Smile design

Molecular design depends heavily on optical properties for applications such as solar cells and polymer-based batteries. Accurate prediction of these properties is essential, and multiple predictive methods exist, from ab initio to data-driven techniques. Although theoretical methods, such as time-dependent density functional theory (TD-DFT) calculations, have well-established physical relevance and are among the most popular methods in computational physics and chemistry, they exhibit errors that are inherent in their approximate nature...

BACKGROUND: Personal Protective Equipment (PPE) Portraits is a hybridized art and medical intervention that lessens the alienating appearance of PPE through wearable, smiling headshot pictures. During the pandemic, the use of these portraits was expanded, but Canadian initiatives offered portraits only to immediate stakeholders. PPE Portraits Canada (PPC) aimed to provide PPE portraits to any Canadian healthcare institution and surveyed healthcare workers (HCW) regarding these portraits' impact...

The purpose of this systematic review was to map all the existing literature on digitally designed and fabricated esthetic veneers. We aimed to compare the accuracy of digitally designed preparation and cementation guides for esthetic indirect veneers with the conventional workflow. We evaluated studies comparing the accuracy and predictability of workflows between digitally fabricated indirect-esthetic veneers and conventional indirect veneers. Searches were performed in August 2023 across three databases, specifically Google Scholar, Cochrane, and PubMed, and were restricted to English-language publications...

A facially driven digital guided crown lengthening method using the virtual smile design approach supplemented with a static 3-dimensional face scan that demonstrates the digital data of extraoral soft tissue is presented. The technique enables the practitioner to virtually design the new smile and surgically plan the crown lengthening procedure.

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd...

BACKGROUND: High-performance computing plays a pivotal role in computer-aided drug design, a field that holds significant promise in pharmaceutical research. The prediction of drug-target affinity (DTA) is a crucial stage in this process, potentially accelerating drug development through rapid and extensive preliminary compound screening, while also minimizing resource utilization and costs. Recently, the incorporation of deep learning into DTA prediction and the enhancement of its accuracy have emerged as key areas of interest in the research community...

Fast and accurate prediction of solvent effects on reaction rates are crucial for kinetic modeling, chemical process design, and high-throughput solvent screening. Despite the recent advance in machine learning, a scarcity of reliable data has hindered the development of predictive models that are generalizable for diverse reactions and solvents. In this work, we generate a large set of data with the COSMO-RS method for over 28 000 neutral reactions and 295 solvents and train a machine learning model to predict the solvation free energy and solvation enthalpy of activation (ΔΔ G ‡ solv , ΔΔ H ‡ solv ) for a solution phase reaction...

Predicting drug-excipient compatibility is a critical aspect of pharmaceutical formulation design. In this study, we introduced an innovative approach that leverages machine learning techniques to improve the accuracy of drug-excipient compatibility predictions. Mol2vec and 2D molecular descriptors combined with the stacking technique were used to improve the performance of the model. This approach achieved a significant advancement in the predictive capacity as demonstrated by the accuracy, precision, recall, AUC, and MCC of 0...

Central ring systems are the most important part of bioactive molecules. They determine molecule shape, keep substituents in their proper positions, and also influence global molecular properties. In the present study, a database of 4 million medicinal chemistry-relevant ring systems has been created, not by crude random enumeration but by applying a set of rules derived by analyzing rings present in bioactive molecules. The aromatic properties and tautomer stability of generated rings have also been considered to ensure that the rings in the database are stable and chemically reasonable...

OBJECTIVES: This study aimed to develop and validate evaluation metric for an automated smile classification model termed the "smile index." This innovative model uses computational methods to numerically classify and analyze conventional smile types. METHODS: The datasets used in this study consisted of 300 images to verify, 150 images to validate, and nine images to test the evaluation metric. Images were annotated using Labelme. Computational techniques were used to calculate smile index values for the study datasets, and the resulting values were evaluated in three stages...

AIM: The goal of this clinical report was to present an alternative to traditional flat bone reduction guides, using a custom-designed 3D printed guide according to the future gingival margin of the planned dentition. MATERIALS AND METHODS: A 61-year-old female with concerns regarding her smile appearance was presented. The initial examination revealed excessive gingival show accompanied by excessive overjet. The dentition was in a failing situation. The proposed treatment plan, relying on the sufficient amount of bone and keratinized tissue, consisted of re-contouring of the alveolar ridge and gingiva and placement of six implants and a FP-1 prosthesis after extraction of all remaining maxillary teeth...

BACKGROUND: Cervical cancer is the fourth most common cancer among women globally and has a strong association with Human Papillomavirus (HPV) infection. Stratified mucinproducing intraepithelial lesion (SMILE), a variant of Adenocarcinoma in situ (AIS), is a rare cervical precancer lesion that is often missed or detected incidentally. CASE PRESENTATION: The present case report briefs the finding of a 39-year-old woman who presented to the gynecological outpatient department with complaints of vaginal discharge for 6-8 months...

In recent years, the application of deep learning in molecular de novo design has gained significant attention. One successful approach involves using SMILES representations of molecules and treating the generation task as a text generation problem, yielding promising results. However, the generation of more effective and novel molecules remains a key research area. Due to the fact that a molecule can have multiple SMILES representations, it is not sufficient to consider only one of them for molecular generation...

Existing formats based on the simplified molecular input line entry system (SMILES) encoding and molecular graph structure are designed to encode the complete semantic and structural information of molecules. However, the physicochemical properties of molecules are complex, and a single encoding of molecular features from SMILES sequences or molecular graph structures cannot adequately represent molecular information. Aiming to address this problem, this study proposes a sequence graph cross-attention (SG-ATT) representation architecture for a molecular property prediction model to efficiently use domain knowledge to enhance molecular graph feature encoding and combine the features of molecular SMILES sequences...

Facial expressions are among the earliest behaviors infants use to express emotional states, and are crucial to preverbal social interaction. Manual coding of infant facial expressions, however, is laborious and poses limitations to replicability. Recent developments in computer vision have advanced automated facial expression analyses in adults, providing reproducible results at lower time investment. Baby FaceReader 9 is commercially available software for automated measurement of infant facial expressions, but has received little validation...

Computational methods play a pivotal role in the pursuit of efficient drug discovery, enabling the rapid assessment of compound properties before costly and time-consuming laboratory experiments. With the advent of technology and large data availability, machine and deep learning methods have proven efficient in predicting molecular solubility. High-precision in silico solubility prediction has revolutionized drug development by enhancing formulation design, guiding lead optimization, and predicting pharmacokinetic parameters...

Cervical cancer screening is a crucial field of femtech (female technology). In this work, we disclosed a new femtech solution─a simple, straightforward, and on-site applicable urine-based cervical cancer diagnostic method using a fluorescent biothiol probe. Our newly developed nitrobenzene-based fluorescent probe, named NPS-B , effectively differentiates between cysteine and homocysteine within urine samples via controlled Smiles rearrangement. The analysis of emission-based signals offers the potential utility of this method in cervical cancer...

In recent times, nanomaterials find extensive applications across diverse domains owing to their distinctive physical and chemical characteristics. It is of great importance in theoretical and practical terms to carry out the relationship between structural characteristics of nanomaterials and different cytotoxicity and to achieve practical assessment and prediction of cytotoxicity. This study investigated the intrinsic quantitative constitutive relationships between the cytotoxicity of nano-metal oxides on human normal lung epithelial cells and human lung adenocarcinoma cells...

BACKGROUND: Nitrous oxide has shown potentially as an efficacious intervention for treatment-resistant depression, yet there remains insufficient evidence pertaining to repeated administration of nitrous oxide over time and active placebo-controlled studies with optimal blinding. Thus, we aim to examine the feasibility and preliminary efficacy of a six-week follow up study examining the effects of a 4 week course of weekly administered nitrous oxide as compared to the active placebo, midazolam...

Design of molecules for candidate compound selection is one of the central challenges in drug discovery due to the complexity of chemical space and requirement of multi-parameter optimization. Here we present an application scenario-oriented platform (ID4Idea) for molecular generation in different scenarios of drug discovery. This platform utilizes both library or rule based and generative based algorithms (VAE, RNN, GAN etc.), in combination with various AI learning types (pre-training, transfer learning, reinforcement learning, and active learning etc...