Official Review · NEURIPS 2024

This paper presents TurboHopp, an accelerated pocket-conditioned 3D scaffold hopping model designed to enhance the efficiency and speed of drug discovery. It addresses the slow processing speeds of 3D-SBDD generative models by offering up to 30 times faster inference speed while maintaining or improving on key metrics like drug-likeness, synthesizability, connectivity, and binding affinity. Additionally, it incorporates reinforcement learning to further optimize molecule designs, demonstrating its potential in various drug discovery scenarios. Accelerated Generation: TurboHopp's inference speed is 5-30 times faster than that of DDPM-based models, greatly improving the efficiency of drug discovery. Combination with Reinforcement Learning: By leveraging the fast inference speed of consistency models, TurboHopp applies reinforcement learning to 3D-SBDD-DMs, enabling fine-tuning of generative models based on specific objectives, such as improving binding affinity or reducing steric clashes, for more refined molecule design. The consistency model and reinforcement learning are well-established techniques, each with a robust body of research. The integration of reinforcement learning into diffusion models has been explored in the literature. In this paper, the authors concatenate these two methodologies for the purpose of scaffold hopping, without explicitly detailing any novel strategies or innovations in their implementations. The empirical comparison can be done in a more thoroughly by comparing with other latest state-of-the-art drug discovery algorithms. 1. Could the authors provide a more detailed analysis of the trade-off between inference speed and the quality of the generated molecules? 2. How does the novelty exceed 1 at Table 1? 3. The part that reduces time is mainly due to the consistency model. How did the author transfer the consistency model? What improvements have been made after the transfer? 4. What are the advantages of this model over SBDD models, such as Targetdiff [1] and Decomdiff [2]? 5. In terms of performance comparison, the enhancement provided by reinforcement learning to the model is significant. Can it be understood that surpassing DiffHopp primarily relies on reinforcement learning? Is it reasonable to use evaluation metrics directly for optimization? Can you conduct an ablation study focused solely on the integration of reinforcement learning? [1] Jiaqi Guan, Wesley Wei Qian, Xingang Peng, Yufeng Su, Jian Peng, and Jianzhu Ma. 3d equivariant diffusion for target-aware molecule generation and affinity prediction. arXiv, 2023. [2] Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, and Quanquan Gu. Decompdiff: diffusion models with decomposed priors for structure-based drug design. arXiv, 2024.