scholarly journals MolAR: Bringing Chemical Structures to Life with Augmented Reality and Machine Learning

2021 ◽  
Author(s):  
Sukolsak Sakshuwong ◽  
Hayley Weir ◽  
Umberto Raucci ◽  
Todd J. Martínez
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
3D Structure ◽  
Three Dimensional ◽  
Real Life ◽  
Data Bank ◽  
Molecular Structures ◽  
Chemical Structures ◽  
Food And Drink ◽  
Research And Education

Visualizing three-dimensional molecular structures is crucial to understanding and predicting their chemical behavior. Existing visualization software, however, can be cumbersome to use, and, for many, hand-drawn skeletal structures remain the preferred method of chemical communication. Although convenient, the static, two-dimensional nature of these drawings can be misleading in conveying the molecule’s 3D structure, not to mention that dynamic movement is completely disregarded. Here, we combine machine learning and augmented reality (AR) to develop MolAR, an immersive mobile application for visualizing molecules in real-world scenes. The application uses deep learning to recognize hand-drawn hydrocarbons structures which it converts into interactive 3D molecules in AR. Users can also “hunt” for chemicals in food and drink to uncover molecules in their real-life environment. A variety of interesting molecules are pre-loaded into the application, and users can visualize molecules in PubChem by providing their name or SMILES string and proteins in the Protein Data Bank by providing their PDB ID. MolAR was designed to be used in both research and education settings, providing an almost barrierless platform to visualize and interact with 3D molecular structures in a uniquely immersive way.

scholarly journals Bringing Chemical Structures to Life with Augmented Reality, Machine Learning and Quantum Chemistry

2022 ◽  
Author(s):  
Sukolsak Sakshuwong ◽  
Hayley Weir ◽  
Umberto Raucci ◽  
Todd Martinez
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Augmented Reality ◽  
Quantum Chemistry ◽  
Mobile Application ◽  
Scientific Community ◽  
Chemical Properties ◽  
Molecular Structures ◽  
Chemical Behavior ◽  
Chemical Structures

Abstract Visualizing 3D molecular structures is crucial to understanding and predicting their chemical behavior. However, static 2D hand-drawn skeletal structures remain the preferred method of chemical communication. Here, we combine cutting-edge technologies in augmented reality (AR), machine learning, and computational chemistry to develop MolAR, a mobile application for visualizing molecules in AR directly from their hand-drawn chemical structures. Users can also visualize any molecule or protein directly from its name or PDB ID, and compute chemical properties in real time via quantum chemistry cloud computing. MolAR provides an easily accessible platform for the scientific community to visualize and interact with 3D molecular structures in an immersive and engaging way.

Visualizing 3D Molecular Structures Using an Augmented Reality App

2019 ◽  
Author(s):  
Kristina Eriksen ◽  
Bjarne Nielsen ◽  
Michael Pittelkow
Keyword(s):  
Augmented Reality ◽  
Small Molecules ◽  
Computer Modelling ◽  
Simple Procedure ◽  
3D Structure ◽  
Molecular Structures ◽  
Free Software ◽  
Programming Skills ◽  
2D Space ◽  
Made In

<p>We present a simple procedure to make an augmented reality app to visualize any 3D chemical model. The molecular structure may be based on data from crystallographic data or from computer modelling. This guide is made in such a way, that no programming skills are needed and the procedure uses free software and is a way to visualize 3D structures that are normally difficult to comprehend in the 2D space of paper. The process can be applied to make 3D representation of any 2D object, and we envisage the app to be useful when visualizing simple stereochemical problems, when presenting a complex 3D structure on a poster presentation or even in audio-visual presentations. The method works for all molecules including small molecules, supramolecular structures, MOFs and biomacromolecules.</p>

scholarly journals Real-Life Continuous Flash Suppression - Rendering The Real World Unconscious Using Augmented Reality Goggles

2018 ◽  
Author(s):  
Uri Korisky ◽  
Rony Hirschhorn ◽  
Liad Mudrik
Keyword(s):  
Augmented Reality ◽  
Real World ◽  
Three Dimensional ◽  
Real Life ◽  
Continuous Flash Suppression ◽  
Future Studies ◽  
The Real ◽  
Dominant Eye ◽  
Novel Variant ◽  
Real Objects

Notice: a peer-reviewed version of this preprint has been published in Behavior Research Methods and is available freely at http://link.springer.com/article/10.3758/s13428-018-1162-0Continuous Flash Suppression (CFS) is a popular method for suppressing visual stimuli from awareness for relatively long periods. Thus far, it has only been used for suppressing two-dimensional images presented on-screen. We present a novel variant of CFS, termed ‘real-life CFS’, with which the actual immediate surroundings of an observer – including three-dimensional, real life objects – can be rendered unconscious. Real-life CFS uses augmented reality goggles to present subjects with CFS masks to their dominant eye, leaving their non-dominant eye exposed to the real world. In three experiments we demonstrate that real objects can indeed be suppressed from awareness using real-life CFS, and that duration suppression is comparable that obtained using the classic, on-screen CFS. We further provide an example for an experimental code, which can be modified for future studies using ‘real-life CFS’. This opens the gate for new questions in the study of consciousness and its functions.

scholarly journals Quaternary structure evaluation tool for protein assemblies

2017 ◽  
Author(s):  
Selcuk Korkmaz ◽  
Jose M. Duarte ◽  
Andreas Prlić ◽  
Dincer Goksuluk ◽  
Gokmen Zararsiz ◽  
...  
Keyword(s):  
Quaternary Structure ◽  
Protein Structures ◽  
3D Structure ◽  
Three Dimensional ◽  
Data Bank ◽  
Prediction Methods ◽  
Evaluation Tool ◽  
Oligomeric State ◽  
Sequence Comparisons ◽  
Structure Evaluation

AbstractThe Protein Data Bank (PDB) is the single worldwide archive of experimentally-determined three-dimensional (3D) structures of proteins and nucleic acids. As of January 2017, the PDB housed more than 125,000 structures and was growing by more than 11,000 structures annually. Since the 3D structure of a protein is vital to understand the mechanisms of biological processes, diseases, and drug design, correct oligomeric assembly information is of critical importance. For example, it makes a difference if the protein is normally a dimer and not a monomer or a trimer or a tetramer or a hexamer in nature. Unfortunately, the biologically relevant oligomeric form of a 3D structure is not directly obtainable by X-ray crystallography. Instead, this information may be provided by the PDB Depositor as metadata coming from additional experiments, be inferred by sequence-sequence comparisons with similar proteins of known oligomeric state, or predicted using software, such as PISA (Proteins, Interfaces, Structures and Assemblies) or EPPIC (Evolutionary Protein Protein Interface Classifier). Despite significant efforts by professional PDB Biocurators during data deposition, there remain a number of structures in the archive with incorrect quaternary structure descriptions (or annotations). Further investigation is, therefore, needed to evaluate the correctness of quaternary structure annotations. In this study, we aim to identify the most probable oligomeric states for proteins represented in the PDB. Our approach evaluated the performance of four independent prediction methods, including text mining of primary publications, inference from homologous protein structures, and two computational methods (PISA and EPPIC). Aggregating predictions to give consensus results outperformed all four of the independent prediction methods, yielding 86% correct, 9% incorrect, and 5% inconclusive predictions, when tested with a well-curated benchmark dataset. We have developed a freely-available web-based tool to make this approach accessible to researchers and PDB Biocurators (http://quatstruct.rcsb.org).

Learning Molecular Structures in a Tangible Augmented Reality Environment

2012 ◽  
pp. 1-18
Author(s):  
Kikuo Asai ◽  
Norio Takase
Keyword(s):  
User Interface ◽  
Augmented Reality ◽  
Performance Test ◽  
Three Dimensional ◽  
Preference Test ◽  
Molecular Structures ◽  
Web Browser ◽  
Usability Test ◽  
Sense Of Presence ◽  
Viewing Experience

This article presents the characteristics of using a tangible tabletop environment produced by augmented reality (AR), aimed at improving the environment in which learners observe three-dimensional molecular structures. The authors perform two evaluation experiments. A performance test for a user interface demonstrates that learners with a tangible AR environment were able to complete the task of identifying molecular structures more quickly and accurately than those with a typical desktop-PC environment using a Web browser. A usability test by participants who learned molecular structures and answered relevant questions demonstrates that the environments had no effect on their learning of molecular structures. However, a preference test reveals that learners preferred a more tangible AR environment to a Web-browser environment in terms of overall enjoyment, reality of manipulation, and sense of presence, and vice versa in terms of ease of viewing, experience, and durability.

Search for therapeutics against COVID 19 targeting SARS-CoV-2 papain-like protease: an in silico study

2020 ◽  
Author(s):  
Monjur Ahmed Laskar ◽  
Manabendra Dutta Choudhury
Keyword(s):  
Three Dimensional ◽  
Data Bank ◽  
Dimensional Structure ◽  
Present Observation ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Chemical Structures ◽  
Ic50 Values ◽  
Potent Drug ◽  
Novel Coronavirus

Abstract Background: The global pandemic of novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an enveloped, positive-sense, single-stranded RNA betacoronavirus of the family Coronaviridae. Papain-like protease (PLpro) of SARS CoV-2 is an important target of COVID-19 because it is a multifunctional cysteine protease essential for coronaviral replication. Large numbers of phytochemicals with varied chemical structures isolated from medicinal plants have been shown to possess antiviral activity. Some of these phytochemicals have been chosen on the basis of literature survey for this study. Reported inhibitors of the papain-like protease are taken as control and for QSAR study.Methods: Three dimensional structure of target was downloaded from Protein Data Bank and docked with phytochemicals & inhibitors by using software FlexX. Inhibitors of the papain-like protease were taken from binding database and QSAR analysis was performed by using EasyQSAR software.Results: Six phytochemicals: Baicalin, Rutin, Biopterin, Licoleafol, Luteolin and Quercetin shows stable bonding pattern with the target in compare to known inhibitors as it shows least score in docking, forms maximum number of hydrogen bonds with the active residues of the receptor. The predicted IC50 values of the phytochemicals are also better than the known inhibitors.Conclusion: Based on present observation of docking score of both phytochemicals and known inhibitors, IC50 value of known inhibitors and predicted IC50 of phytochemicals, we suggests above mentioned six phytochemicals may be the Papain-like protease (PLpro) targeted potent drug leads against Covid-19.

scholarly journals A Toxicity Prediction Tool for Potential Agonist/Antagonist Activities in Molecular Initiating Events Based on Chemical Structures

2020 ◽  
Vol 21 (21) ◽  
pp. 7853
Author(s):  
Kota Kurosaki ◽  
Raymond Wu ◽  
Yoshihiro Uesawa
Keyword(s):  
Web Application ◽  
Endocrine Disrupting Chemicals ◽  
Three Dimensional ◽  
Quantitative Structure Activity Relationship ◽  
Molecular Structures ◽  
Adverse Outcome Pathway ◽  
Toxicity Prediction ◽  
Practical Applications ◽  
Chemical Structures ◽  
Qsar Models

Because the health effects of many compounds are unknown, regulatory toxicology must often rely on the development of quantitative structure–activity relationship (QSAR) models to efficiently discover molecular initiating events (MIEs) in the adverse-outcome pathway (AOP) framework. However, the QSAR models used in numerous toxicity prediction studies are publicly unavailable, and thus, they are challenging to use in practical applications. Approaches that simultaneously identify the various toxic responses induced by a compound are also scarce. The present study develops Toxicity Predictor, a web application tool that comprehensively identifies potential MIEs. Using various chemicals in the Toxicology in the 21st Century (Tox21) 10K library, we identified potential endocrine-disrupting chemicals (EDCs) using a machine-learning approach. Based on the optimized three-dimensional (3D) molecular structures and XGBoost algorithm, we established molecular descriptors for QSAR models. Their predictive performances and applicability domain were evaluated and applied to Toxicity Predictor. The prediction performance of the constructed models matched that of the top model in the Tox21 Data Challenge 2014. These advanced prediction results for MIEs are freely available on the Internet.

scholarly journals A Local Spherical Coordinate System Approach to Protein 3D Structure Description

2020 ◽  
Author(s):  
Wei Li
Keyword(s):  
Coordinate System ◽  
Protein Structures ◽  
3D Structure ◽  
Three Dimensional ◽  
Data Bank ◽  
Research Field ◽  
Spherical Coordinate System ◽  
Spherical Coordinate ◽  
Structure Description ◽  
Two Parameters

To date, Cartesian (x, y, z) coordinate system (CCS) has been the default approach to geometrically specify atomic spatial positions in protein structures since the launch of Protein Data Bank (PDB) in 1971. To this end, this paper proposes a local spherical coordinate system (SCS) approach as an alternative to the default approach and a previously reported global SCS approach. The local SCS approach here requires only two parameters (&theta; and &phi;), instead of x, y and z as required by the default CCS approach. Essentially, CCS and SCS are like the two sides of one coin, i.e., geometric coordinate system for three-dimensional position specification. Therefore, this paper furthermore argues that it is time to flip the coin over, and have a look at the other side of the coin, e.g., the local SCS approach, which possesses an intrinsically lower degree of descriptional complexity than that of the default CCS approach, and constitutes a potentially useful alternative perspective for all protein structural research field.

scholarly journals Docking studies in targeting proteins involved in cardiovascular disorders using phytocompounds from Terminalia arjuna

2020 ◽  
Author(s):  
Vikas Kumar ◽  
Nitin Sharma ◽  
Anuradha Sourirajan ◽  
Prem Kumar Khosla ◽  
Kamal Dev
Keyword(s):  
Molecular Docking ◽  
3D Structure ◽  
Three Dimensional ◽  
Data Bank ◽  
Docking Studies ◽  
Terminalia Arjuna ◽  
Structure Based Drug Design ◽  
Lipinski’S Rule ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

AbstractTerminalia arjuna (Roxb.) Wight and Arnot (T. arjuna) commonly known as Arjuna has been known for its cardiotonic nature in heart failure, ischemic, cardiomyopathy, atherosclerosis, myocardium necrosis and also has been used in the treatment of different human disorders such as blood diseases, anaemia and viral diseases. Our focus has been on phytochemicals which do not exhibit any cytotoxicity and have significant cardioprotective activity. Since Protein-Ligand interactions play a key role in structure-based drug design, therefore with the help of molecular docking, we screened 19 phytochemicals present in T. arjuna and investigated their binding affinity against different cardiovascular target proteins. The three-dimensional (3D) structure of target cardiovascular proteins were retrieved from Protein Data Bank, and docked with 3D Pubchem structures of 19 phytochemicals using Autodock vina. Molecular docking and drug-likeness studies were made using ADMET properties while Lipinski’s rule of five was performed for the phytochemicals to evaluate their cardio protective activity. Among all selected phytocompounds, arjunic acid, arjungenin, and terminic acid were found to fulfill all ADMET rules, drug likeness, and are less toxic in nature. Our studies, therefore revealed that these three phytochemicals from T. arjuna can be used as promising candidates for developing broad spectrum drugs against cardiovascular diseases.

scholarly journals Evaluation of the stereochemical quality of predicted RNA 3D models in the RNA-Puzzles submissions

RNA ◽  
2021 ◽  
pp. rna.078685.121
Author(s):  
Francisco Carrascoza ◽  
Maciej Antczak ◽  
Zhichao Miao ◽  
Eric Westhof ◽  
Marta Szachniuk
Keyword(s):  
3D Structure ◽  
Three Dimensional ◽  
Data Bank ◽  
3D Models ◽  
Its Sequence ◽  
In Silico Prediction ◽  
General Shape ◽  
Stereochemical Quality ◽  
Three Dimensional Models

In silico prediction is a well-established approach to derive a general shape of an RNA molecule based on its sequence or secondary structure. This paper reports an analysis of the stereochemical quality of the RNA three-dimensional models predicted using dedicated computer programs. The stereochemistry of 1,052 RNA 3D structures, including 1,030 models predicted by fully automated and human-guided approaches within 22 RNA-Puzzles challenges and reference structures, is analysed. The evaluation is based on standards of RNA stereochemistry that the Protein Data Bank requires from deposited experimental structures. Deviations from standard bond lengths and angles, planarity or chirality are quantified. A reduction in the number of such deviations should help in the improvement of RNA 3D structure modelling approaches.

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