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) …
Artificial intelligence in drug discovery and development
KK Mak, YH Wong, MR Pichika - Drug Discovery and Evaluation: Safety …, 2023 - Springer
This chapter comprehensively explores the pivotal role of artificial intelligence (AI) in drug
discovery and development, encapsulating its potentials, methodologies, real-world …
discovery and development, encapsulating its potentials, methodologies, real-world …
Comprehensive survey of recent drug discovery using deep learning
Drug discovery based on artificial intelligence has been in the spotlight recently as it
significantly reduces the time and cost required for developing novel drugs. With the …
significantly reduces the time and cost required for developing novel drugs. With the …
AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification
In this study, we introduce an interpretable graph-based deep learning prediction model,
AttentionSiteDTI, which utilizes protein binding sites along with a self-attention mechanism …
AttentionSiteDTI, which utilizes protein binding sites along with a self-attention mechanism …
Deep learning allows genome-scale prediction of Michaelis constants from structural features
The Michaelis constant KM describes the affinity of an enzyme for a specific substrate and is
a central parameter in studies of enzyme kinetics and cellular physiology. As measurements …
a central parameter in studies of enzyme kinetics and cellular physiology. As measurements …
PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions
Recently, deep neural network (DNN)-based drug–target interaction (DTI) models were
highlighted for their high accuracy with affordable computational costs. Yet, the models' …
highlighted for their high accuracy with affordable computational costs. Yet, the models' …
Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning
Drug-target interaction (DTI) prediction plays a crucial role in drug repositioning and virtual
drug screening. Most DTI prediction methods cast the problem as a binary classification task …
drug screening. Most DTI prediction methods cast the problem as a binary classification task …
Deep learning tools for advancing drug discovery and development
A few decades ago, drug discovery and development were limited to a bunch of medicinal
chemists working in a lab with enormous amount of testing, validations, and synthetic …
chemists working in a lab with enormous amount of testing, validations, and synthetic …
MDeePred: novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery
AS Rifaioglu, R Cetin Atalay… - …, 2021 - academic.oup.com
Motivation Identification of interactions between bioactive small molecules and target
proteins is crucial for novel drug discovery, drug repurposing and uncovering off-target …
proteins is crucial for novel drug discovery, drug repurposing and uncovering off-target …
Artificial intelligence in aptamer–target binding prediction
Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA)
molecules that can interact with corresponding targets with high affinity. Owing to their …
molecules that can interact with corresponding targets with high affinity. Owing to their …