LSF facilitates the direct substitution of C-H bonds by other functional groups or building blocks in a single step, eliminating the need for pre-functionalization of complex molecules . Consequently, this approach can offer higher efficiency in diversifying hit and lead structures compared to the traditional multi-step syn-thesis starting from basic building blocks . Among the available LSF methodologies, Minisci-type C-H alkylations are of particular interest as they leverage readily available carboxylic acids to incorporate alkyl fragments into heterocyclic cores , such as the widely used pyridine motif in medicinal chemistry. In addition, the use of carboxylic acid building blocks with a high content of carbon sp 3 centers can improve leadand drug-like characteristics, activity and selectivity profiles, as well as the physicochemical molecular properties of candidate compounds . However, due to the structural intricacies and the number of similar C-H bonds in a molecule, the transformation of pharmaceuticals and advanced building blocks using LSF reactions remains challenging . HTE can help address these issues by enabling the rapid identification and optimization of reaction conditions with minimal material consumption . The curation of the generated HTE data based on FAIR principles (findable, accessible, interoperable, and reusable) can aid in creating high-quality data sets suitable for computational analyses and machine learning applications . We previously reported the successful in silico coupling performance prediction of advanced heterocyclic building blocks with diverse sp 3 rich carboxylic acids using Minisci-type reactions leveraging graph neural networks (GNNs) trained on HTE Figure : Hit optimization workflow. A set of 125 MAGL-inhibiting "hit" molecules (seed structures) containing N -heterocycles are combined with 211 commercially available carboxylic acids to generate 26,375 in silico compounds. These virtual compounds are passed through a series of filters to identify potent, synthetically accessible molecules with favorable physicochemical properties ("machine learning funnel"). Multi-template docking and a machine learning-based potency scoring function are used to identify potent compounds. Synthetic accessibility is predicted using graph neural networks (GNNs) trained on a novel reaction data set encompassing 13,490 Minisci-type alkylation reactions. Physicochemical properties are determined using various, readily available machine learning-based predictions. Consequently, from 212 potentially suitable compounds, 34 molecule-acid combinations are manually selected and screened using miniaturized high-throughput experimentation (HTE) to identify favorable alkylation reaction conditions. Based on building block availability, up-scaling of a selected number of reactions successfully delivered 14 compounds with improved profiles. data . GNNs enable learning on two-dimensional (2D) and three-dimensional (3D) molecular models . They were utilized in predicting reaction outcomes and yields and regioselectivity, such as for borylation and alkylation reactions, with some models integrating quantum chemical description to enhance accuracy . Recent studies have shown how trained GNNs can be used for condition optimization in crosscoupling reactions , and reaction outcome prediction for three-component reactions .