Multicapitate Neural Networks for Joint Protein Sequence-Structure Design
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Solution Overview
Problem
Existing deep learning methods for protein and drug design fail to integrate sequence and structure learning, leading to high failure rates in drug development due to an exponentially large search space and lack of unified approaches.
Innovation Solution
A multicapitate neural network with shared weights between a sequence head and a structure head, trained via gradient descent, to jointly learn protein sequence and structure representations given a specified condition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If separate methods are used to determine sequence and structure, then the complexity of the neural network architecture is reduced, but the integration and effectiveness of protein and drug design is compromised
Solution Approach 1:
The patent combines sequence determination and structure determination into a single integrated neural network with shared weights. The sequence head and structure head both receive input from the same encoded representation and share embedding layers, creating a unified model that simultaneously learns both sequence and structure relationships rather than treating them as separate tasks.
Solution Approach 2:
The neural network body serves multiple functions by providing a shared representation that feeds into both sequence prediction and structure prediction heads. The shared weights and embeddings enable the model to universally process input data for both sequence and structure generation, making the system multi-functional rather than specialized for separate tasks.
2Reliability
If a unified neural network is used to jointly learn sequence and structure, then the effectiveness of drug design is improved, but the computational resources and training complexity increase
Solution Approach 1:
By merging sequence and structure determination into a single neural network with shared computational components, the patent reduces redundant computation. The shared weights and embeddings mean that the same computational resources process the input data for both sequence and structure predictions simultaneously, rather than requiring separate full-model computations.
Solution Approach 2:
The unified neural network is segmented into distinct functional components: an encoding layer, shared embedding layers, a sequence head, and a structure head. This segmentation allows for efficient computation by dividing the complex task into manageable parts that can be processed in parallel or sequentially, reducing overall computational burden while maintaining integration.
3Ease of manufacture
If separate sequence and structure determination methods are used, then the training process is simpler, but the ability to generalize to novel drug design cases is reduced
Solution Approach 1:
The patent merges sequence and structure learning into a unified model that trains simultaneously on both tasks. The shared weights ensure that the model learns a comprehensive representation that captures both sequence and structure relationships, enabling better generalization to novel cases where the model must infer both sequence and structure from limited input data.
Solution Approach 2:
The unified neural network incorporates feedback mechanisms where the shared representation is refined through simultaneous sequence and structure prediction tasks. The model receives feedback from both sequence loss and structure loss during training, allowing it to iteratively improve its generalization capability by learning from multiple complementary signals rather than a single task.
Data Source
AI summary
Methods and apparatus for protein and drug design using multicapitate (“two or more headed”) neural networks, wherein one head, a sequence head, is trained to generate the sequence of a protein, and another head, a structure head, is trained to generate the structure of the protein; and wherein the neural network is configured to accept a representation of a specified condition as input, and output a representation of a protein's sequence and structure. The structure head and sequence head each have their own loss functions, and the weights of the neural network body are shared, and jointly updated during training. Non-limiting examples of specified input conditions include representations of associated proteins and/or sets of properties of the desired output protein. Some embodiments of the invention include for the design and synthesis of effective peptide drug ligands, synthetic biologic antibody drugs, antibody drug conjugates, and monoclonal antibody (mAb) drugs.