racy and minimises computing expense for a given RBFE protocol is not trivial (figure ). Such tasks have historically been carried out manually by practitioners relying on expertise in a specific RBFE implementation and intuition to select an efficient network. However, with increased adoption of RBFE and a push for routine applications to large datasets such an approach is increasingly impractical. As a consequence it is common practice to generate star-shaped networks (where all ligands are perturbed to a single reference ligand) for large ligand series (n > 50). Although this style of network generation is attractive because of its simplicity, little research has been done to investigate the impact it has on RBFE accuracy. Lead Optimization Mapper (LOMAP ) is the primary programmatic approach to RBFE network generation and is used in diverse RBFE software implementations including Flare. The LOMAP approach is critically based on LOMAP-Score which is a model metric for the reliability or statistical fluctuation (SF) of a given RBFE perturbation. SF is a measure of precision, i.e. whether it is possible to get a converged estimate with reasonable computing effort. The LOMAP algorithm in its current form relies on expert knowledge in the form of rules that influence the LOMAP-Score. For example, within the LOMAP-Score algorithm a perturbation between a pair of molecules involving removal of a sulfonamide moiety would be penalised heavily as the SF of this perturbation in the context of other molecules has been found to be high during testing of the RBFE software in question. Conversely, a perturbation involving replacement of a hydrogen by a fluorine on an aromatic ring would result in relatively high LOMAP-Score as SFs for this class of perturbation have been found to be low during testing. Because the collection of perturbations that would ever be performed in RBFE is suffi-ciently large to prohibit rule generation for all of them, LOMAP-Score models SF imperfectly, resulting in sub-optimal RBFE network design. Additionally, the set of rules in LOMAP-Score has been fine-tuned for years by RBFE experts in order to make it perform acceptably for specific implementations; this has decreased transferability of LOMAP-Score between diverse RBFE implementations. Examples of these additions to LOMAP-Score rules can be found in the original LOMAP repository commit history, e.g. commit e7026e671d7a410e220dc110ad79a0d661b30b8b (). In practice and in an effort to deal with these shortcomings retrospective RBFE benchmarking studies often features networks that have been adjusted manually using a LOMAP generated network as a starting point. In almost all cases there is an opacity as to how these networks are augmented, and it is likely that additional edges are frequently added iteratively upon examination of the initial RBFE campaign's accuracy versus experimental measures. Although it can be argued that the augmented RBFE network is a better representation of the specific RBFE implementation's predictiveness, this practice decreases comparability between implementation as augmentation is highly dependent on human expertise. Additionally, as not all RBFE practitioners hold expert knowledge for network augmentation, this practice delivers an overstated picture of the true performance of the RBFE implementation in question when applied prospectively. This highlights the need for an objective approach in RBFE network generation that is not based on expert knowledge. More recently, data-driven approaches based on optimal design that offer a theoretically more objective approach have been proposed . Although promising alternatives to LOMAP, these algorithms are still in active development. Notably, NetBFE uses an iterative exploration of congeneric series using knowledge of SF gained incrementally by processing specific edges in the RBFE network; an initial estimation of SF is thus pivotal in this approach. However, a robust SF predictor is currently absent in the field of RBFE, forcing some approaches to revert back to simpler metrics such as molecular similarity. Additionally, novel machine learning (ML) techniques of describing RBFE perturbations have been proposed in the form of siamese neural networks. The current work proposes a data-driven RBFE network generator as an alternative to expert-driven approaches. To accomplish this, a transfer learning ML framework was designed that allows predictions of SF for molecular perturbations typically handled in RBFE. Using all predicted SFs for a given congeneric series, a data-driven RBFE network can be generated. The approach was implemented in LOMAP to generate networks using predicted SFs as input metric instead of the default LOMAP-Score. This work presents several concepts novel to the field of RBFE network generation. RBFE-Space, a transferable training domain that is composed of a large number of RBFE perturbations (n ∼4000) was created for this work and has been made publicly available to further drive ML research in the field of RBFE. The SF predictor leverages a novel siamese neural network architecture using graph neural network (GNN) legs. The ML predictor is shown to predict SFs more accurately than the expert-driven LOMAP-Score. Finally, a fully-connected network of the TYK2 RBFE benchmarking series was simulated; network analysis on this dataset has revealed several key learning points for RBFE network generation. The prototype data-driven RBFE network generator already performs comparatively to state-of-the-art network generators, is transferable between RBFE implementations and can be objectively improved by training set expansion.