Drug discovery is the task of applying machine learning to discover new candidate drugs.
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The atomic convolutional neural network is trained to predict the experimentally determined binding affinity of a protein-ligand complex by direct calculation of the energy associated with the complex, protein, and ligand given the crystal structure of the binding pose.
Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science.
SOTA for Drug Discovery on QM9
Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases.
#2 best model for Graph Classification on IPC-grounded
Multi-task learning (MTL) has led to successes in many applications of machine learning, from natural language processing and speech recognition to computer vision and drug discovery.
Deep generative models such as generative adversarial networks, variational autoencoders, and autoregressive models are rapidly growing in popularity for the discovery of new molecules and materials.
In this paper, we quantify this internal chemical diversity, and we raise the following challenge: can a nontrivial AI model reproduce natural chemical diversity for desired molecules?