Recursive Protein Complex Modeling for AI Drug Design
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Solution Overview
Problem
The drug discovery and development pipeline is exorbitantly costly and inefficient, with a high failure rate in clinical testing phases, and there is a lack of protein-level recursive transformer neural networks for obtaining protein complexes given constituent proteins or subcomplexes, hindering effective drug and diagnostics design.
Innovation Solution
A protein-level recursive transformer neural network is employed to determine protein complexes from constituent proteins or subcomplexes, utilizing an encoder-decoder architecture with structure and sequence heads, and autoregressive residue generation to generate protein complexes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional drug discovery pipelines are used, then comprehensive testing can be performed, but the process becomes exorbitantly costly and lengthy with high failure rates
Solution Approach 1:
The patent applies preliminary action by using recursive transformer neural networks to predict protein complexes and drug-ligand interactions before entering costly clinical trials. The system performs in silico screening and prediction of binding affinities, structural compatibility, and interaction outcomes in advance, allowing researchers to prioritize only the most promising candidates for wet lab validation and clinical testing, thereby reducing both time and failure rates.
2Reliability
If more resources are invested in drug discovery, then more comprehensive testing can be done, but costs increase to over $2 billion per drug
Solution Approach 1:
The patent employs copying by creating accurate digital replicas and simulations of protein structures, protein complexes, and drug-ligand interactions using recursive transformer models. Instead of physically synthesizing and testing numerous drug candidates, the system generates virtual models and predictions of binding affinities and structural compatibility, allowing researchers to screen thousands of candidates computationally before investing in physical synthesis and testing, thereby dramatically reducing material and financial resources required.
3Measurement precision
If protein-level recursive transformer networks are implemented, then accurate protein complex prediction is achieved, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex protein complex prediction task into hierarchical sub-tasks handled by specialized transformer components. The recursive transformer architecture processes individual protein sequences to generate embeddings, then combines these through attention mechanisms to predict complex formation. This modular segmentation allows the system to manage computational complexity by breaking down the overall prediction problem into manageable sequence encoding, embedding generation, and complex prediction stages, each optimized independently.
Data Source
AI summary
Methods for determining a representation of a protein complex, given a constituent target complex of that protein complex are presented; where the constituent target complex is a single entity constituent or subcomplex of the protein complex; and wherein a protein complex is a complex of some combination of one or more of proteins, nucleic acids, metal ions, and small molecules. A recursive neural network is devised, wherein for each iteration of the recursion, a representation of the output constituent of the protein complex together with the input constituent target complex is passed into the neural network as input for the next iteration. Some embodiments of the invention include design and manufacturing of effective synthetic biologic drugs, monoclonal antibody (mAb) drug, Antibody Drug Conjugate (ADC), peptide ligand drug, and small molecule drugs (SMDs).