Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data. Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change. Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

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ORGANIC (1).pdf

10.26434/chemrxiv.5309668.v1 ◽

2017 ◽

Author(s):

Benjamin Sanchez-Lengeling ◽

Carlos Outeiral ◽

Gabriel L. Guimaraes ◽

Alan Aspuru-Guzik

Keyword(s):

Machine Learning ◽

Learning Community ◽

Chemical Species ◽

Material Design ◽

Organic Photovoltaic ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

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Machine Learning for Acute Toxicity Prediction Using High-Throughput Enzyme-Reaction Chip

10.26434/chemrxiv.7263596.v2 ◽

2019 ◽

Author(s):

Qiannan Duan ◽

Jianchao Lee ◽

Jinhong Gao ◽

Jiayuan Chen ◽

Yachao Lian ◽

...

Keyword(s):

Machine Learning ◽

Acute Toxicity ◽

Model Building ◽

Low Cost ◽

Enzyme Reaction ◽

Chemical Effect ◽

Technological Innovations ◽

Acute Toxicity Test ◽

Toxicity Prediction ◽

Traditional Understanding

<p>Machine learning (ML) has brought significant technological innovations in many fields, but it has not been widely embraced by most researchers of natural sciences to date. Traditional understanding and promotion of chemical analysis cannot meet the definition and requirement of big data for running of ML. Over the years, we focused on building a more versatile and low-cost approach to the acquisition of copious amounts of data containing in a chemical reaction. The generated data meet exclusively the thirst of ML when swimming in the vast space of chemical effect. As proof in this study, we carried out a case for acute toxicity test throughout the whole routine, from model building, chip preparation, data collection, and ML training. Such a strategy will probably play an important role in connecting ML with much research in natural science in the future.</p>

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Development of innovative hygienic technologies for preservation of the workers’ reproductive health

Russian Journal of Occupational Health and Industrial Ecology ◽

10.31089/1026-9428-2019-59-9-792-793 ◽

2020 ◽

pp. 792-792

Author(s):

M. A. Fesenko ◽

G. V. Golovaneva ◽

A. V. Miskevich

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Reproductive Health ◽

Reproductive System ◽

Preventive Measures ◽

Learning Algorithms ◽

Reproductive Function ◽

Machine Learning Algorithms ◽

New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

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A Survey of Fair and Responsible Machine Learning and Artificial Intelligence: Implications of Consumer Financial Services

SSRN Electronic Journal ◽

10.2139/ssrn.3527034 ◽

2020 ◽

Author(s):

Stephen Rea

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Financial Services

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A Methodology for Software Cost Estimation Using Machine Learning Techniques: International conference on Recent Trends in Artificial Intelligence, IOT, Smart Cities & Applications (ICAISC-2020)

SSRN Electronic Journal ◽

10.2139/ssrn.3647639 ◽

2020 ◽

Author(s):

Md aaquib nawaz ◽

Mrityunjay kumar ◽

Md kashif zaki ◽

Sunil Kumar Gouda ◽

Prithvi Raj ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Cost Estimation ◽

Smart Cities ◽

Machine Learning Techniques ◽

Software Cost Estimation ◽

Software Cost ◽

International Conference ◽

Learning Techniques ◽

Recent Trends

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Detection of Virus Using Artificial Intelligence and Machine Learning Concepts

SSRN Electronic Journal ◽

10.2139/ssrn.3701754 ◽

2020 ◽

Author(s):

Sai Harshavardhan A.V.N ◽

Akash Ranjan

Keyword(s):

Artificial Intelligence ◽

Machine Learning

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Virtual Screening Meets Deep Learning

Current Computer - Aided Drug Design ◽

10.2174/1573409914666181018141602 ◽

2018 ◽

Vol 15 (1) ◽

pp. 6-28 ◽

Cited By ~ 6

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.

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