Chemical hazard assessment requires extrapolation of information from model organisms to all species of concern. The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool was developed as a rapid, cost effective method to aid cross-species extrapolation of susceptibility to chemicals acting on specific protein targets through evaluation of protein structural similarities and differences. The greatest resolution for extrapolation of chemical susceptibility across species involves comparisons of individual amino acid residues at key positions involved in protein-chemical interactions. However, a lack of understanding of whether specific amino acid substitutions among species at key positions in proteins affect interaction with chemicals made manual interpretation of alignments time consuming and potentially inconsistent. Therefore, this study used in silico site-directed mutagenesis coupled with docking simulations of computational models for acetylcholinesterase (AChE) and ecdysone receptor (EcR) to investigate how specific amino acid substitutions impact protein-chemical interaction. This study found that computationally derived substitutions in identities of key amino acids caused no change in protein-chemical interaction if residues share the same side chain functional properties and have comparable molecular dimensions, while differences in these characteristics can change protein-chemical interaction. These findings were considered in the development of capabilities for automatically generated species-specific predictions of chemical susceptibility in SeqAPASS. These predictions for AChE and EcR were shown to agree with SeqAPASS predictions comparing the primary sequence and functional domain sequence of proteins for more than 90 % of the investigated species, but also identified dramatic species-specific differences in chemical susceptibility that align with results from standard toxicity tests. These results provide a compelling line-of-evidence for use of SeqAPASS in deriving screening level, species-specific, susceptibility predictions across broad taxonomic groups for application to human and ecological hazard assessment.