Artificial intelligence for drug discovery: Resources, methods, and applications
W Chen, X Liu, S Zhang, S Chen - Molecular Therapy-Nucleic Acids, 2023 - cell.com
Conventional wet laboratory testing, validations, and synthetic procedures are costly and
time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques …
time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques …
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 …
Pre-training molecular graph representation with 3d geometry
Molecular graph representation learning is a fundamental problem in modern drug and
material discovery. Molecular graphs are typically modeled by their 2D topological …
material discovery. Molecular graphs are typically modeled by their 2D topological …
[HTML][HTML] Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model
The infection of a novel coronavirus found in Wuhan of China (SARS-CoV-2) is rapidly
spreading, and the incidence rate is increasing worldwide. Due to the lack of effective …
spreading, and the incidence rate is increasing worldwide. Due to the lack of effective …
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 …
GraphDTA: predicting drug–target binding affinity with graph neural networks
The development of new drugs is costly, time consuming and often accompanied with safety
issues. Drug repurposing can avoid the expensive and lengthy process of drug development …
issues. Drug repurposing can avoid the expensive and lengthy process of drug development …
DeepPurpose: a deep learning library for drug–target interaction prediction
Accurate prediction of drug–target interactions (DTI) is crucial for drug discovery. Recently,
deep learning (DL) models for show promising performance for DTI prediction. However …
deep learning (DL) models for show promising performance for DTI prediction. However …
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 …
MolTrans: molecular interaction transformer for drug–target interaction prediction
Motivation Drug–target interaction (DTI) prediction is a foundational task for in-silico drug
discovery, which is costly and time-consuming due to the need of experimental search over …
discovery, which is costly and time-consuming due to the need of experimental search over …
Counting on natural products for drug design
Natural products and their molecular frameworks have a long tradition as valuable starting
points for medicinal chemistry and drug discovery. Recently, there has been a revitalization …
points for medicinal chemistry and drug discovery. Recently, there has been a revitalization …