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.
Ranked #2 on Drug Discovery on QM9
Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases.
Ranked #2 on 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.
Generative models are becoming the tools of choice for the discovery of new molecules and materials.
Deep learning has led to a paradigm shift in artificial intelligence, including web, text and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics.
Ranked #4 on Formation Energy on Materials Project
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.