Multi-Headed 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 address sequence and structure in a fully integrated manner, leading to high failure rates of new drugs in clinical trials.
Innovation Solution
A multicapitate neural network with a sequence head and a structure head that shares weights in the neural network body, allowing for joint learning of sequence and structure, given a specified condition, using a transformer architecture with encoder-decoder configurations and shared weights for parallel training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If separate methods are used to determine sequence and structure of proteins, then the complexity of the neural network architecture is reduced, but the integration and effectiveness of drug design is compromised
Solution Approach 1:
The patent merges sequence determination and structure determination into a single integrated neural network model. The network simultaneously processes sequence data and structure data through shared embedding layers and processing modules, enabling joint learning of sequence-structure relationships rather than treating them as separate tasks. This integration allows the model to capture contextual dependencies between sequence and structure that separate models would miss.
Solution Approach 2:
The neural network is designed as a universal model that performs multiple functions: sequence embedding, structure embedding, and joint sequence-structure representation learning. The shared embedding layers and processing modules serve multiple purposes, extracting features from both sequence and structure inputs while capturing their interrelationships, making the model adaptable to various drug design tasks.
2Reliability
If an integrated neural network is used to jointly learn sequence and structure, then the effectiveness of drug design is improved, but the complexity of the neural network architecture increases
Solution Approach 1:
The integrated neural network is segmented into distinct functional modules: sequence embedding layer, structure embedding layer, processing modules for joint representation learning, and output layers for sequence and structure prediction. This modular segmentation allows the complex integrated model to be constructed from manageable components, each with a specific function, making the overall architecture more tractable and easier to train despite the integration.
3Ease of manufacture
If separate methods are used for sequence and structure determination, then the training process is simpler, but the joint learning of sequence and structure is compromised
Solution Approach 1:
The training process merges sequence and structure learning into a unified optimization framework. The loss function combines sequence prediction loss and structure prediction loss, allowing simultaneous training of both tasks. The shared embedding layers and processing modules are trained end-to-end, enabling the model to learn sequence-structure relationships while being optimized for both prediction tasks together rather than sequentially.
Data Source
AI summary
Methods and apparatus for protein and drug design using neural networks with two or more output heads, 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.