Graph Neural Networks for Binding Affinity Prediction скачать в хорошем качестве

Graph Neural Networks for Binding Affinity Prediction

The topic relates to the applications of AI and bioinformatics during the early stages of drug discovery. Bio-/cheminformatics is now on the edge of a similar paradigm shift as computer vision before the deep learning model AlexNet won the 2012 ImageNet contest. Instead of selecting manually crafted features for molecules, integrative features are learned by optimization methods. Challenges: Predicting drug-target interactions is crucial for novel drug discovery, drug repurposing, and uncovering off-target effects. Experimental bioactivity screening takes significant time (1–3 years) and expense (more than 100 million USD on average per new drug-on-market) but has low efficiency. Bioassays are typically backed by computational methods, but legacy simulations fail to deliver either sufficient precision — like in the example of AutoDock Vina with modern RF Score which failed to separate active and inactive thrombin ligands — or sufficient speed — like in the example of molecular dynamics or first-principle quantum mechanics simulations. As a result, more than 90% of the proposed leads are declined (He et al., 2017). Solution: In silico methods are highly demanded since they can expedite the drug development process by systemically suggesting a new set of candidate molecules promptly, which saves time and reduces the cost of the whole process by up to 43% (DiMasi et al., 2016). Graph neural networks deliver superior accuracy for the task in a matter of milliseconds per receptor-ligand pair and extend docking capabilities by accepting structures without coordinates. More information in the related Medium post:   / graph-neural-networks-for-binding-affinity...