Virtual screening

Ligand (biochemistry)

Test set

Receptor

Organic chemistry

Stereochemistry

Protein ligand

Machine learning

Artificial intelligence

Set (abstract data type)

Chemistry

Programming language

Affinities

Computer science

Drug discovery

Binding affinities

Artificial neural network

Biochemistry

Convolutional neural network

Journal of Chemical Information and Modeling

Accurately predicting protein–ligand binding affinities is an important problem in computational chemistry since it can substantially accelerate drug discovery for virtual screening and lead optimization. We propose here a fast machine-learning approach for predicting binding affinities using state-of-the-art 3D-convolutional neural networks and compare this approach to other machine-learning and scoring methods using several diverse data sets. The results for the standard PDBbind (v.2016) core test-set are state-of-the-art with a Pearson's correlation coefficient of 0.82 and a RMSE of 1.27 in pK units between experimental and predicted affinity, but accuracy is still very sensitive to the specific protein used. KDEEP is made available via PlayMolecule.org for users to test easily their own protein–ligand complexes, with each prediction taking a fraction of a second. We believe that the speed, performance, and ease of use of KDEEP makes it already an attractive scoring function for modern computational chemistry pipelines.

<i>K</i><sub>DEEP</sub>: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks

KDEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks

K_DEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks