Protein Generation With 3D Layout Control via Cross-Attention
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Solution Overview
Problem
Conventional machine learning models for generating proteins lack user control over the three-dimensional spatial layouts, such as the locations of alpha helices and beta sheets, leading to proteins that may not exhibit desired properties.
Innovation Solution
A computer-implemented method using a trained machine learning model applies cross-attention between tokens associated with a 3D representation, such as ellipsoids, to generate proteins, allowing users to control the spatial layout through sketches or statistical models, and iteratively integrates a neural network-defined vector field to conform to the specified layout.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional machine learning models generate proteins based on automatically learned patterns, then the generation process is automated and efficient, but users cannot control the 3D spatial layouts of the generated proteins
Solution Approach 1:
The patent introduces 3D spatial layout representations as an intermediary between user intent and the protein generation process. These representations serve as a mediating layer that allows users to specify desired spatial configurations without directly manipulating the complex protein generation mechanics, thus maintaining automation while enabling control.
Solution Approach 2:
The system performs preliminary action by allowing users to define 3D spatial layouts before the actual protein generation occurs. This pre-specification of spatial constraints enables the subsequent automated generation process to produce proteins that adhere to desired structural properties from the outset.
2Productivity
If conventional machine learning models generate proteins without user control, then the generation process is simple and fast, but the generated proteins may lack desired properties
Solution Approach 1:
The system implements feedback by using the specified 3D spatial layouts as constraints that guide and evaluate the protein generation process. The generated proteins are assessed against these spatial requirements, and the generation process is adjusted accordingly to ensure desired properties are achieved while maintaining efficiency.
Solution Approach 2:
The patent changes key parameters by introducing 3D spatial layout specifications as additional input parameters to the generation process. This allows the system to optimize both speed and reliability by controlling structural parameters directly rather than relying solely on learned patterns.
3Manufacturing precision
If users specify detailed 3D spatial layouts, then control over protein structure is improved, but the complexity of the generation process increases
Solution Approach 1:
The patent applies segmentation by breaking down the complex protein generation task into manageable components: users specify high-level 3D spatial layouts rather than atomic-level details. This segmentation allows for precise structural control while keeping the interface and process complexity at a manageable level.
Data Source
AI summary
The disclosed method for generating proteins includes generating, using a trained machine learning model, a first protein based on a three-dimensional (3D) representation of a spatial layout for the first protein, where generating the first protein comprises applying cross-attention between one or more first tokens associated with the 3D representation and one or more second tokens associated with a second protein.