AI Biomolecule Design Pipeline for Binding Affinity Prediction
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Solution Overview
Problem
Current software tools and computational methods, including AI and machine learning, have limited success in accurately predicting the properties and behavior of complex biologics due to their intricate structures, hindering the drug discovery and development process for biologics, which are complex biomolecules.
Innovation Solution
The development of AI-powered systems and methods that utilize a pipeline comprising a scaffold docker, interface designer, and binding affinity predictor to design and test custom biologic molecules in silico, employing machine learning algorithms to predict performance scores and optimize structural features for binding to specific targets, such as proteins or peptides, by evaluating candidate peptide backbones and amino acid sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If AI and machine learning are applied to predict biologic properties, then the cost and time of preclinical pipeline are reduced, but the prediction accuracy for complex biologics remains insufficient
Solution Approach 1:
The system segments the biologic molecule into scaffold and interface regions, applying different computational approaches to each. The scaffold docker module handles backbone positioning while the interface designer module focuses on amino acid sequence optimization, allowing specialized treatment of each structural component to improve overall prediction accuracy
Solution Approach 2:
The system applies local quality by treating different regions of the biologic molecule with different levels of computational detail. The interface regions, which are critical for binding, receive focused attention through the interface designer module that evaluates specific amino acid sequences, while other regions use broader docking approaches
2Reliability
If comprehensive in vitro and in vivo testing is performed, then the reliability of drug candidates is improved, but the time and capital required are enormous
Solution Approach 1:
The system performs preliminary computational screening and evaluation of biologic candidates before in vitro and in vivo testing. The AI-powered modules assess binding affinity, structural compatibility, and potential efficacy in silico, filtering out weak candidates early in the process to reduce the number of molecules that require extensive wet lab testing
Solution Approach 2:
The system creates computational models and virtual replicas of biologic molecules and their interactions with targets. These digital twins allow extensive virtual testing and evaluation without requiring physical samples, reducing the need for repeated in vitro experiments while maintaining assessment quality
Data Source
AI summary
Described herein are systems and methods for designing and testing custom biologic molecules in silico which are useful, for example, for the treatment, prevention, and diagnosis of disease. In particular, in certain embodiments, the biomolecule engineering technologies described herein employ artificial intelligence (AI) software modules to accurately predict performance of candidate biomolecules and/or portions thereof with respect to particular design criteria. In certain embodiments, the AI-powered modules described herein determine performance scores with respect to design criteria such as binding to a particular target. AI-computed performance scores may, for example, be used as objective functions for computer implemented optimization routines that efficiently search a landscape of potential protein backbone orientations and binding interface amino-acid sequences. By virtue of their modular design, AI-powered scoring modules can be used separately, or in combination, such as in a pipeline approach where different structural features of a custom biologic are optimized in succession.