GZMB, PRF1, FASLG and IFNG in quiescent T cells. F5446 inhibits H3K9me3, thereby upregulating expression of these effectors in tumor-infiltrating CTLs and suppressing colon carcinoma growth in a CD8+ CTL-dependent manner in vivo. Our data indicate that SUV39H1 represses CTL effector expression and, in doing so, confers colon cancer immune escape. Citation Format: Chunwan Lu, Dafeng Yang, John D. Klement, II Kyu Oh, Natasha M. Savage, Jennifer L. Waller, Aaron H. Colby, Mark W. Grinstaff, Nicholas H. Oberlies, Cedric Pearce, Zhiliang Xie, Samuel Kulp, Christopher Coss, Mitch A. Phelps, Thomas Albers, Iryna O. Lebedyeva, Kebin Liu. The SUV39H1-H3K9me3 pathway represses cytotoxic T lymphocyte effector expression to confer colon carcinoma immune escape [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2018 Nov 27-30; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(4 Suppl):Abstract nr A32.

In silico and in vitro studies of a number PILs as new antibacterials against MDR clinical isolate Acinetobacter baumannii.

QSAR analysis of a set of previously synthesized phosphonium ionic liquids (PILs) tested against Gram-negative multidrug-resistant clinical isolate Acinetobacter baumannii was done using the Online Chemical Modeling Environment (OCHEM). To overcome the problem of overfitting due to descriptor selection, fivefold cross-validation with variable selection in each step of the model development was applied. The predictive ability of the classification models was tested by cross-validation, giving balanced accuracies (BA) of 76%-82%. The validation of the models using an external test set proved that the models can be used to predict the activity of newly designed compounds with a reasonable accuracy within the applicability domain (BA=83%-89%). The models were applied to screen a virtual chemical library with expected activity of compounds against MDR Acinetobacter baumannii. The eighteen most promising compounds were identified, synthesized, and tested. Biological testing of compounds was performed using the disk diffusion method in Mueller-Hinton agar. All tested molecules demonstrated high anti-A.baumannii activity and different toxicity levels. The developed classification SAR models are freely available online at http://ochem.eu/article/113921 and could be used by scientists for design of new more effective antibiotics.