Decomposable Diffusion Ligand Generation Model
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Solution Overview
Problem
Current drug design methods, particularly structure-based drug design models using diffusion models, face challenges in generating ligand molecules with sufficient affinity to target proteins due to inadequate constraints on atom positions and chemical bonds, leading to potential collisions and insufficient interaction forces.
Innovation Solution
A ligand generation model based on a diffusion model that decomposes ligand molecules into scaffold and functional groups, using a decomposable diffusion (DecompDiff) model to generate atom types, positions, and chemical bonds, with a training process involving noise addition and denoising stages, and position guidance to improve affinity and avoid collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If diffusion models are used to generate ligand molecules, then the generation process can be automated and scaled, but the generated ligands may have collisions between atoms and insufficient interaction forces with target proteins
Solution Approach 1:
The patent segments the ligand molecule into scaffold and functional group components, and further divides the generation process into multiple stages: generating scaffold atoms first, then adding functional groups. This segmentation allows the model to generate structurally sound ligands while maintaining automation, as each stage can be independently controlled and optimized for chemical validity and binding affinity.
Solution Approach 2:
The patent applies preliminary action by generating the scaffold structure before adding functional groups. The scaffold generation stage creates the core molecular framework with proper geometry and bonding, while subsequent stages add functional groups that provide binding affinity. This sequential preliminary actions ensure that the base structure is chemically valid before additional components are added, preventing collisions and ensuring proper interaction forces.
2Productivity
If atom positions and chemical bonds are not adequately constrained during generation, then the generation process is simpler and faster, but the generated ligands may have collisions and insufficient interaction forces
Solution Approach 1:
The patent implements dynamics by using a diffusion model that operates in multiple time steps, allowing continuous adjustment and refinement of atom positions and chemical bonds. The model dynamically evolves the molecular structure from a noisy initial state to a refined final state, enabling both speed and precision through controlled iterative optimization rather than static generation.
Solution Approach 2:
The patent incorporates feedback mechanisms where the generated ligand structure is evaluated against chemical validity rules and binding affinity criteria. The model uses this feedback to adjust and refine the generated structure, ensuring that atom positions and chemical bonds meet structural accuracy requirements while maintaining generation speed through efficient iterative optimization.
3Device complexity
If the ligand generation model does not decompose ligands into scaffold and functional groups, then the model structure is simpler, but the generated ligands may lack rationality and completeness
Solution Approach 1:
The patent segments the ligand into scaffold and functional group components, which provides a systematic framework for generating chemically valid and rational structures. This segmentation increases model complexity only moderately while significantly improving the rationality and completeness of generated ligands, as each component can be generated and combined according to established chemical principles.
Solution Approach 2:
The patent applies local quality by treating the scaffold and functional groups with different generation strategies appropriate to their chemical roles. The scaffold generation focuses on structural validity and geometry, while functional group generation focuses on binding affinity and chemical reactivity. This differentiated approach ensures that each part of the molecule has the appropriate local properties for its function, improving overall rationality without requiring complete redesign of the entire model.
Data Source
AI summary
Embodiments of the present disclosure relate to a method and an electronic device for ligand generation. The method comprises: obtaining a trained ligand generation model, wherein the trained ligand generation model is generated based on decomposition of a ligand by modeling of atom positions, atom types, and chemical bonds; and obtaining, based on a target protein, a target ligand molecule corresponding to the target protein by using the trained ligand generation model. According to the method, the ligand generation model in the embodiments of the present disclosure considers the decomposition of the ligand and also models the chemical bonds, so that it can generate the target ligand molecule with higher affinity.