Machine learning in drug discovery: a review
This review provides the feasible literature on drug discovery through ML tools and
techniques that are enforced in every phase of drug development to accelerate the research …
techniques that are enforced in every phase of drug development to accelerate the research …
Artificial intelligence and machine learning technology driven modern drug discovery and development
C Sarkar, B Das, VS Rawat, JB Wahlang… - International Journal of …, 2023 - mdpi.com
The discovery and advances of medicines may be considered as the ultimate relevant
translational science effort that adds to human invulnerability and happiness. But advancing …
translational science effort that adds to human invulnerability and happiness. But advancing …
A unified drug–target interaction prediction framework based on knowledge graph and recommendation system
Prediction of drug-target interactions (DTI) plays a vital role in drug development in various
areas, such as virtual screening, drug repurposing and identification of potential drug side …
areas, such as virtual screening, drug repurposing and identification of potential drug side …
A review on applications of computational methods in drug screening and design
Drug development is one of the most significant processes in the pharmaceutical industry.
Various computational methods have dramatically reduced the time and cost of drug …
Various computational methods have dramatically reduced the time and cost of drug …
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 …
Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions
Neurological disorders significantly outnumber diseases in other therapeutic areas.
However, developing drugs for central nervous system (CNS) disorders remains the most …
However, developing drugs for central nervous system (CNS) disorders remains the most …
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 …
DeepDTA: deep drug–target binding affinity prediction
Motivation The identification of novel drug–target (DT) interactions is a substantial part of the
drug discovery process. Most of the computational methods that have been proposed to …
drug discovery process. Most of the computational methods that have been proposed to …
iGRLDTI: an improved graph representation learning method for predicting drug–target interactions over heterogeneous biological information network
Motivation The task of predicting drug–target interactions (DTIs) plays a significant role in
facilitating the development of novel drug discovery. Compared with laboratory-based …
facilitating the development of novel drug discovery. Compared with laboratory-based …
Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences
Motivation In bioinformatics, machine learning-based methods that predict the compound–
protein interactions (CPIs) play an important role in the virtual screening for drug discovery …
protein interactions (CPIs) play an important role in the virtual screening for drug discovery …