Exploring ∆∆G prediction with Siamese Networks

During lead optimization, lead molecules are refined for potency via slight modifications of their chemical structure. Relative binding free energy (RBFE) methods allow comparisons of molecular potency during this optimization. We utilize a Siamese Convolutional Neural Network (CNN) to directly estimate the RBFE with higher throughput than simulation based methods. Our models show improved performance over a previously published Siamese RBFE predictor. We observe decreased performance on out-of-domain RBFE predictions.