scholarly journals A Sequence-to-Sequence Transformer Model for Disconnection Aware Retrosynthesis

2021 ◽  
Author(s):  
Andrea Byekwaso ◽  
Alain C. Vaucher ◽  
Philippe Schwaller ◽  
Alessandra Toniato ◽  
Teodoro Laino
Keyword(s):  
Functional Group ◽  
Ground Truth ◽  
Single Step ◽  
Computer Assisted ◽  
Modern Computer ◽  
Forward Prediction ◽  
Synthesis Planning ◽  
Correct Location ◽  
Clear Idea ◽  
Transformer Model

Retrosynthesis is an approach commonly undertaken when considering the manufacture of novel molecules. During this process, a target molecule is broken down and analyzed by considering the bonds to be changed as well as the functional group interconversion. In modern computer-assisted synthesis planning tools, the predictions of these changes are typically carried out automatically. However there may be some benefit to the decision being guided by those executing the process: typically, chemists have a clear idea where the retrosynthetic change should happen, but not how such a transformation is to be realized. Using a data-driven model, the retrosynthesis task can be further explored by giving chemists the option to explore specific disconnections. In this work, we design an approach to provide this option by adapting a transformer-based model for single-step retrosynthesis. The model takes as input a product SMILES string, in which the atoms where the transformation should occur are tagged accordingly. This model predicts precursors corresponding to a disconnection occurring in the correct location in 88.9% of the test set reactions. The assessment with a forward prediction model shows that 76% of the predictions are chemically correct, with 14.1% perfectly matching the ground truth.

scholarly journals Identification of Skin Lesions by Using Single-Step Multiframe Detector

2021 ◽  
Vol 10 (1) ◽  
pp. 144
Author(s):  
Yu-Ping Hsiao ◽  
Chih-Wei Chiu ◽  
Chih-Wei Lu ◽  
Hong Thai Nguyen ◽  
Yu Sheng Tseng ◽  
...  
Keyword(s):  
Ground Truth ◽  
Skin Lesions ◽  
Single Step ◽  
Single Shot ◽  
Accuracy Rate ◽  
Tissue Slices ◽  
Image Detection ◽  
Sensitivity Rate ◽  
Diagnosis Accuracy ◽  
Artificial Intelligence Algorithm

An artificial intelligence algorithm to detect mycosis fungoides (MF), psoriasis (PSO), and atopic dermatitis (AD) is demonstrated. Results showed that 10 s was consumed by the single shot multibox detector (SSD) model to analyze 292 test images, among which 273 images were correctly detected. Verification of ground truth samples of this research come from pathological tissue slices and OCT analysis. The SSD diagnosis accuracy rate was 93%. The sensitivity values of the SSD model in diagnosing the skin lesions according to the symptoms of PSO, AD, MF, and normal were 96%, 80%, 94%, and 95%, and the corresponding precision were 96%, 86%, 98%, and 90%. The highest sensitivity rate was found in MF probably because of the spread of cancer cells in the skin and relatively large lesions of MF. Many differences were found in the accuracy between AD and the other diseases. The collected AD images were all in the elbow or arm and other joints, the area with AD was small, and the features were not obvious. Hence, the proposed SSD could be used to identify the four diseases by using skin image detection, but the diagnosis of AD was relatively poor.

scholarly journals Datasets and Their Influence on the Development of Computer Assisted Synthesis Planning Tools in the Pharmaceutical Domain

2019 ◽  
Author(s):  
Amol Thakkar ◽  
Thierry Kogej ◽  
Jean-Louis Reymond ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Model Performance ◽  
Computer Assisted ◽  
Patent Office ◽  
United States Patent ◽  
Planning Tool ◽  
Virtual Libraries ◽  
Synthesis Planning ◽  
Synthetic Routes ◽  
The Cost ◽  
States Patent

<p>Computer Assisted Synthesis Planning (CASP) has gained considerable interest as of late. Herein we investigate a template-based retrosynthetic planning tool, trained on a variety of datasets consisting of up to 17.5 million reactions. We demonstrate that models trained on datasets such as internal Electronic Laboratory Notebooks (ELN), and the publicly available United States Patent Office (USPTO) extracts, are sufficient for the prediction of full synthetic routes to compounds of interest in medicinal chemistry. As such we have assessed the models on 1,731 compounds from 41 virtual libraries for which experimental results were known. Furthermore, we show that accuracy is a misleading metric for assessment of the ‘filter network’, and propose that the number of successfully applied templates, in conjunction with the overall ability to generate full synthetic routes be examined instead. To this end we found that the specificity of the templates comes at the cost of generalizability, and overall model performance. This is supplemented by a comparison of the underlying datasets and their corresponding models.</p>

scholarly journals Automatic Retrosynthetic Pathway Planning Using Template-free Models

2019 ◽  
Author(s):  
Kangjie Lin ◽  
Jianfeng Pei ◽  
Luhua Lai ◽  
Youjun Xu,
Keyword(s):  
Monte Carlo ◽  
Route Planning ◽  
The United States ◽  
Single Step ◽  
Drug Candidate ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Patent Literature ◽  
Acute Myeloid Leukemia Cells ◽  
Transformer Model

<div><div><div><p>We present an attention-based Transformer model for automatic retrosynthesis route planning. Our approach starts from <a></a><a>reactants prediction of single-step organic reactions for gi</a>ven products, <a>followed by Monte Carlo tree search-based automatic retrosynthetic pathway prediction</a>. Trained on two datasets from the United States patent literature, our models achieved a top-1 prediction accuracy of over 54.6% and 63.0% with more than 95% and 99.6% validity rate of SMILES, respectively, which is the best up to now to our knowledge. We also demonstrate the application potential of our model by successfully performing multi-step retrosynthetic route planning for four case products, i.e., antiseizure drug Rufinamide, a novel allosteric activator, an inhibitor of human acute-myeloid-leukemia cells and a complex intermediate of drug candidate. Further, by using heuristics Monte Carlo tree search, we achieved automatic retrosynthetic pathway searching and successfully reproduced published synthesis pathways. In summary, our model has achieved the state-of-the-art performance on single-step retrosynthetic prediction and provides a novel strategy for automatic retrosynthetic pathway planning. </p><div> <div><div><p><br></p></div></div><div><div> </div> </div> </div><br><p></p></div></div></div>

Constructing Knowledge-Based Feedback in the Context of an Interactive Spoken CALL Application

2020 ◽  
pp. 48-70
Author(s):  
Nikos Tsourakis ◽  
Claudia Baur ◽  
Manny Rayner
Keyword(s):  
Language Learning ◽  
User Interaction ◽  
Feedback Mechanism ◽  
Computer Assisted ◽  
Secondary Classrooms ◽  
Knowledge Based ◽  
Modern Computer ◽  
German Speaking ◽  
Spoken Language Skills ◽  
Constructing Knowledge

Modern Computer Assisted Language Learning (CALL) systems use speech recognition to give students the opportunity to build up their spoken language skills through interactive practice with a mechanical partner. Besides the obvious benefits that these systems can offer, e.g. flexible and inexpensive learning, user interaction in this context can often be problematic. In this article, the authors introduce a parallel layer of feedback in a CALL application, which can monitor interaction, report errors and provide advice and suggestions to students. This mechanism combines knowledge accumulated from four different inputs in order to decide on appropriate feedback, which can be customized and adapted in terms of phrasing, style and language. The authors report the results from experiments conducted at six lower secondary classrooms in German-speaking Switzerland with and without this mechanism. After analyzing approximately 13,000 spoken interactions it can be reasonably argued that their parallel feedback mechanism in L2 actually does help students during interaction and contributes as a motivation factor.

Challenges and Perspectives of Computerassisted Instruction in Medical Education

2007 ◽  
Vol 46 (01) ◽  
pp. 67-69 ◽  
Author(s):  
R. Singer ◽  
M. Bauch ◽  
J. Heid ◽  
F. Hess ◽  
F.J. Leven ◽  
...  
Keyword(s):  
Medical Education ◽  
Computer Assisted Instruction ◽  
Computer Assisted ◽  
Curricular Integration ◽  
Modern Problem ◽  
Shell System ◽  
Modern Computer ◽  
Self Study ◽  
Teachers And Students ◽  
Assisted Instruction

Summary Objectives: In this paper we discuss solutions to the problem that medical teachers and students do not use modern computer-assisted instruction systems in medical education as much as expected by their developers. Methods: As an example for a modern problem-based CAI system we introduce the CAMPUS shell system for case-based training in medicine. Results: CAMPUS has received several awards and positive evaluation results. Nevertheless, the usage of such systems in courses and for self-study could be increased. Conclusions: Curricular integration of CAI as well as further improvements on existing CAI systems to increase the usage in medical education is essential.

scholarly journals The Effects of Computer-Assisted Learning Based on Dual Coding Theory

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 701
Author(s):  
Xianghu Liu ◽  
Chia-Hui Liu ◽  
Yang Li
Keyword(s):  
Language Learning ◽  
Coding Theory ◽  
Dual Coding ◽  
Traditional Learning ◽  
Computer Assisted ◽  
Computer Assisted Learning ◽  
Dual Coding Theory ◽  
Modern Computer ◽  
Learning Attitude ◽  
Post Test

This research explored the integration of dual coding theory and modern computer technology with symmetry into a vocabulary class to improve students’ learning attitude and effectiveness. Three research questions are addressed in this research on the effects of computer-assisted learning based on dual coding theory (DCT). This experimental research was carried out in a high school in a remote rural area in China. The study was conducted in two parallel classes (the experimental and the control) in Grade 8 with a total of 88 students. Our research methods included pre- and post-test, questionnaires, and an interview with symmetry as the focus to obtain the results as follows: (1) Using the integration of computer assisted language learning (CALL) and DCT to effectively improve students’ learning attitude, (2) transforming students’ traditional learning methods into the dual coding method, and (3) enhancing students’ vocabulary learning effectiveness.

Characterizing a Mississippian tripolitic chert reservoir using 3D unsupervised and supervised multiattribute seismic facies analysis: An example from Osage County, Oklahoma

2013 ◽  
Vol 1 (2) ◽  
pp. SB109-SB124 ◽  
Author(s):  
Atish Roy ◽  
Benjamin L. Dowdell ◽  
Kurt J. Marfurt
Keyword(s):  
Ground Truth ◽  
Seismic Facies ◽  
Seismic Survey ◽  
Computer Assisted ◽  
Seismic Attribute ◽  
Self Organizing Maps ◽  
Seismic Facies Analysis ◽  
Tripolitic Chert ◽  
Mississippi Lime ◽  
Osage County

Seismic interpretation is based on the identification of reflector configuration and continuity, with coherent reflectors having a distinct amplitude, frequency, and phase. Skilled interpreters may classify reflector configurations as parallel, converging, truncated, or hummocky, and use their expertise to identify stratigraphic packages and unconformities. In principal, a given pattern can be explicitly defined as a combination of waveform and reflector configuration properties, although such “clustering” is often done subconsciously. Computer-assisted classification of seismic attribute volumes builds on the same concepts. Seismic attributes not only quantify characteristics of the seismic reflection events, but also measure aspects of reflector configurations. The Mississippi Lime resource play of northern Oklahoma and southern Kansas provides a particularly challenging problem. Instead of defining the facies stratigraphically, we need to define them either diagenetically (tight limestone, stratified limestone and nonporous chert, and highly porous tripolitic chert) or structurally (fractured versus unfractured chert and limestone). Using a 3D seismic survey acquired in Osage County Oklahoma, we use Kohonen self-organizing maps to classify different diagenetically altered facies of the Mississippi Lime play. The 256 prototype vectors (potential clusters) reduce to only three or four distinct “natural” clusters. We use ground truth of seismic facies seen on horizontal image logs to fix three average attribute data vectors near the well locations, resulting in three “known” facies, and do a minimum Euclidean distance supervised classification. The predicted clusters correlate well to the poststack impedance inversion result.

scholarly journals CompRet: A Comprehensive Recommendation Framework for Chemical Synthesis Planning with Algorithmic Enumeration

2020 ◽  
Author(s):  
Ryosuke Shibukawa ◽  
Shoichi Ishida ◽  
Kazuki Yoshizoe ◽  
Kunihiro Wasa ◽  
Kiyosei Takasu ◽  
...  
Keyword(s):  
Chemical Synthesis ◽  
Chemical Reaction ◽  
Reaction Network ◽  
Exact Algorithm ◽  
Chemical Reaction Network ◽  
Computer Assisted ◽  
Enumeration Algorithm ◽  
Synthesis Planning ◽  
Synthetic Routes ◽  
Alternative Routes

In computer-assisted synthesis planning (CASP) programs, providing chemical synthetic routes as many as possible is essential for considering optimal and alternative routes in a chemical reaction network. As the majority of CASP programs have been designed to provide one or a few optimal routes, it is likely that desired one will not be included. To avoid this, an exact algorithm that lists possible synthetic routes from the chemical reaction network is required, alongside a recommendation of synthetic routes that meet specified criteria based on chemist's objectives. Herein, we propose a chemical-reaction-network-based synthetic route recommendation framework called "CompRet" with a mathematically guaranteed enumeration algorithm. In a preliminary experiment, CompRet was shown to successfully provide alternative routes for a known antihistaminic drug, cetirizine. CompRet is expected to promote desirable enumeration-based chemical synthesis searches and aid the development of an interactive CASP framework for chemists.

scholarly journals A Research on CALL-Based POA in English Writing Teaching

2021 ◽  
Vol 5 (12) ◽  
pp. 156-161
Author(s):  
Peipei Zhou
Keyword(s):  
Language Learning ◽  
Computer Assisted ◽  
Computer Assisted Language Learning ◽  
English Learning ◽  
Teaching Mode ◽  
Learning And Teaching ◽  
English Writing ◽  
Modern Computer ◽  
Hybrid Teaching ◽  
Modern Computer Technology

The combination of production-oriented approach (POA) and modern computer technology makes English learning and teaching more convenient and efficient. This research combines the computer-assisted language learning (CALL) mode with POA to form the online and offline hybrid teaching mode, aiming to provide a reference for English teachers.

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