GraphscoreDTA: optimized graph neural network for protein–ligand binding affinity prediction
Motivation Computational approaches for identifying the protein–ligand binding affinity can
greatly facilitate drug discovery and development. At present, many deep learning-based …
greatly facilitate drug discovery and development. At present, many deep learning-based …
Recent applications of artificial intelligence in RNA-targeted small molecule drug discovery
EC Morishita, S Nakamura - Expert Opinion on Drug Discovery, 2024 - Taylor & Francis
ABSTRACT Introduction Targeting RNAs with small molecules offers an alternative to the
conventional protein-targeted drug discovery and can potentially address unmet and …
conventional protein-targeted drug discovery and can potentially address unmet and …
RNA-targeted small-molecule drug discoveries: a machine-learning perspective
H Xiao, X Yang, Y Zhang, Z Zhang, G Zhang… - RNA biology, 2023 - Taylor & Francis
In the past two decades, machine learning (ML) has been extensively adopted in protein-
targeted small molecule (SM) discovery. Once trained, ML models could exert their …
targeted small molecule (SM) discovery. Once trained, ML models could exert their …
DeepSTF: predicting transcription factor binding sites by interpretable deep neural networks combining sequence and shape
P Ding, Y Wang, X Zhang, X Gao, G Liu… - Briefings in …, 2023 - academic.oup.com
Precise targeting of transcription factor binding sites (TFBSs) is essential to comprehending
transcriptional regulatory processes and investigating cellular function. Although several …
transcriptional regulatory processes and investigating cellular function. Although several …
Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN
The rational targeting of RNA with small molecules is hampered by our still limited
understanding of RNA structural and dynamic properties. Most in silico tools for binding site …
understanding of RNA structural and dynamic properties. Most in silico tools for binding site …
RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning method
Small molecule drugs can be used to target nucleic acids (NA) to regulate biological
processes. Computational modeling methods, such as molecular docking or scoring …
processes. Computational modeling methods, such as molecular docking or scoring …
DeepRSMA: a cross-fusion-based deep learning method for RNA–small molecule binding affinity prediction
Motivation RNA is implicated in numerous aberrant cellular functions and disease
progressions, highlighting the crucial importance of RNA-targeted drugs. To accelerate the …
progressions, highlighting the crucial importance of RNA-targeted drugs. To accelerate the …
Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation
Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute
98–99% of the human genome. Non-coding RNAs encompass diverse functional classes …
98–99% of the human genome. Non-coding RNAs encompass diverse functional classes …
An interpretable deep learning model predicts RNA–small molecule binding sites
The intricate interplay between RNA molecules and small ligands plays a pivotal role in
regulating biological processes, underscoring the necessity for accurate prediction models …
regulating biological processes, underscoring the necessity for accurate prediction models …
Molecular Sharing and Molecular-Specific Representations for Multimodal Molecular Property Prediction
X Tian, S Zhang, Y Su, W Huang, Y Zhang, X Ma… - Applied Soft …, 2024 - Elsevier
Molecular property prediction plays a crucial role in drug discovery and development.
However, traditional experimental measurements and Quantitative Structure-Activity …
However, traditional experimental measurements and Quantitative Structure-Activity …