Drug discovery is the task of applying machine learning to discover new candidate drugs.
|Trend||Dataset||Best Method||Paper title||Paper||Code||Compare|
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.
SOTA for Graph Classification on IPC-lifted
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.
De novo design seeks to generate molecules with required property profiles by virtual design-make-test cycles.
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?