Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Drug designing and development is an important area of research for pharmaceutical
companies and chemical scientists. However, low efficacy, off-target delivery, time …
companies and chemical scientists. However, low efficacy, off-target delivery, time …
AI in drug discovery and its clinical relevance
The COVID-19 pandemic has emphasized the need for novel drug discovery process.
However, the journey from conceptualizing a drug to its eventual implementation in clinical …
However, the journey from conceptualizing a drug to its eventual implementation in clinical …
Learning functional properties of proteins with language models
Data-centric approaches have been used to develop predictive methods for elucidating
uncharacterized properties of proteins; however, studies indicate that these methods should …
uncharacterized properties of proteins; however, studies indicate that these methods should …
Advances in de novo drug design: from conventional to machine learning methods
De novo drug design is a computational approach that generates novel molecular structures
from atomic building blocks with no a priori relationships. Conventional methods include …
from atomic building blocks with no a priori relationships. Conventional methods include …
Deep learning in virtual screening: recent applications and developments
TB Kimber, Y Chen, A Volkamer - International journal of molecular …, 2021 - mdpi.com
Drug discovery is a cost and time-intensive process that is often assisted by computational
methods, such as virtual screening, to speed up and guide the design of new compounds …
methods, such as virtual screening, to speed up and guide the design of new compounds …
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 …
Drug–target affinity prediction using graph neural network and contact maps
Computer-aided drug design uses high-performance computers to simulate the tasks in drug
design, which is a promising research area. Drug–target affinity (DTA) prediction is the most …
design, which is a promising research area. Drug–target affinity (DTA) prediction is the most …
Artificial intelligence in drug discovery: applications and techniques
Artificial intelligence (AI) has been transforming the practice of drug discovery in the past
decade. Various AI techniques have been used in many drug discovery applications, such …
decade. Various AI techniques have been used in many drug discovery applications, such …
FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction
Accurate prediction of molecular properties, such as physicochemical and bioactive
properties, as well as ADME/T (absorption, distribution, metabolism, excretion and toxicity) …
properties, as well as ADME/T (absorption, distribution, metabolism, excretion and toxicity) …
Geometric interaction graph neural network for predicting protein–ligand binding affinities from 3d structures (gign)
Predicting protein–ligand binding affinities (PLAs) is a core problem in drug discovery.
Recent advances have shown great potential in applying machine learning (ML) for PLA …
Recent advances have shown great potential in applying machine learning (ML) for PLA …