Mapping the resources and approaches facilitating computer-aided synthesis planning

2021 ◽  
Author(s):  
Zheng Wang ◽  
Wei Zhao ◽  
Gefei Hao ◽  
Baoan Song
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Organic Synthesis ◽  
Computer Aided ◽  
Synthesis Planning ◽  
Manual Tasks

Computer-aided synthesis planning could facilitate organic synthesis study and relieve chemists of manual tasks. Artificial intelligence and deep learning would be useful for the development of computer-aided synthesis planning.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

scholarly journals Retrosynthetic Accessibility Score (RAscore) - Rapid Machine Learned Synthesizability Classification from AI Driven Retrosynthetic Planning

2020 ◽  
Author(s):  
Amol Thakkar ◽  
Veronika Chadimova ◽  
Esben Jannik Bjerrum ◽  
Ola Engkvist ◽  
Jean-Louis Reymond
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Virtual Screening ◽  
Generative Models ◽  
Synthetic Route ◽  
Synthetic Accessibility ◽  
Wide Range ◽  
Computer Aided ◽  
Synthesis Planning ◽  
Retrosynthetic Analysis

<p>Computer aided synthesis planning (CASP) is part of a suite of artificial intelligence (AI) based tools that are able to propose synthesis to a wide range of compounds. However, at present they are too slow to be used to screen the synthetic feasibility of millions of generated or enumerated compounds before identification of potential bioactivity by virtual screening (VS) workflows. Herein we report a machine learning (ML) based method capable of classifying whether a synthetic route can be identified for a particular compound or not by the CASP tool AiZynthFinder. The resulting ML models return a retrosynthetic accessibility score (RAscore) of any molecule of interest, and computes 4,500 times faster than retrosynthetic analysis performed by the underlying CASP tool. The RAscore should be useful for the pre-screening millions of virtual molecules from enumerated databases or generative models for synthetic accessibility and produce higher quality databases for virtual screening of biological activity. </p>

Artificial intelligence and automation in computer aided synthesis planning

2021 ◽  
Vol 6 (1) ◽  
pp. 27-51
Author(s):  
Amol Thakkar ◽  
Simon Johansson ◽  
Kjell Jorner ◽  
David Buttar ◽  
Jean-Louis Reymond ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Aided ◽  
Synthesis Planning

In this perspective we deal with questions pertaining to the development of synthesis planning technologies over the course of recent years.

scholarly journals Diagnosis of common pulmonary diseases in children by X-ray images and deep learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Kai-Chi Chen ◽  
Hong-Ren Yu ◽  
Wei-Shiang Chen ◽  
Wei-Che Lin ◽  
Yi-Chen Lee ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Respiratory Infection ◽  
Pulmonary Diseases ◽  
Lower Respiratory Infection ◽  
X Ray ◽  
Lobar Pneumonia ◽  
Computer Aided ◽  
Chest X Ray ◽  
The One

Abstract Acute lower respiratory infection is the leading cause of child death in developing countries. Current strategies to reduce this problem include early detection and appropriate treatment. Better diagnostic and therapeutic strategies are still needed in poor countries. Artificial-intelligence chest X-ray scheme has the potential to become a screening tool for lower respiratory infection in child. Artificial-intelligence chest X-ray schemes for children are rare and limited to a single lung disease. We need a powerful system as a diagnostic tool for most common lung diseases in children. To address this, we present a computer-aided diagnostic scheme for the chest X-ray images of several common pulmonary diseases of children, including bronchiolitis/bronchitis, bronchopneumonia/interstitial pneumonitis, lobar pneumonia, and pneumothorax. The study consists of two main approaches: first, we trained a model based on YOLOv3 architecture for cropping the appropriate location of the lung field automatically. Second, we compared three different methods for multi-classification, included the one-versus-one scheme, the one-versus-all scheme and training a classifier model based on convolutional neural network. Our model demonstrated a good distinguishing ability for these common lung problems in children. Among the three methods, the one-versus-one scheme has the best performance. We could detect whether a chest X-ray image is abnormal with 92.47% accuracy and bronchiolitis/bronchitis, bronchopneumonia, lobar pneumonia, pneumothorax, or normal with 71.94%, 72.19%, 85.42%, 85.71%, and 80.00% accuracy, respectively. In conclusion, we provide a computer-aided diagnostic scheme by deep learning for common pulmonary diseases in children. This scheme is mostly useful as a screening for normal versus most of lower respiratory problems in children. It can also help review the chest X-ray images interpreted by clinicians and may remind possible negligence. This system can be a good diagnostic assistance under limited medical resources.

A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges

2021 ◽  
Vol 19 ◽  
Author(s):  
Xi Chen ◽  
Yu Lei ◽  
Jiabin Su ◽  
Heng Yang ◽  
Wei Ni ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Medical Imaging ◽  
Arteriovenous Malformation ◽  
Moyamoya Disease ◽  
Cerebrovascular Diseases ◽  
Machine Learning Techniques ◽  
Computer Aided Detection ◽  
Computer Aided

Background: A variety of emerging medical imaging technologies based on artificial intelligence have been widely applied in many diseases, but they are still limited used in the cerebrovascular field even though the diseases can lead to catastrophic consequences. Objective: This work aims to discuss the current challenges and future directions of artificial intelligence technology in cerebrovascular diseases through reviewing the existing literature related to applications in terms of computer-aided detection, prediction and treatment of cerebrovascular diseases. Methods: Based on artificial intelligence applications in four representative cerebrovascular diseases including intracranial aneurysm, arteriovenous malformation, arteriosclerosis and moyamoya disease, this paper systematically reviews studies published between 2006 and 2021 in five databases: National Center for Biotechnology Information, Elsevier Science Direct, IEEE Xplore Digital Library, Web of Science and Springer Link. And three refinement steps were further conducted after identifying relevant literature from these databases. Results: For the popular research topic, most of the included publications involved computer-aided detection and prediction of aneurysms, while studies about arteriovenous malformation, arteriosclerosis and moyamoya disease showed an upward trend in recent years. For the algorithms, both conventional machine learning and deep learning algorithms were utilized in these publications, but machine learning techniques accounted for a larger proportion. Conclusion: Algorithms related to artificial intelligence, especially deep learning, are promising tools for medical imaging analysis and will enhance the performance of computer-aided detection, prediction and treatment of cerebrovascular diseases.

Computational Intelligence for Detection of Skin Cancer using Deep Learning Classifiers

2021 ◽  
Vol 7 (1) ◽  
pp. 10-18
Author(s):  
S. Archana ◽  
◽  
N. Shyamsundar ◽  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Skin Cancer ◽  
Environmental Factors ◽  
Computational Intelligence ◽  
Skin Diseases ◽  
Research Field ◽  
Important Research ◽  
Wide Range ◽  
Computer Aided

Artificial intelligence (AI) has been recognized as an important research field in computer science. Although AI has been around for a while and has been used in many disciplines of medicine, its usage in dermatology is very recent and constrained. Dermatology is a field of bioscience concerned with the diagnosis and treatment of skin diseases. The wide range of dermatologic diseases changes regionally and seasonally according to temperature, humidity, and other environmental factors. Dermatological illnesses have been shown to have major impacts on the behavior of millions of individuals since nearly all forms of skin problems affect everyone every year. Because human analysis of such diseases requires time and effort, and existing techniques are only utilized to analyze certain types of skin diseases, there is a need for higher-level computer-aided skills in the analysis and diagnosis of multi-type skin disorders.

scholarly journals Retrosynthetic Accessibility Score (RAscore) - Rapid Machine Learned Synthesizability Classification from AI Driven Retrosynthetic Planning

2020 ◽  
Author(s):  
Amol Thakkar ◽  
Veronika Chadimova ◽  
Esben Jannik Bjerrum ◽  
Ola Engkvist ◽  
Jean-Louis Reymond
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Virtual Screening ◽  
Generative Models ◽  
Synthetic Route ◽  
Synthetic Accessibility ◽  
Wide Range ◽  
Computer Aided ◽  
Synthesis Planning ◽  
Retrosynthetic Analysis

<p>Computer aided synthesis planning (CASP) is part of a suite of artificial intelligence (AI) based tools that are able to propose synthesis to a wide range of compounds. However, at present they are too slow to be used to screen the synthetic feasibility of millions of generated or enumerated compounds before identification of potential bioactivity by virtual screening (VS) workflows. Herein we report a machine learning (ML) based method capable of classifying whether a synthetic route can be identified for a particular compound or not by the CASP tool AiZynthFinder. The resulting ML models return a retrosynthetic accessibility score (RAscore) of any molecule of interest, and computes 4,500 times faster than retrosynthetic analysis performed by the underlying CASP tool. The RAscore should be useful for the pre-screening millions of virtual molecules from enumerated databases or generative models for synthetic accessibility and produce higher quality databases for virtual screening of biological activity. </p>

scholarly journals A Systematic Review on the Role of Artificial Intelligence in Sonographic Diagnosis of Thyroid Cancer: Past, Present and Future

2021 ◽  
Author(s):  
Fatemeh Abdolali ◽  
Atefeh Shahroudnejad ◽  
Sepideh Amiri ◽  
Abhilash Rakkunedeth Hareendranathan ◽  
Jacob L Jaremko ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Thyroid Cancer ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Computer Aided Diagnosis ◽  
Sonographic Diagnosis ◽  
Computer Aided ◽  
Diagnosis Of Thyroid Cancer ◽  
Aided Diagnosis

Thyroid cancer is common worldwide with a rapid increase in prevalence across North America in recent years. While most patients present with palpable nodules through physical examination, a large number of small and medium-sized nodules are detected by ultrasound examination. Suspicious nodules are then sent for biopsy through fine needle aspiration to determine whether the nodule is malignant. Since biopsies are invasive and sometimes inconclusive, various research groups have tried to develop computer-aided diagnosis systems aimed at characterizing thyroid nodules based on ultrasound scans. Earlier approaches along these lines relied on clinically relevant features that were manually identified by radiologists. With the recent success of Artificial Intelligence (AI), various new methods using deep learning are being developed to identify these features in thyroid ultrasound automatically. In this paper, we present a systematic review of state-of-the-art on Artificial Intelligence (AI) application in sonographic diagnosis of thyroid cancer. This review follows a methodology-based classification of the different techniques available for thyroid cancer diagnosis, from methods using feature-based models to the most recent deep learning-based approaches. In this review, we reflect on the trends and challenges of the field of sonographic diagnosis of thyroid malignancies and potential of computer-aided diagnosis to increase the impact of ultrasound applications on the future of thyroid cancer diagnosis. Machine learning will continue to play a fundamental role in the development of future thyroid cancer diagnosis frameworks.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

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