Connecting chemistry and biology through molecular descriptors
A Fernández-Torras, A Comajuncosa-Creus… - Current Opinion in …, 2022 - Elsevier
Through the representation of small molecule structures as numerical descriptors and the
exploitation of the similarity principle, chemoinformatics has made paramount contributions …
exploitation of the similarity principle, chemoinformatics has made paramount contributions …
Advancing targeted protein degradation via multiomics profiling and artificial intelligence
M Duran-Frigola, M Cigler… - Journal of the American …, 2023 - ACS Publications
Only around 20% of the human proteome is considered to be druggable with small-molecule
antagonists. This leaves some of the most compelling therapeutic targets outside the reach …
antagonists. This leaves some of the most compelling therapeutic targets outside the reach …
MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction
Predicting drug–target affinity (DTA) is beneficial for accelerating drug discovery. Graph
neural networks (GNNs) have been widely used in DTA prediction. However, existing …
neural networks (GNNs) have been widely used in DTA prediction. However, existing …
Sequence-based prediction of protein binding regions and drug–target interactions
Identifying drug–target interactions (DTIs) is important for drug discovery. However,
searching all drug–target spaces poses a major bottleneck. Therefore, recently many deep …
searching all drug–target spaces poses a major bottleneck. Therefore, recently many deep …
Chemical representation learning for toxicity prediction
Undesired toxicity is a major hindrance to drug discovery and largely responsible for high
attrition rates in early stages. This calls for new, reliable, and interpretable molecular …
attrition rates in early stages. This calls for new, reliable, and interpretable molecular …
AI for targeted polypharmacology: The next frontier in drug discovery
In drug discovery, targeted polypharmacology, ie, targeting multiple molecular targets with a
single drug, is redefining therapeutic design to address complex diseases. Pre-selected …
single drug, is redefining therapeutic design to address complex diseases. Pre-selected …
Active site sequence representations of human kinases outperform full sequence representations for affinity prediction and inhibitor generation: 3D effects in a 1D …
Recent advances in deep learning have enabled the development of large-scale multimodal
models for virtual screening and de novo molecular design. The human kinome with its …
models for virtual screening and de novo molecular design. The human kinome with its …
Comprehensive detection and characterization of human druggable pockets through binding site descriptors
Druggable pockets are protein regions that have the ability to bind organic small molecules,
and their characterization is essential in target-based drug discovery. However, deriving …
and their characterization is essential in target-based drug discovery. However, deriving …
Applications of artificial intelligence in drug design: opportunities and challenges
Artificial intelligence (AI) has undergone rapid development in recent years and has been
successfully applied to real-world problems such as drug design. In this chapter, we review …
successfully applied to real-world problems such as drug design. In this chapter, we review …
[HTML][HTML] TAG-DTA: Binding-region-guided strategy to predict drug-target affinity using transformers
The proper assessment of target-specific compound selectivity is paramount in the drug
discovery context, promoting the identification of drug-target interactions (DTIs) and the …
discovery context, promoting the identification of drug-target interactions (DTIs) and the …