RIDE improves drug molecule scaffold design preserving 3D shape
RIDE: Reference-Anchored Inference-Time Diffusion Editing for Scaffold Hopping
Artificial IntelligenceMachine Learning
Summary
Designing new drug molecules often involves changing the core structure or scaffold while keeping important parts that interact with the body. The authors developed RIDE, a method that edits molecules by preserving their 3D shape but creating new 2D structures. This helps find distinct molecules that might work similarly but are structurally different, which is important for drug discovery. They show RIDE works better than other methods by producing more novel structures that still look alike in 3D.
What this means in practice
- •For pharmaceutical chemists: Generate novel drug core structures that maintain crucial 3D interactions with target proteins during drug design.$Commercial implications: Enables pharmaceutical companies to create innovative drug candidates with desired binding profiles, expediting drug discovery pipelines.
- •For computational chemists: Implement scaffold editing techniques that balance structural novelty with 3D similarity for molecular modeling tasks.
Authors
Ruoxi Gao, Frazier N. Baker, Trieu Nguyen, Xia Ning
Abstract
Scaffold hopping is a critical task in drug discovery, which seeks to discover new, structurally distinct molecules that share key functional groups and similar 3D shape with a reference binding ligand. Existing diffusion-based scaffold hopping methods formulate the problem as conditional generation of scaffolds given the functional groups. However, they lack a principled mechanism to jointly enforce 2D structural novelty and preserve the 3D shape of the reference ligand. Here, we introduce RIDE, a Reference-anchored Inference-time Diffusion Editing framework for scaffold hopping. RIDE recovers the reference diffusion noise trajectory conditioned on the binding pocket and functional groups, selects an optimal trajectory segment for editing via noise perturbation, and conducts a value-guided scaffold sampling to generate new scaffolds. Extensive experimental results demonstrate that, compared to baselines, RIDE consistently generates scaffolds with lower 2D similarity and higher 3D similarity to the reference, with an average improvements of 11.7% and 7.3%, respectively. Further analysis reveals that RIDE can accommodate various reward functions, and can preserve 3D similarity even when this is not explicitly included in the reward. Two case studies illustrate RIDE's ability to generate distinct scaffolds with different structures and properties, and its ability to introduce substantial 2D variation while maintaining very high 3D similarity. RIDE is publicly available at https://anonymous.4open.science/r/RIDE-C8A0.