[HTML][HTML] m6A modification: recent advances, anticancer targeted drug discovery and beyond
LJ Deng, WQ Deng, SR Fan, MF Chen, M Qi, WY Lyu… - Molecular cancer, 2022 - Springer
Abstract Abnormal N6-methyladenosine (m6A) modification is closely associated with the
occurrence, development, progression and prognosis of cancer, and aberrant m6A …
occurrence, development, progression and prognosis of cancer, and aberrant m6A …
[HTML][HTML] Artificial intelligence in pharmaceutical technology and drug delivery design
Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic
knowledge and provides expedited solutions to complex challenges. Remarkable …
knowledge and provides expedited solutions to complex challenges. Remarkable …
[HTML][HTML] 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 …
Use of molecular docking computational tools in drug discovery
F Stanzione, I Giangreco, JC Cole - Progress in medicinal chemistry, 2021 - Elsevier
Molecular docking has become an important component of the drug discovery process.
Since first being developed in the 1980s, advancements in the power of computer hardware …
Since first being developed in the 1980s, advancements in the power of computer hardware …
[HTML][HTML] The role of machine learning in clinical research: transforming the future of evidence generation
Background Interest in the application of machine learning (ML) to the design, conduct, and
analysis of clinical trials has grown, but the evidence base for such applications has not …
analysis of clinical trials has grown, but the evidence base for such applications has not …
[HTML][HTML] 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 …
[HTML][HTML] Towards the sustainable discovery and development of new antibiotics
An ever-increasing demand for novel antimicrobials to treat life-threatening infections
caused by the global spread of multidrug-resistant bacterial pathogens stands in stark …
caused by the global spread of multidrug-resistant bacterial pathogens stands in stark …
Contrastive learning in protein language space predicts interactions between drugs and protein targets
Sequence-based prediction of drug–target interactions has the potential to accelerate drug
discovery by complementing experimental screens. Such computational prediction needs to …
discovery by complementing experimental screens. Such computational prediction needs 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 …
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 …