Fig. 9: Best practices for the generation and validation of an FEP-enabling model for lead optimization.

In the first step (1), an ensemble of receptor-ligand structural models is built by combining multiple receptor models (including experimental structures available as well as relevant model from homology modeling or AI-based methods like AF2) with induced-fit docking on each of these starting receptor conformations. In the second step (2), each receptor-ligand complex model is assessed for its ability to retrospectively predict the SAR around the ligand of interest with RB-FEP (grey arrows). For efficiency, this can be done in a stepwise manner where a smaller, select subset of the SAR data available is used for the rapid initial assessment of all the models, and only the models showing promising accuracy at that stage are further validated on a larger dataset. If the SAR data allows, the results can be also broken down by region of ligand modification, and provide more granular insights in terms of which region of each model is predictive or not with RB-FEP (small colored squares). Oftentimes, when several models show good RB-FEP accuracy, they include variations around the same model, which can be clarified by clustering the best models based on structural similarity. In such cases, local structural refinements as well as FEP parameter optimization typically allow convergence to an optimal model for prospective FEP usage. If the available retrospective activity data is insufficient, though, it is possible that several models with significant differences show reasonable RB-FEP predictions at that stage. In such cases, the best models need to be further tested in order to discriminate those to be used prospectively from potential false positives (which will not hold prospectively, by definition). AB-FEP, as well as the extension of the dataset used for RB-FEP validation (potentially including non-binders in the same series), are typical ways to discriminate between top models.