Predicting Protein-Ligand Docking Structure with Graph Neural Network

Abstract

Modern day drug discovery is extremely expensive and time consuming. Although computational approaches help accelerate and decrease the cost of drug discovery, existing computational software packages for docking-based drug discovery suffer from both low accuracy and high latency. A few recent machine learning-based approaches have been proposed for virtual screening by improving the ability to evaluate protein-ligand binding affinity, but such methods rely heavily on conventional docking software to sample docking poses, which results in excessive execution latencies. Here, we propose and evaluate a novel graph neural network (GNN)-based framework, MedusaGraph, which includes both pose-prediction (sampling) and pose-selection (scoring) models. Unlike the previous machine learning-centric studies, MedusaGraph generates the docking poses directly and achieves from 10 to 100 times speedup compared to state-of-the-art approaches, while having a slightly better docking accuracy.

Figure 1.

(a) Flow of the pose-prediction GNN model. We first run MedusaDock on a protein–ligand complex to achieve a candidate docking pose. Then, the pose-prediction model calculates the movement of each flexible atom in the complex. Flexible atoms include all the ligand atoms and the receptor atoms that are near the receptor surface. Finally, we obtain the final docking pose based on the movement prediction. The pose-prediction GNN model contains several TransformerConv layers. (b) Multistep pose-prediction. An atom moves from the initial location to the final location step-by-step. (c) Flow of the pose-selection GNN model. The final poses generated by the pose-prediction model travel through three TransformerConv layers and three fully connected layers. The final output is the Yes/No neuron indicating the docking probability.

Figure 2.

Example of generating the docking pose with MedusaGraph and other ML-based approach.

Figure 3.

Average RMSD of the output poses for each approach. For the normalized latency, we set the execution time of one running of MedusaDock as 1 and normalized all other approaches. We randomly split the data set into a training set and a testing set 10 times to perform the cross-validation, and the error bar indicates the min/max RMSD value of each cross.

Figure 4.

Histograms of RMSDs of all poses for initial docking poses generated by MedusaDock: the final docking poses generated by the three-step pose-prediction model and the poses selected by the pose-selection model.

Figure 5.

We show the average RMSD of the initial poses and final docking poses for the complexes with different properties. We also calculate the good pose (with RMSD less than 2.5 Å) rates in initial docking poses and final docking poses. (a) Results of the complexes with a different number of atoms. (b) Results of the complexes with a different number of rotatable bonds.