AI-Enabled Automated Flow Synthesis Platform for Peptide Design
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Solution Overview
Problem
Conventional drug discovery methods are inefficient, limited in application, and often result in ineffective or dangerous therapeutics, particularly for diseases like prosthetic joint infections, due to their reliance on small design spaces and labor-intensive processes.
Innovation Solution
An artificial intelligence (AI)-enabled automated flow synthesis platform that uses machine learning models and causal inference to generate optimized synthesizing recipes for peptides, expanding the design space to include non-canonical amino acids and improving the efficiency of drug compound synthesis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional drug discovery methods are used, then the process is simple and familiar, but it is inefficient and limited in application scope
Solution Approach 1:
The patent replaces manual, mechanical drug discovery processes with an AI-enabled automated flow synthesis platform. The system uses machine learning models to predict peptide properties and automatically generates synthesizing recipes, substituting human expertise and manual experimentation with computational algorithms and automated instrumentation.
Solution Approach 2:
The system enables self-service through autonomous operation of the automated flow synthesis platform. The AI engine independently optimizes synthesizing recipes, predicts peptide characteristics, and controls the synthesis process without requiring continuous human intervention, allowing the system to serve itself in generating and testing peptide candidates.
2Adaptability or versatility
If small design spaces are used in conventional methods, then the synthesis process is manageable, but the application scope and effectiveness are limited
Solution Approach 1:
The patent expands the design space by adding new dimensions to the synthesis process. It incorporates non-canonical amino acids alongside canonical amino acids, creating a multi-dimensional design space that includes diverse peptide compositions, sequences, and structures. This dimensional expansion allows the system to explore a vastly larger space of possible peptide candidates.
Solution Approach 2:
The system dynamically adapts the design space based on AI predictions and synthesis results. The machine learning models continuously learn from experimental data and adjust the search space, allowing the system to dynamically expand into promising regions of the peptide design space while managing complexity through intelligent exploration strategies.
3Productivity
If labor-intensive processes are used, then manual control is possible, but productivity and resource efficiency deteriorate
Solution Approach 1:
The patent replaces labor-intensive manual processes with automated flow synthesis technology. The system uses robotic liquid handling, automated sample preparation, and computer-controlled instrumentation to perform synthesis operations, dramatically increasing throughput while reducing human labor requirements and associated resource consumption.
Solution Approach 2:
The automated flow synthesis platform enables continuous synthesis operations without interruption. Multiple peptides can be synthesized in parallel through continuous flow processes, maintaining constant productive action rather than batch-wise manual operations, thereby increasing overall throughput and resource utilization efficiency.
Data Source
AI summary
In one aspect, a computer-implemented automated platform configured to use an artificial intelligence (AI) engine is disclosed and includes a reaction chamber configured to synthesize a sequence, detectors configured to monitor the synthesis of the sequence in the reaction chamber, and a computing device communicatively coupled to the detectors. The computing device receives measurements from the one or more detectors, wherein the measurements comprise a spectral profile at at least one coupling of at least one amino acid in the sequence, trains, using training data comprising the measurements, machine learning models to determine a synthesizing recipe that enables the sequence to be synthesized, wherein the synthesizing recipe comprises parameters used to synthesize the sequence, and controls, using the synthesizing recipe, the synthesis of the sequence in the reaction chamber.