Exploring DrugBank in Virtual Reality Chemical Space

The regulation of GSH/GPX4-mediated lipid accumulation confirms that schisandra polysaccharides should be valued equally as lignans

ETHNOPHARMACOLOGICAL RELEVANCE

Acetaminophen (APAP) induced liver injury (AILI) is a common cause of clinical hepatic damage and even acute liver failure. Our previous research has shown that Schisandra chinensis lignan extract (SLE) can exert a hepatoprotective effect by regulating lipid metabolism. Although polysaccharides from Schisandra chinensis (S. chinensis), like lignans, are important components of S. chinensis, their pharmacological activity and target effects on AILI have not yet been explored.

AIM OF THE STUDY

This study aims to quantitatively reveal the role of SCP in the pharmacological activity of S. chinensis, and further explore the pharmacological components, potential action targets and mechanisms of S. chinensis in treating AILI.

MATERIALS AND METHODS

The therapeutic effect of SCP on AILI was systematically determined via comparing the efficacy of SCP and SLE on in vitro and in vivo models. Network pharmacology, molecular docking and multi-omics techniques were then used to screen and verify the action targets of S. chinensis against AILI.

RESULTS

SCP intervention could significantly improve AILI, and the therapeutic effect was comparable to that of SLE. Notably, the combination of SCP and SLE did not produce mutual antagonistic effects. Subsequently, we found that both SCP and SLE could significantly reverse the down-regulation of GPX4 caused by the APAP modeling, and then further improving lipid metabolism abnormalities.

CONCLUSIONS

Hepatoprotective effects of SCP and SLE is most correlated with their regulation of GSH/GPX4-mediated lipid accumulation. This is the first exploration of the hepatoprotective effect and potential mechanism of SCP in treating AILI, which is crucial for fully utilizing S. chinensis and developing promising AILI therapeutic agents.

Democratizing cheminformatics: interpretable chemical grouping using an automated KNIME workflow

With the increased availability of chemical data in public databases, innovative techniques and algorithms have emerged for the analysis, exploration, visualization, and extraction of information from these data. One such technique is chemical grouping, where chemicals with common characteristics are categorized into distinct groups based on physicochemical properties, use, biological activity, or a combination. However, existing tools for chemical grouping often require specialized programming skills or the use of commercial software packages. To address these challenges, we developed a user-friendly chemical grouping workflow implemented in KNIME, a free, open-source, low/no-code, data analytics platform. The workflow serves as an all-encompassing tool, expertly incorporating a range of processes such as molecular descriptor calculation, feature selection, dimensionality reduction, hyperparameter search, and supervised and unsupervised machine learning methods, enabling effective chemical grouping and visualization of results. Furthermore, we implemented tools for interpretation, identifying key molecular descriptors for the chemical groups, and using natural language summaries to clarify the rationale behind these groupings. The workflow was designed to run seamlessly in both the KNIME local desktop version and KNIME Server WebPortal as a web application. It incorporates interactive interfaces and guides to assist users in a step-by-step manner. We demonstrate the utility of this workflow through a case study using an eye irritation and corrosion dataset.Scientific contributionsThis work presents a novel, comprehensive chemical grouping workflow in KNIME, enhancing accessibility by integrating a user-friendly graphical interface that eliminates the need for extensive programming skills. This workflow uniquely combines several features such as automated molecular descriptor calculation, feature selection, dimensionality reduction, and machine learning algorithms (both supervised and unsupervised), with hyperparameter optimization to refine chemical grouping accuracy. Moreover, we have introduced an innovative interpretative step and natural language summaries to elucidate the underlying reasons for chemical groupings, significantly advancing the usability of the tool and interpretability of the results.

Exploring the role of candidalysin in the pathogenicity of Candida albicans by gene set enrichment analysis and evolutionary dynamics

AIMS

To explore the pathogenic mechanisms of Candida albicans (C. albicans), focusing on its impact on human health, particularly through invasive infections in the gastrointestinal and respiratory tracts.

METHODS

In this study, we evaluated the demographic and clinical profiles of 7 pneumonia patients. Meanwhile, we used Gene Set Enrichment Analysis (GSEA) and Evolutionary Dynamics method to analyze the role of candidalysin in C. albicans pathogenicity.

RESULTS

By analyzing genomic data and conducting biomedical text mining, we identified novel mutation sites in the candidalysin coding gene ECE1-III, shedding light into the genetic diversity within C. albicans strains and their potential implications for antifungal resistance. Our results revealed significant associations between C. albicans and respiratory as well as gastrointestinal diseases, emphasizing the fungus's role in the pathogenesis of these diseases. Additionally, we identified a new mutation site in the C. albicans strain YF2-5, isolated from patients with pneumonia. This mutation may be associated with its heightened pathogenicity.

CONCLUSION

Our research advances the understanding of C. albicans pathogenicity and opens new avenues for developing targeted antifungal therapies. By focusing on the molecular basis of fungal virulence, we aim to contribute to the development of more effective treatment strategies, addressing the challenge of multidrug resistance in invasive fungal infections.

chARpack: The Chemistry Augmented Reality Package

Off-loading visualization and interaction into virtual reality (VR) using head-mounted displays (HMDs) has gained considerable popularity in simulation sciences, particularly in chemical modeling. Because of its unique way of soft immersion, augmented reality (AR) HMD technology has even more potential to be integrated into the everyday workflow of computational chemists. In this work, we present our environment to explore the prospects of AR in chemistry and general molecular sciences: The chemistry in Augmented Reality package (chARpack). Besides providing an extensible framework, our software focuses on a seamless transition between a 3D stereoscopic view with true 3D interactions and the traditional desktop PC setup to provide users with the best setup for all tasks in their workflow. Using feedback from domain experts, we discuss our design requirements for this kind of hybrid working environment (AR + PC), regarding input, features, degree of immersion, and collaboration.

Cross-Modal Retrieval Between 13C NMR Spectra and Structures Based on Focused Libraries

Library matching by comparing carbon-13 nuclear magnetic resonance (13C NMR) spectra with spectral data in the library is a crucial method for compound identification. In our previous paper, we introduced a deep contrastive learning system called CReSS, which used a library that contained more structures. However, CReSS has two limitations: there were no unknown structures in the library, and a redundant library reduces the structure-elucidation accuracy. Herein, we replaced the oversize traditional libraries with focused libraries containing a small number of molecules. A previously generative model, CMGNet, was used to generate focused libraries for CReSS. The combined model achieved a Top-10 accuracy of 54.03% when tested on 6,471 13C NMR spectra. In comparison, CReSS with a random reference structure library achieved an accuracy of only 9.17%. Furthermore, to expand the advantages of the focused libraries, we proposed SAmpRNN, which is a recurrent neural network (RNN). With the large focused library amplified by SAmpRNN, the structure-identification accuracy of the model increased in 70.0% of the 30 random example cases. In general, cross-modal retrieval between 13C NMR spectra and structures based on focused libraries (CFLS) achieved high accuracy and provided more accurate candidate structures than traditional libraries for compound identification.

Probable targets and mechanism of ginsenoside Rg1 for non-alcoholic fatty liver disease: a study integrating network pharmacology, molecular docking, and molecular dynamics simulation

Ginsenoside Rg1 (GRg1), a key bioactive component of medicinal herbs, has shown beneficial effects on non-alcoholic fatty liver disease (NAFLD) and numerous other conditions. Nevertheless, the specific targets that are actively involved and the potential mechanisms underlying NAFLD treatment remain unclear. This study aimed to elucidate the therapeutic effects and mechanism of GRg1 in alleviating NAFLD using a combined approach of network pharmacology and molecular biology validation. The analysis yielded 294 targets for GRg1 and 1293 associated with NAFLD, resulting in 89 overlapping targets. Through protein-protein interactions (PPI) network topology analysis, 10 key targets were identified. Upon evaluating the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) analysis, GRg1 may exert therapeutic effects on NAFLD by negatively regulating the apoptotic process, insulin and endocrine resistance, the AGE-RAGE signaling pathway in diabetic complications, and the Estrogen, PI3K/Akt, and MAPK pathways. The three differential gene targets for Akt1, EGFR, and IGF1 were identified through the compound-target network in conjunction with the aforementioned methods. The molecular docking and molecular dynamics (MD) simulations showed that AKT1 and EGFR had a strong binding affinity with GRg1. Overall, our findings point to a novel therapeutic strategy involving NAFLD, with further in vivo and in vitro studies promising to deepen our comprehension and validate its potential advantages.Communicated by Ramaswamy H. Sarma.

YASARA Model-Interactive Molecular Modeling from Two Dimensions to Virtual Realities

The industry's transition from three-dimensional (3D) glasses to virtual reality (VR) headsets has left modelers stranded without hardware supply, since walking around and waving arms in a virtual world is a great experience but also very tiring when doing time-intensive modeling work. We present a novel software implementation that uses a VR headset while sitting at a desk in front of the normal screen, which is beamed into the virtual reality together with keyboard, mouse, and chair using the headset's cameras and an extra tracker attached to the seat-back. Compared to 3D glasses, this yields a comparably relaxing but much more immersive workplace and provides additional possibilities such as taking molecules into one's hands, standing up, and walking or teleporting through the models. This VR functionality has been combined with a molecular graphics engine based on Vulkan, a next-generation cross-platform application programming interface (API) for GPUs and the successor of the widely used Open Graphics Library (OpenGL). It is built into the YASARA Model program, which includes many features like small and large molecule builders, electron densities, partial surfaces, contact analysis, coordinate manipulation, and animations. Interactive tutorials are provided to guide modelers into VR and familiarize them with the molecular modeling features. YASARA Model is available for Linux, Windows, Android, and MacOS (the latter without VR) with an introductory video at www.YASARA.org/vr.

Coptidis rhizoma and evodiae fructus against lipid droplet deposition in nonalcoholic fatty liver disease-related liver cancer by AKT

Nonalcoholic fatty liver disease (NAFLD) is the most common liver disease in the world. NAFLD has become one of the major factors contributing to hepatocellular carcinoma (HCC) development. However, there are no clear targets and therapeutic drugs for NAFLD-related liver cancer. This study explored the active compounds, target and mechanism of coptidis rhizoma and evodiae fructus in the treatment of NAFLD-related liver cancer based on the network pharmacology and experimental verification. There were 455 intersection targets of NAFLD-related liver cancer, and 65 drug-disease common targets. AKT1 has the highest degree, indicating that it may be a key target of coptidis rhizoma and evodiae fructus in the treatment of NAFLD-related liver cancer. The expression level of AKT1 was high in high-risk group, and the overall survival rate was lower than that in low-risk group. After oleic acid induction, p-AKT expression and lipid droplet deposition were promoted in HepG2 cells. Quercetin and resveratrol reduced lipid droplet deposition in vivo. Moreover, quercetin inhibited p-AKT expression, resveratrol both reduced the expression of p-AKT and AKT. The overall findings suggested that quercetin inhibited AKT in the treatment of NAFLD-related liver cancer.

Developing virtual and augmented reality applications for science, technology, engineering and math education

The Rhode Island IDeA Network of Biomedical Research Excellence Molecular Informatics Core at the University of Rhode Island Information Technology Services Innovative Learning Technologies developed virtual and augmented reality applications to teach concepts in biomedical science, including pharmacology, medicinal chemistry, cell culture and nanotechnology. The apps were developed as full virtual reality/augmented reality and 3D gaming versions, which do not require virtual reality headsets. Development challenges included creating intuitive user interfaces, text-to-voice functionality, visualization of molecules and implementing complex science concepts. In-app quizzes are used to assess the user's understanding of topics, and user feedback was collected for several apps to improve the experience. The apps were positively reviewed by users and are being implemented into the curriculum at the University of Rhode Island.

Shared mechanisms and crosstalk of COVID-19 and osteoporosis via vitamin D

Recently accumulated evidence implicates a close association of vitamin D (VitD) insufficiency to the incidence and clinical manifestations of the COVID-19 caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2). Populations with insufficient VitD including patients with osteoporosis are more susceptible to SARS-COV-2 infection and patients with COVID-19 worsened or developed osteoporosis. It is currently unknown, however, whether osteoporosis and COVID-19 are linked by VitD insufficiency. In this study, 42 common targets for VitD on both COVID-19 and osteoporosis were identified among a total of 243 VitD targets. Further bioinformatic analysis revealed 8 core targets (EGFR, AR, ESR1, MAPK8, MDM2, EZH2, ERBB2 and MAPT) in the VitD-COVID-19-osteoporosis network. These targets are involved in the ErbB and MAPK signaling pathways critical for lung fibrosis, bone structural integrity, and cytokines through a crosstalk between COVID-19 and osteoporosis via the VitD-mediated conventional immune and osteoimmune mechanisms. Molecular docking confirmed that VitD binds tightly to the predicted targets. These findings support that VitD may target common signaling pathways in the integrated network of lung fibrosis and bone structural integrity as well as the immune systems. Therefore, VitD may serve as a preventive and therapeutic agent for both COVID-19 and osteoporosis.

On scientific understanding with artificial intelligence

An oracle that correctly predicts the outcome of every particle physics experiment, the products of every possible chemical reaction or the function of every protein would revolutionize science and technology. However, scientists would not be entirely satisfied because they would want to comprehend how the oracle made these predictions. This is scientific understanding, one of the main aims of science. With the increase in the available computational power and advances in artificial intelligence, a natural question arises: how can advanced computational systems, and specifically artificial intelligence, contribute to new scientific understanding or gain it autonomously? Trying to answer this question, we adopted a definition of 'scientific understanding' from the philosophy of science that enabled us to overview the scattered literature on the topic and, combined with dozens of anecdotes from scientists, map out three dimensions of computer-assisted scientific understanding. For each dimension, we review the existing state of the art and discuss future developments. We hope that this Perspective will inspire and focus research directions in this multidisciplinary emerging field.

Network pharmacology and molecular docking technology-based predictive study of the active ingredients and potential targets of rhubarb for the treatment of diabetic nephropathy

Diabetic nephropathy (DN) is one of the most serious complications of diabetes and the main cause of end-stage renal failure. Rhubarb is a widely used traditional Chinese herb, and it has exhibited efficacy in reducing proteinuria, lowering blood sugar levels and improving kidney function in patients with DN. However, the exact pharmacological mechanism by rhubarb improves DN remain unclear due to the complexity of its ingredients. Hence, we systematically explored the underlying mechanisms of rhubarb in the treatment of DN. We adopted a network pharmacology approach, focusing on the identification of active ingredients, drug target prediction, gene collection, Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes enrichment. Molecular docking technology was used to verify the binding ability between the main active compounds and central therapeutic targets, and screen out the core active ingredients in rhubarb for the treatment of DN. Finally, molecular dynamics simulation was performed for the optimal core protein-ligand obtained by molecular docking using GROMACS software. The network analysis identified 16 active compounds in rhubarb that were linked to 37 possible therapeutic targets related to DN. Through protein-protein interaction analysis, TP53, CASP8, CASP3, MYC, JUN and PTGS2 were identified as the key therapeutic targets. By validation of molecular docking, finding that the central therapeutic targets have good affinities with the main active compounds of rhubarb, and rhein, beta-sitosterol and aloe-emodin were identified as the core active ingredients in rhubarb for the treatment of DN. Results from molecular dynamics simulations showed that TP53 and aloe-emodin bound very stably with a binding free energy of - 26.98 kcal/mol between the two. The results of the gene enrichment analysis revealed that the PI3K-Akt signalling pathway, p53 signalling pathway, AGE-RAGE signalling pathway and MAPK signalling pathway might be the key pathways for the treatment of DN, and these pathways were involved in podocyte apoptosis, glomerular mesangial cell proliferation, inflammation and renal fibrosis. Based on the network pharmacology approach and molecular docking technology, we successfully predicted the active compounds and their respective targets. In addition, we illustrated the molecular mechanisms that mediate the therapeutic effects of rhubarb against DN. These findings provided an important scientific basis for further research of the mechanism of rhubarb in the treatment of DN.

The Pharmacological Mechanism of Xiyanping Injection for the Treatment of Novel Coronavirus Pneumonia (COVID-19): Based on Network Pharmacology Strategy

Purpose

The possible mechanism of Xiyanping injection treatment COVID-19 is discussed through the network pharmacology.

Methods

Obtaining the chemical structure of Xiyanping injection through the patent application and obtaining control compounds I, II, III, IV, V, Yanhuning injection (VI, VII), Chuanhuning injection (VIII, IX), 10 compounds were analyzed by D3Targets-2019-nCoV. The human anti-COVID-19 gene in COVID-19 DisGeNET was intersected with the CTD Andrographolide target gene and then combined with D3Targets-2019-nCoV, resulting in 93 genes, using the Venny 2.1 platform. The PPI network was constructed by the String platform and Cytoscape 3.8.2 platform. The GO, KEGG, and tissue of the target were analyzed using the Metascape platform and DAVID platform. The gene expression in the respiratory system was analyzed using the ePlant platform. The CB-Dock is used for the docking verification and degree values of the first 20 genes.

Results

Finally, 1599 GO and 291 KEGG results were obtained. GO is mostly associated with the cell stress response to chemicals, the cell response to oxidative stress, and the cell response to reactive oxygen species. In total, 218 KEGG pathway concentrations were related to infection and other diseases and 73 signaling pathways mostly related to inflammation and immune pathways, such as TNF signaling pathway and MAPK signaling pathway. The molecular docking results show that Xiyanping injection, compound III, has a good docking relationship with 20 target proteins such as HSP90AA1. Tissue has 22 genes that are pooled in the lungs.

Conclusion

Xiyanping injection may inhibit the release of various inflammatory factors by inhibiting intracellular pathways such as MAPK and TNF. It acts on protein targets such as HSP90AA1 and plays a potential therapeutic role in COVID-19. Thus, compound III may be treated as a potential new drug for the treatment of COVID-19 and the Xiyanping injection may treat patients with COVID-19 infection.

Mechanisms of Gynostemma pentaphyllum against non-alcoholic fibre liver disease based on network pharmacology and molecular docking

As a progressive chronic disease, the effective treatment for non‐alcoholic fibre liver disease (NAFLD) has not yet been thoroughly explored at the moment. The widespread use of Gynostemma pentaphyllum (Thunb) for its anti‐insulin resistance effect indicates that potential therapeutic value may be found in Thunb for NAFLD. Hence, this research aims to discover the latent mechanism of Thunb for NAFLD treatment. To achieve the goal of discovering the latent mechanism of Thunb for NAFLD treatment, molecular docking strategy integrated a network phamacology was adopted in the exploration. We acquire Thunb compounds with activeness from TCMSP database. We collect the putative targets of Thunb and NAFLD to generate the network. Key targets and mechanism are screened by PPI analysis, GO and KEGG pathway enrichment analyses. Molecular docking simulation is introduced into the study as assessment method. Through network analysis and virtual screening based on molecular docking, 2 targets (AKT 1 and GSK3B) are identified as key therapeutic targets with satisfying binding affinity. Main mechanism is believed to be the biological process and pathway related to insulin resistance according to the enrichment analyses outcomes. Particularly, the P13K–AKT signalling pathway is recognized as a key pathway of the mechanism. In conclusion, the study shows that Thunb could be a potential treatment against NAFLD and may suppress insulin resistance through the P13K–AKT signalling pathway. The result of the exploration provides a novel perspective for approaching experimental exploration.

A Brave New World: Virtual Reality and Augmented Reality in Systems Biology

How we interact with computer graphics has not changed significantly from viewing 2D text and images on a flatscreen since their invention. Yet, recent advances in computing technology, internetworked devices and gaming are driving the design and development of new ideas in other modes of human-computer interfaces (HCIs). Virtual Reality (VR) technology uses computers and HCIs to create the feeling of immersion in a three-dimensional (3D) environment that contains interactive objects with a sense of spatial presence, where objects have a spatial location relative to, and independent of the users. While this virtual environment does not necessarily match the real world, by creating the illusion of reality, it helps users leverage the full range of human sensory capabilities. Similarly, Augmented Reality (AR), superimposes virtual images to the real world. Because humans learn the physical world through a gradual sensory familiarization, these immersive visualizations enable gaining familiarity with biological systems not realizable in the physical world (e.g., allosteric regulatory networks within a protein or biomolecular pathways inside a cell). As VR/AR interfaces are anticipated to be explosive in consumer markets, systems biologists will be more immersed into their world. Here we introduce a brief history of VR/AR, their current roles in systems biology, and advantages and disadvantages in augmenting user abilities. We next argue that in systems biology, VR/AR technologies will be most useful in visually exploring and communicating data; performing virtual experiments; and education/teaching. Finally, we discuss our perspective on future directions for VR/AR in systems biology.

Identifying the Characteristics of Virtual Reality Gamification for Complex Educational Topics

Multidisciplinary topics in education pose a major challenge for traditional learning and teaching methods. Such topics can deter students from selecting particular courses or hinder their study progress. This study focused on the subject of medicinal chemistry, which is a discipline combining medicine and chemistry. This combination of applied and basic science creates a complex field of education that is challenging to both teach and learn. Chemical and pharmacological principles are typically presented in 2D molecular structures and, recently, 3D molecular models have been utilized to improve the visualization of chemical compounds and their chemical interactions. Contemporary studies have presented Virtual Reality (VR) as an alternative method for improving the learning and teaching of multidisciplinary specialties such as this. However, current educational efforts employing VR offer limited interactivity and a traditional teaching method previously presented in 2D. This reduces students’ interest and concentration in the taught subjects. This paper presents the development rationale of a novel VR educational application based on the evaluation of the user requirements by 405 pharmacy undergraduate students. The results informed the development and preliminary evaluation of a proposed VR serious game application, which was deployed in a real-life class environment and evaluated in contrast to traditional teaching methods by 15 students. The derived results confirmed the advantages of VR technology as a learning and teaching tool, in addition to the end-users’ willingness to adopt VR systems as a learning aid.

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.

Progress on open chemoinformatic tools for expanding and exploring the chemical space

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

“MedChemVR”: A Virtual Reality Game to Enhance Medicinal Chemistry Education

Medicinal chemistry (MC) is an indispensable component of the pharmacy curriculum. The pharmacists’ unique knowledge of a medicine’s chemistry enhances their understanding of the pharmacological activity, manufacturing, storage, use, supply, and handling of drugs. However, chemistry is a challenging subject for both teaching and learning. These challenges are typically caused by the inability of students to construct a mental image of the three-dimensional (3D) structure of a drug molecule from its two-dimensional presentations. This study explores a prototype virtual reality (VR) gamification option, as an educational tool developed to aid the learning process and to improve the delivery of the MC subject to students. The developed system is evaluated by a cohort of 41 students. The analysis of the results was encouraging and provided invaluable feedback for the future development of the proposed system.

ChemVA: Interactive Visual Analysis of Chemical Compound Similarity in Virtual Screening

In the modern drug discovery process, medicinal chemists deal with the complexity of analysis of large ensembles of candidate molecules. Computational tools, such as dimensionality reduction (DR) and classification, are commonly used to efficiently process the multidimensional space of features. These underlying calculations often hinder interpretability of results and prevent experts from assessing the impact of individual molecular features on the resulting representations. To provide a solution for scrutinizing such complex data, we introduce ChemVA, an interactive application for the visual exploration of large molecular ensembles and their features. Our tool consists of multiple coordinated views: Hexagonal view, Detail view, 3D view, Table view, and a newly proposed Difference view designed for the comparison of DR projections. These views display DR projections combined with biological activity, selected molecular features, and confidence scores for each of these projections. This conjunction of views allows the user to drill down through the dataset and to efficiently select candidate compounds. Our approach was evaluated on two case studies of finding structurally similar ligands with similar binding affinity to a target protein, as well as on an external qualitative evaluation. The results suggest that our system allows effective visual inspection and comparison of different high-dimensional molecular representations. Furthermore, ChemVA assists in the identification of candidate compounds while providing information on the certainty behind different molecular representations.

Forecasting System of Computational Time of DFT/TDDFT Calculations under the Multiverse Ansatz via Machine Learning and Cheminformatics

With the view of achieving a better performance in task assignment and load-balancing, a top-level designed forecasting system for predicting computational times of density-functional theory (DFT)/time-dependent DFT (TDDFT) calculations is presented. The computational time is assumed as the intrinsic property for the molecule. Based on this assumption, the forecasting system is established using the "reinforced concrete", which combines the cheminformatics, several machine-learning (ML) models, and the framework of many-world interpretation (MWI) in multiverse ansatz. Herein, the cheminformatics is used to recognize the topological structure of molecules, the ML models are used to build the relationships between topology and computational cost, and the MWI framework is used to hold various combinations of DFT functionals and basis sets in DFT/TDDFT calculations. Calculated results of molecules from the DrugBank dataset show that (1) it can give quantitative predictions of computational costs, typical mean relative errors can be less than 0.2 for DFT/TDDFT calculations with derivations of ±25% using the exactly pretrained ML models and (2) it can also be employed to various combinations of DFT functional and basis set cases without exactly pretrained ML models, while only slightly enlarge predicting errors.

Systems Pharmacology-Based Identification of Mechanisms of Action of Bolbostemma paniculatum for the Treatment of Hepatocellular Carcinoma

Background

Traditional Chinese medicine has widely used Bolbostemma paniculatum to treat diseases, including cancer, but its underlying mechanisms remain unclear. The present study aimed to elucidate the potential pharmacological mechanisms of “Tu Bei Mu” (TBM), the Chinese name for Bolbostemmatis Rhizoma, the dry tuber of B. paniculatum, for the treatment of hepatocellular carcinoma (HCC).

Material/Methods

The active components and putative therapeutic targets of TBM were explored using SwissTargetPrediction, Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and Search Tool for Interactions of Chemicals (STITCH). The HCC-related target database was built using DrugBank, DisGeNet, Online Mendelian Inheritance in Man (OMIM), and Therapeutic Target Database (TTD). A protein–protein interaction network of the common targets was constructed, based on the matches between TBM potential targets and HCC-related targets, using Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the cluster networks were used to elucidate the biological functions of TBM.

Results

Pharmacological network diagrams of the TBM compound-target network and HCC-related target network were successfully constructed. A total of 22 active components, 191 predicted biological targets of TBM, and 3775 HCC-related targets were identified. Through construction of an HCC-related target database and a protein–protein interaction network of the common targets, TBM was predicted to be effective in treating HCC mainly through the PI3K-Akt, HIF-1, p53, and PPAR signaling pathways.

Conclusions

The PI3K/Akt, HIF1, p53, and PPAR pathways may play vital roles in TBM treatment of HCC. Also, the potential anti-cancer effect of TBM on HCC appears to stem from the synergetic effect of multiple targets and mechanisms.

Combining Virtual Reality Visualization with Ensemble Molecular Dynamics to Study Complex Protein Conformational Changes

Molecular dynamics (MD) simulations are increasingly used to elucidate relationships between protein structure, dynamics, and their biological function. Currently, it is extremely challenging to perform MD simulations of large-scale structural rearrangements in proteins that occur on millisecond timescales or beyond, as this requires very significant computational resources, or the use of cumbersome "collective variable" enhanced sampling protocols. Here, we describe a framework that combines ensemble MD simulations and virtual reality visualization (eMD-VR) to enable users to interactively generate realistic descriptions of large amplitude, millisecond timescale protein conformational changes in proteins. Detailed tests demonstrate that eMD-VR substantially decreases the computational cost of folding simulations of a WW domain, without the need to define collective variables a priori. We further show that eMD-VR generated pathways can be combined with Markov state models to describe the thermodynamics and kinetics of large-scale loop motions in the enzyme cyclophilin A. Our results suggest eMD-VR is a powerful tool for exploring protein energy landscapes in bioengineering efforts.

Toward Mass Video Data Analysis: Interactive and Immersive 4D Scene Reconstruction

The technical progress in the last decades makes photo and video recording devices omnipresent. This change has a significant impact, among others, on police work. It is no longer unusual that a myriad of digital data accumulates after a criminal act, which must be reviewed by criminal investigators to collect evidence or solve the crime. This paper presents the VICTORIA Interactive 4D Scene Reconstruction and Analysis Framework ("ISRA-4D" 1.0), an approach for the visual consolidation of heterogeneous video and image data in a 3D reconstruction of the corresponding environment. First, by reconstructing the environment in which the materials were created, a shared spatial context of all available materials is established. Second, all footage is spatially and temporally registered within this 3D reconstruction. Third, a visualization of the hereby created 4D reconstruction (3D scene + time) is provided, which can be analyzed interactively. Additional information on video and image content is also extracted and displayed and can be analyzed with supporting visualizations. The presented approach facilitates the process of filtering, annotating, analyzing, and getting an overview of large amounts of multimedia material. The framework is evaluated using four case studies which demonstrate its broad applicability. Furthermore, the framework allows the user to immerse themselves in the analysis by entering the scenario in virtual reality. This feature is qualitatively evaluated by means of interviews of criminal investigators and outlines potential benefits such as improved spatial understanding and the initiation of new fields of application.

In Silico Screening for Novel Leucine Aminopeptidase Inhibitors with 3,4-Dihydroisoquinoline Scaffold

Cancers are the leading cause of deaths worldwide. In 2018, an estimated 18.1 million new cancer cases and 9.6 million cancer-related deaths occurred globally. Several previous studies have shown that the enzyme, leucine aminopeptidase is involved in pathological conditions such as cancer. On the basis of the knowledge that isoquinoline alkaloids have antiproliferative activity and inhibitory activity towards leucine aminopeptidase, the present study was conducted a study which involved database search, virtual screening, and design of new potential leucine aminopeptidase inhibitors with a scaffold based on 3,4-dihydroisoquinoline. These compounds were then filtered through Lipinski's "rule of five," and 25 081 of them were then subjected to molecular docking. Next, three-dimensional quantitative structure-activity relationship (3D-QSAR) study was performed for the selected group of compounds with the best binding score results. The developed model, calculated by leave-one-out method, showed acceptable predictive and descriptive capability as represented by standard statistical parameters r² (0.997) and q² (0.717). Further, 35 compounds were identified to have an excellent predictive reliability. Finally, nine selected compounds were evaluated for drug-likeness and different pharmacokinetics parameters such as absorption, distribution, metabolism, excretion, and toxicity. Our methodology suggested that compounds with 3,4-dihydroisoquinoline moiety were potentially active in inhibiting leucine aminopeptidase and could be used for further in-depth in vitro and in vivo studies.

Entorno virtual de la tabla periódica como herramienta de apoyo en la educación de la química

Este artículo plantea el desarrollo de un entorno virtual de la tabla periódica con el propósito de adquirir una herramienta que apoye el aprendizaje de la química, usando técnicas de realidad virtual. Para el desarrollo de esta propuesta, primero, se realizó la caracterización de los diferentes conceptos de la tabla periódica y lo concerniente a Realidad Virtual; luego, se diseñaron los componentes de la arquitectura, se modeló la base de datos y se realizaron los mockups; a partir de esto, se implementó el prototipo de la tabla periódica a través de técnicas de realidad virtual y modelado 3D y finalmente, se validaron los resultados del entorno virtual mediante un grupo de control de estudiantes de la materia química orgánica del Politécnico Jaime Isaza Cadavid, Profesores y personas externas y se hizo la retroalimentación del entorno, con base en la encuestas realizadas.

Uncovering the protective mechanism of Huoxue Anxin Recipe against coronary heart disease by network analysis and experimental validation

Coronary heart disease (CHD) is a leading cause of death and disability worldwide. Huoxue Anxin Recipe (HAR) is a novel Chinese Herbal Medicine formula of that has been used to treat CHD for several decades. Our previous study found that HAR had anti-oxidative effects, and could promote myocardial angiogenesis and improve cardiac function following myocardial infarction (MI) in rats. However, the active compounds, potential targets, and biological processes related to HAR have not been systematically investigated. Here, network pharmacology and experimental validation were used to study the protective mechanisms of HAR against CHD. We identified 124 active components, 124 verified targets, and 111 predictive targets. A total of 1192 genes related to CHD were identified by cDNA microarray and database analysis. A total of 47 putative targets of HAR against CHD were identified, including 32 verified targets and 15 predictive targets. ClueGo enrichment analysis identified 49 biological processes involved in the anti-CHD effects of HAR. Among them, the negative regulation of blood coagulation and regulation of collagen biosynthetic process were experimentally validated. After constructing a protein-protein interaction network and clustering with MECODE and ClusterONE, 162 key proteins (from ClueGo and clustering) were used to construct an internal interaction network. Complement C3 (C3), Fibrinogen alpha (FGA), Fibrinogen gamma (FGG), interleukin-6 (IL6), and Apolipoprotein A1 (APOA1) were the top 5 hub proteins identified by cytoHubber analysis. HAR limited the concentrations of C3, FGA, FGG, and IL6 and increased APOA1 levels. The results indicated that HAR could down-regulate blood coagulation, regulate collagen biosynthesis, inhibit peroxidation and inflammation injury, and promote cholesterol efflux. HAR could be a potential source of novel and effective drugs for CHD.

Reaching for the bright StARs in chemical space

Visualization of activity data in chemical space is common in drug discovery. Navigating the space in a systematic manner is not trivial, given its size and huge coverage. To this end, methods for data visualization have been developed charting biological activity into chemical space. Herein, we review the progress in different visualization approaches to explore the chemical space aiming at reaching insightful structure-activity relationships (SARs) in the chemical space. We discuss recent methods including consensus diversity plots, ChemMaps, and constellation plots. Several of the methods we review can be extended to analyze other properties of interest in medicinal chemistry, such as structure-toxicity relationships, and can be adapted to postprocess results of virtual screening (VS) of large compound libraries.

Design and Challenges of Radiopharmaceuticals

This review describes general concepts with regard to radiopharmaceuticals for diagnostic or therapeutic applications that help to understand the specific challenges encountered during the design, (radio)synthesis, in vitro and in vivo evaluation and clinical translation of novel radiopharmaceuticals. The design of a radiopharmaceutical requires upfront decisions with regard to combining a suitable vector molecule with an appropriate radionuclide, considering the type and location of the molecular target, the desired application, and the time constraints imposed by the relatively short half-life of radionuclides. Well-designed in vitro and in vivo experiments allow nonclinical validation of radiotracers. Ultimately, in combination with a limited toxicology package, the radiotracer becomes a radiopharmaceutical for clinical evaluation, produced in compliance with regulatory requirements for medicines for intravenous (IV) injection.

Finding Constellations in Chemical Space Through Core Analysis

Herein we introduce the constellation plots as a general approach that merges different and complementary molecular representations to enhance the information contained in a visual representation and analysis of chemical space. The method is based on a combination of a sub-structure based representation and classification of compounds with a "classical" coordinate-based representation of chemical space. A distinctive outcome of the method is that organizing the compounds in analog series leads to the formation of groups of molecules, aka "constellations" in chemical space. The novel approach is general and can be used to rapidly identify, for instance, insightful and "bright" Structure-Activity Relationships (StARs) in chemical space that are easy to interpret. This kind of analysis is expected to be especially useful for lead identification in large datasets of unannotated molecules, such as those obtained through high-throughput screening. We demonstrate the application of the method using two datasets of focused inhibitors designed against DNMTs and AKT1.

Using virtual reality for drug discovery: a promising new outlet for novel leads

Introduction: Virtual reality (VR) environments are increasingly being used by researchers in various fields in addition to being increasingly integrated into various areas of human life, ranging from videogames to different industrial uses. VR can be used to create interactive and multimodal sensory stimuli and thus offers unique advantages over other computer-based approaches for scientific research and molecular-level applications. Consequently, VR is starting to be used in novel drug development, such as in drug discovery, and rational drug design. Areas covered: In this review, the authors discuss the basic development of VR technology, including the available hardware and software. The latest advances of VR technology in novel drug development are then detailed, and the VR programs that can be applied in relevant studies are highlighted. Expert opinion: VR will lead to a revolution in pharmaceutical development. However, there are still obstacles to the successful and extensive application of VR to drug development, including the demand for further improvements to the available hardware and software and the various limitations described with regard to accuracy and precision. As technology continues to improve, the barriers to the widespread adoption of VR will diminish and VR technologies will play an increasingly important role in novel drug development.