m6A modification: recent advances, anticancer targeted drug discovery and beyond
LJ Deng, WQ Deng, SR Fan, MF Chen, M Qi, WY Lyu… - Molecular cancer, 2022 - Springer
Abstract Abnormal N6-methyladenosine (m6A) modification is closely associated with the
occurrence, development, progression and prognosis of cancer, and aberrant m6A …
occurrence, development, progression and prognosis of cancer, and aberrant m6A …
Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions
New drug production, from target identification to marketing approval, takes over 12 years
and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the …
and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the …
AlphaFold2 protein structure prediction: Implications for drug discovery
N Borkakoti, JM Thornton - Current opinion in structural biology, 2023 - Elsevier
The drug discovery process involves designing compounds to selectively interact with their
targets. The majority of therapeutic targets for low molecular weight (small molecule) drugs …
targets. The majority of therapeutic targets for low molecular weight (small molecule) drugs …
Opportunities and challenges in application of artificial intelligence in pharmacology
Artificial intelligence (AI) is a machine science that can mimic human behaviour like
intelligent analysis of data. AI functions with specialized algorithms and integrates with deep …
intelligent analysis of data. AI functions with specialized algorithms and integrates with deep …
The machine learning life cycle and the cloud: implications for drug discovery
O Spjuth, J Frid, A Hellander - Expert opinion on drug discovery, 2021 - Taylor & Francis
Introduction: Artificial intelligence (AI) and machine learning (ML) are increasingly used in
many aspects of drug discovery. Larger data sizes and methods such as Deep Neural …
many aspects of drug discovery. Larger data sizes and methods such as Deep Neural …
Artificial intelligence in pharmacology research and practice
M van der Lee, JJ Swen - Clinical and Translational Science, 2023 - Wiley Online Library
In recent years, the use of artificial intelligence (AI) in health care has risen steadily,
including a wide range of applications in the field of pharmacology. AI is now used …
including a wide range of applications in the field of pharmacology. AI is now used …
[HTML][HTML] Artificial intelligence and machine learning in pharmacological research: bridging the gap between data and drug discovery
Artificial intelligence (AI) has transformed pharmacological research through machine
learning, deep learning, and natural language processing. These advancements have …
learning, deep learning, and natural language processing. These advancements have …
Success stories of AI in drug discovery-where do things stand?
Introduction Artificial intelligence (AI) in drug discovery and development (DDD) has gained
more traction in the past few years. Many scientific reviews have already been made …
more traction in the past few years. Many scientific reviews have already been made …
Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network
As safety is one of the most important properties of drugs, chemical toxicology prediction has
received increasing attentions in the drug discovery research. Traditionally, researchers rely …
received increasing attentions in the drug discovery research. Traditionally, researchers rely …
Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting
The identification of druggable proteins (DPs) is significant for the development of new
drugs, personalized medicine, understanding of disease mechanisms, drug repurposing …
drugs, personalized medicine, understanding of disease mechanisms, drug repurposing …