[HTML][HTML] Artificial intelligence in pharmaceutical technology and drug delivery design
Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic
knowledge and provides expedited solutions to complex challenges. Remarkable …
knowledge and provides expedited solutions to complex challenges. Remarkable …
Deep learning in drug discovery: an integrative review and future challenges
H Askr, E Elgeldawi, H Aboul Ella… - Artificial Intelligence …, 2023 - Springer
Recently, using artificial intelligence (AI) in drug discovery has received much attention
since it significantly shortens the time and cost of developing new drugs. Deep learning (DL) …
since it significantly shortens the time and cost of developing new drugs. Deep learning (DL) …
Single-cell analysis highlights differences in druggable pathways underlying adaptive or fibrotic kidney regeneration
The kidney has tremendous capacity to repair after acute injury, however, pathways guiding
adaptive and fibrotic repair are poorly understood. We developed a model of adaptive and …
adaptive and fibrotic repair are poorly understood. We developed a model of adaptive and …
A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions
Drug repositioning is the process of identifying novel therapeutic potentials for existing drugs
and discovering therapies for untreated diseases. Drug repositioning, therefore, plays an …
and discovering therapies for untreated diseases. Drug repositioning, therefore, plays an …
Concepts of artificial intelligence for computer-assisted drug discovery
X Yang, Y Wang, R Byrne, G Schneider… - Chemical …, 2019 - ACS Publications
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …
opportunities for the discovery and development of innovative drugs. Various machine …
Drug repurposing: progress, challenges and recommendations
Given the high attrition rates, substantial costs and slow pace of new drug discovery and
development, repurposing of'old'drugs to treat both common and rare diseases is …
development, repurposing of'old'drugs to treat both common and rare diseases is …
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper
The task of predicting the interactions between drugs and targets plays a key role in the
process of drug discovery. There is a need to develop novel and efficient prediction …
process of drug discovery. There is a need to develop novel and efficient prediction …
Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities
New technologies have enabled the investigation of biology and human health at an
unprecedented scale and in multiple dimensions. These dimensions include a myriad of …
unprecedented scale and in multiple dimensions. These dimensions include a myriad of …
Single-cell transcriptional changes associated with drug tolerance and response to combination therapies in cancer
Tyrosine kinase inhibitors were found to be clinically effective for treatment of patients with
certain subsets of cancers carrying somatic mutations in receptor tyrosine kinases. However …
certain subsets of cancers carrying somatic mutations in receptor tyrosine kinases. However …
A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information
Y Luo, X Zhao, J Zhou, J Yang, Y Zhang… - Nature …, 2017 - nature.com
The emergence of large-scale genomic, chemical and pharmacological data provides new
opportunities for drug discovery and repositioning. In this work, we develop a computational …
opportunities for drug discovery and repositioning. In this work, we develop a computational …