The fragment linking experiment was run in triplicate and the results are shown in Fig. (See Supporting Information Fig. for all training plots). Over the course of 100 epochs, the average Glide docking score of the batch of molecules generated by Link-INVENT gradually becomes more favourable (Fig. ). The docking scores distributions of the triplicate runs are essentially identical and demonstrate reproducible experimental outcome (Fig. ). The relatively few molecules that possess a docking score of 0 do not satisfy the docking constraint and were generated towards the beginning of the Link-INVENT run at a timestep where the agent has received minimal feedback. Furthermore, some molecules proposed by Link-INVENT exhibit a more favourable docking score than the reference ligand (-15.20 kcal/mol, black dotted line in Fig. ). The majority of the remaining molecules dock similar to the reference ligand (approximately -14 kcal/mol) and demonstrates that Link-INVENT at the very least, proposes chemical ideas that can satisfy the docking constraint. Subsequently, the interplay between the agent and the DF is exemplified in Fig. . The DF encourages balance between agent exploration and exploitation by penalizing repeated sampling of identical Bemis-Murcko scaffolds. The triplicate runs yield a large number of unique scaffolds with minimal overlap, demonstrating diversity in the results and showing that replicate experiments explore different areas in chemical space (Fig. ). Next, the plausibility of generated molecules was investigated by comparing their binding poses with the reference ligand. Fig. shows the binding pose of an example top scoring molecule (based on satisfaction of the composite Scoring Function) superimposed with the reference ligand (See Supporting Information Fig. for more examples). Firstly, the proposed linker is similar to the ground-truth linker, differing only by a single atom shift of an amide bond and the presence of an additional nitrogen. It is important to note that information about the reference ligand was not available to the Link-INVENT agent during the generative process. Fusco, Brear, et al. posits that the flexibility and rigidity of the reference ligand linker is crucial its potency. The similarity in the linker proposed by Link-INVENT suggests that the docking constraint implicitly guides the agent towards 3D structural awareness, in agreement with our previous results. This is further supported by the predicted polar interactions of the generated molecule (Fig. turquoise dotted lines) being mostly identical to those of the reference ligand (Fig. yellow dotted lines) with the only exception being His160. Consequently, the structural similarity between the linkers naturally results in significant overlap of the binding poses and is exemplified in the docking score in which the generated molecule is predicted to dock more favourably than the reference ligand. Taken together, the results in this section demonstrate that Link-INVENT is able to generate plausible chemical ideas spanning diverse minima and is easily tuned for bespoke applications via the Scoring Function. but with high in vivo clearance (Fig. ). Subsequently, in a more recent work, Patel et al. applied a scaffold hopping strategy to improve the physico-chemical properties of their initial inhibitor to achieve central nervous system (CNS) penetration (Fig. ). Scaffold hopping from the pyridine core to a pyrazole core led to the discovery of a DLK inhibitor with in vivo efficacy (Fig. ). In this section, we adopt the scaffold hopping strategy devised by Patel et al. and task Link-INVENT with generated novel core ideas with a focus on improving CNS properties. A docking constraint to enforce the Cys193 hydrogen-bond interactions is applied to retain predicted potency and the following specific physico-chemical properties, adopted from Patel et al., were enforced 57 : the number of HBDs must be less than 2, the topological polar surface area (tPSA) must be less than 90 Å 2 , and the CNS MPO score must be greater than or equal to 4. The CNS MPO is an algorithm developed from analysis of CNS drugs and candidates as a predictor for CNS efficacy and encompasses six physicochemical properties (ClogP, ClogD, MW, tPSA, number of HBDs, and pKa). In the devised experiment, we do not account for all six CNS MPO properties and only enforce logP, MW, tPSA, and number of HBDs.