scholarly journals Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship

2021 ◽  
Author(s):  
Maciej Staszak ◽  
Katarzyna Staszak ◽  
Karolina Wieszczycka ◽  
Anna Bajek ◽  
Krzysztof Roszkowski ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Biological Activity ◽  
Drug Design ◽  
Chemical Structure ◽  
Activity Relationship

Artificial Intelligence (AI) in Drugs and Pharmaceuticals

2021 ◽  
Vol 25 ◽  
Author(s):  
Adarsh Sahu ◽  
Jyotika Mishra ◽  
Namrata Kushwaha
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Design ◽  
De Novo ◽  
Pharmaceutical Companies ◽  
Design Activity ◽  
De Novo Drug Design ◽  
Preclinical Trials ◽  
The Cost ◽  
Heath Care

: The advancement of computing and technology has invaded all the dimensions of science. Artificial intelligence (AI) is one core branch of Computer Science, which has percolated to all the arenas of science and technology, from core engineering to medicines. Thus, AI has found its way for application in the field of medicinal chemistry and heath care. The conventional methods of drug design have been replaced by computer-aided designs of drugs in recent times. AI is being used extensively to improve the design techniques and required time of the drugs. Additionally, the target proteins can be conveniently identified using AI, which enhances the success rate of the designed drug. The AI technology is used in each step of the drug designing procedure, which decreases the health hazards related to preclinical trials and also reduces the cost substantially. The AI is an effective tool for data mining based on the huge pharmacological data and machine learning process. Hence, AI has been used in de novo drug design, activity scoring, virtual screening and in silico evaluation in the properties (absorption, distribution, metabolism, excretion and toxicity) of a drug molecule. Various pharmaceutical companies have teamed up with AI companies for faster progress in the field of drug development, along with the healthcare system. The review covers various aspects of AI (Machine learning, Deep learning, Artificial neural networks) in drug design. It also provides a brief overview of the recent progress by the pharmaceutical companies in drug discovery by associating with different AI companies.

scholarly journals Analysis of Trabecular Bone Mechanics Using Machine Learning

2019 ◽  
Vol 15 ◽  
pp. 117693431882508 ◽  
Author(s):  
Ayesha Sohail ◽  
Muhammad Younas ◽  
Yousaf Bhatti ◽  
Zhiwu Li ◽  
Sümeyye Tunç ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Design ◽  
Computational Model ◽  
Dynamic Process ◽  
Bone Mechanics ◽  
Bone Microenvironment ◽  
Artificial Intelligence Algorithm ◽  
Surface Remodeling ◽  
Trabecular Surface

“Bone remodeling” is a dynamic process, and mutliphase analysis incorporated with the forecasting algorithm can help the biologists and orthopedics to interpret the laboratory generated results and to apply them in improving applications in the fields of “drug design, treatment, and therapy” of diseased bones. The metastasized bone microenvironment has always remained a challenging puzzle for the researchers. A multiphase computational model is interfaced with the artificial intelligence algorithm in a hybrid manner during this research. Trabecular surface remodeling is presented in this article, with the aid of video graphic footage, and the associated parametric thresholds are derived from artificial intelligence and clinical data.

scholarly journals Improving prediction of compound function from chemical structure using chemical-genetic networks

2017 ◽  
Author(s):  
Hamid Safizadeh ◽  
Scott W. Simpkins ◽  
Justin Nelson ◽  
Chad L. Myers
Keyword(s):  
Machine Learning ◽  
Biological Activity ◽  
Chemical Structure ◽  
Genetic Interaction ◽  
Structural Similarity ◽  
Superior Performance ◽  
Interaction Data ◽  
Similarity Coefficients ◽  
Chemical Genetic ◽  
Genetic Interaction Data

ABSTRACTThe drug discovery process can be significantly improved through understanding how the structure of chemical compounds relates to their function. A common paradigm that has been used to filter and prioritize compounds is ligand-based virtual screening, where large libraries of compounds are queried for high structural similarity to a target molecule, with the assumption that structural similarity is predictive of similar biological activity. Although the chemical informatics community has already proposed a wide range of structure descriptors and similarity coefficients, a major challenge has been the lack of systematic and unbiased benchmarks for biological activity that covers a broad range of targets to definitively assess the performance of the alternative approaches.We leveraged a large set of chemical-genetic interaction data from the yeast Saccharomyces cerevisiae that our labs have recently generated, covering more than 13,000 compounds from the RIKEN NPDepo and several NCI, NIH, and GlaxoSmithKline (GSK) compound collections. Supportive of the idea that chemical-genetic interaction data provide an unbiased proxy for biological functions, we found that many commonly used structural similarity measures were able to predict the compounds that exhibited similar chemical-genetic interaction profiles, although these measures did exhibit significant differences in performance. Using the chemical-genetic interaction profiles as a basis for our evaluation, we performed a systematic benchmarking of 10 different structure descriptors, each combined with 12 different similarity coefficients. We found that the All-Shortest Path (ASP) structure descriptor paired with the Braun-Blanquet similarity coefficient provided superior performance that was robust across several different compound collections.We further describe a machine learning approach that improves the ability of the ASP metric to capture biological activity. We used the ASP fingerprints as input for several supervised machine learning models and the chemical-genetic interaction profiles as the standard for learning. We found that the predictive power of the ASP fingerprints (as well as several other descriptors) could be substantially improved by using support vector machines. For example, on held-out data, we measured a 5-fold improvement in the recall of biologically similar compounds at a precision of 50% based upon the ASP fingerprints. Our results generally suggest that using high-dimensional chemical-genetic data as a basis for refining chemical structure descriptors can be a powerful approach to improving prediction of biological function from structure.

scholarly journals Artificial intelligence in drug design: algorithms, applications, challenges and ethics

2021 ◽  
pp. FDD59
Author(s):  
Alya A Arabi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Discovery ◽  
Drug Design ◽  
Chemical Properties ◽  
Binding Affinities ◽  
Future Perspectives ◽  
Ethical Considerations ◽  
Brain Barrier Permeability ◽  
And Chemical Properties

The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

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.

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

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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