Improving ΔΔG Predictions with a Multitask Convolutional Siamese Network.

ABSTRACT: The lead optimization phase of drug discovery refines an initial hit molecule for desired properties, especially potency. Synthesis and experimental testing of the small perturbations during this refinement can be quite costly and time-consuming. Relative binding free energy (RBFE, also referred to as ΔΔG) methods allow the estimation of binding free energy changes after small changes to a ligand scaffold. Here, we propose and evaluate a Siamese convolutional neural network (CNN) for the prediction of RBFE between two bound ligands. We show that our multitask loss is able to improve on a previous state-of-the-art Siamese network for RBFE prediction via increased regularization of the latent space. The Siamese network architecture is well suited to the prediction of RBFE in compariso

SUBMITTER: McNutt AT 

PROVIDER: S-EPMC9038699 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature