Data-Error Scaling in Machine Learning on Natural Discrete Combinatorial Mutation-prone Sets: Case Studies on Peptides and Small Molecules

arXiv:2405.05167v1 Announce Type: cross
Abstract: We investigate trends in the data-error scaling behavior of machine learning (ML) models trained on discrete combinatorial spaces that are prone-to-mutation, such as proteins or organic small molecules. We trained and evaluated kernel ridge regression machines using variable amounts of computationally generated training data. Our synthetic datasets comprise i) two na\"ive functions based on many-body theory; ii) binding energy estimates between a protein and a mutagenised peptide; and iii) solvation energies of two 6-heavy atom …

abstract arxiv behavior case case studies cs.lg data error kernel machine machine learning machines molecules mutation natural physics.chem-ph proteins regression ridge scaling small spaces studies trends type