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 …
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 …
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 …
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 …
[HTML][HTML] Chemical language models for de novo drug design: Challenges and opportunities
F Grisoni - Current Opinion in Structural Biology, 2023 - Elsevier
Generative deep learning is accelerating de novo drug design, by allowing the generation of
molecules with desired properties on demand. Chemical language models–which generate …
molecules with desired properties on demand. Chemical language models–which generate …
DeepDTAF: a deep learning method to predict protein–ligand binding affinity
Biomolecular recognition between ligand and protein plays an essential role in drug
discovery and development. However, it is extremely time and resource consuming to …
discovery and development. However, it is extremely time and resource consuming to …
Interpretable bilinear attention network with domain adaptation improves drug–target prediction
Predicting drug–target interaction is key for drug discovery. Recent deep learning-based
methods show promising performance, but two challenges remain: how to explicitly model …
methods show promising performance, but two challenges remain: how to explicitly model …
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 …
Deep drug-target binding affinity prediction with multiple attention blocks
Drug-target interaction (DTI) prediction has drawn increasing interest due to its substantial
position in the drug discovery process. Many studies have introduced computational models …
position in the drug discovery process. Many studies have introduced computational models …
FusionDTA: attention-based feature polymerizer and knowledge distillation for drug-target binding affinity prediction
W Yuan, G Chen, CYC Chen - Briefings in Bioinformatics, 2022 - academic.oup.com
The prediction of drug-target affinity (DTA) plays an increasingly important role in drug
discovery. Nowadays, lots of prediction methods focus on feature encoding of drugs and …
discovery. Nowadays, lots of prediction methods focus on feature encoding of drugs and …