The accurate prediction of changes in protein stability under multiple amino acid substitutions is essential for realising true in-silico protein re-design. To this purpose, we propose improvements to state-of-the-art Deep learning (DL) protein stability prediction models, enabling first of a kind predictions for variable numbers of amino acid substitutions. By decoupling the atomic and residue scales of protein representations, using E(3)-equivariant graph neural networks (EGNN) for both Atomic Environment (AE) embedding and residue level scoring tasks. OurAE embedder was used to featurise a residue level graph, then trained to score mutant stability (∆∆G). To achieve effective training of this predictive EGNN we have leveraged the unprecedented scale of a new high-throughput protein stability experimental data-set, Mega-scale. Finally, we demonstrate the immeadiately promising results of this procedure, discuss the current shortcomings, and highlight potential future strategies