Drug discovery is the task of applying machine learning to discover new candidate drugs.
( Image credit: A Turing Test for Molecular Generators )
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Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science.
Ranked #4 on Graph Regression on ZINC-500k
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
Generative models are becoming a tool of choice for exploring the molecular space.
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.
Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery.
The results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction.