Drug discovery is the task of applying machine learning to discover new candidate drugs.
( Image credit: Neural Graph Fingerprints )
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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.
#2 best model 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.
We evaluate our model on multiple tasks ranging from molecular generation to optimization.
Finally, since all of the simulation code is written in Python, researchers can have unprecedented flexibility in setting up experiments without having to edit any low-level C++ or CUDA code.