![Author ORCID: We display the ORCID iD icon alongside authors names on our website to acknowledge that the ORCiD has been authenticated when entered by the user. To view the users ORCiD record click the icon. [opens in a new tab]](https://chemrxiv.org/engage/assets/public/chemrxiv/images/logos/orcid.png)
Abstract
Proteochemometrics modeling (PCM) combines protein and ligand information to create predictive models for biological activity. It has great potential to extrapolate information across targets, enabling its application in screening drug candidates across a whole family of proteins. In this study, we present a series of PCM models for the SLC6 transporter family showing reasonable performance (Q2 values up to 0.79) when using standard validation metrics. Moreover, feature importance analysis pointed towards residue position A173 in hSERT, corresponding to G149 and G153 in hNET and hDAT, respectively, to be relevant for subtype selectivity. However, examining the impact of different data splits on model validation metrics highlights potential over-optimism when only considering target stratification splits. Furthermore, when performing leave-one-transporter-out studies, a considerable drop in performance was observed. This points towards the need for more complex technologies to exploit the potential of PCM and identify new drug candidates.
