Fig. 4: AI-Bind pipeline: VecNet Performance and Validation.
From: Improving the generalizability of protein-ligand binding predictions with AI-Bind
a AI-Bind pipeline generates embeddings for ligands (drugs and natural compounds) and proteins using unsupervised pre-training. These embeddings are used to train the deep models. Top predictions are validated using docking simulations and are used as potential binders to test experimentally. b AI-Bind’s VecNet architecture uses Mol2vec and ProtVec for generating the node embeddings. VecNet is trained in a 5-fold cross-validation set-up. Averaged prediction over the 5 folds is used as the final output of VecNet. c–f The average performance for a 5-fold cross-validation of VecNet, DeepPurpose, and Configuration Model (dots represent the performance of each fold, bar height corresponds to the mean, n = 5). All the models perform similarly in case of predicting binding for unseen edges (transductive) and unseen targets (semi-inductive). The advantage of using deep learning and unsupervised pre-training is observed in the case of unseen nodes (inductive test). AI-Bind’s VecNet is the best performing model across all the scenarios. Additionally, we observe a similar performance of VecNet for both drugs and natural compounds. Source data are provided as a Source Data file.
