A.1 Additional experimental results We further introduce our additional experiments in this section. In our main article, we compared our model FREED with baseline models REINVENT and MORLD. For fairer comparison of quality scores, we also performed multi-objective optimization of REINVENT and MORLD on both quality score (pharmacochemical filter score) and docking score as follows. Table 1 in the main text shows that such an implicit method is not enough to achieve nearly perfect filter scores as our model did. Also, as shown in Table 1 REINVENT showed deteriorated performance when jointly trained with filter scores, in terms of hit ratio and top 5% scores, implying that multiobjective optimization is more difficult than explicitly constrained optimization. Such a result was consistent for all three targets. The two baseline models REINVENT and MORLD that are jointly trained to maximize filter scores are noted as REINVENT w/ filter and MORLD w/ filter.

Navigating the vast chemical space of druggable compounds is a formidable challenge in drug discovery, where generative models are increasingly employed to identify viable candidates. Conditional 3D structure-based drug design (3D-SBDD) models, which take into account complex three-dimensional interactions and molecular geometries, are particularly promising. Scaffold hopping is an efficient strategy that facilitates the identification of similar active compounds by strategically modifying the core structure of molecules, effectively narrowing the wide chemical space and enhancing the discovery of drug-like products. However, the practical application of 3D-SBDD generative models is hampered by their slow processing speeds. To address this bottleneck, we introduce TurboHopp, an accelerated pocket-conditioned 3D scaffold hopping model that merges the strategic effectiveness of traditional scaffold hopping with rapid generation capabilities of consistency models. This synergy not only enhances efficiency but also significantly boosts generation speeds, achieving up to 30 times faster inference speed as well as superior generation quality compared to existing diffusion-based models, establishing TurboHopp as a powerful tool in drug discovery.

Large Language Models (LLMs) with strong abilities in natural language processing tasks have emerged and have been applied in various kinds of areas such as science, finance and software engineering.

As 4, number M 100) of FedA M = 1000performs faster gradient Model trained T1 2 {0,20,40,60,80,100}, whereT1 =0 Figure 5(a), we random performs margin20%intest model.

To show that the larger library allows our model to generate more unique molecules, we provide quality scores of our model (FREED(PE)) trained with the small library and the large, unfiltered libraryinTable2andTable3. Lastly, for 5ht1b, the scaffold and the generated molecule are docked in different binding sites. Since the generated molecule of 5ht1b is twice the size of the 5ht1b scaffold, we assume that the generated molecule could not fit in the originalbindingpocket. In this experiment, we tested our model's performance on the larger action space. We constructed a fragment library of 350 fragments and a fragment library of 1k fragments and trained our model on both libraries.

Infederated learning, itiscommon toassume thatclients arealwaysavailable to participate in training, which may not be feasible with user devices in practice.