Automated Reaction Condition Optimization for Synthon Conversion
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Solution Overview
Problem
Pharmaceutical companies face significant challenges in drug discovery due to the high cost and time required for manually testing large compound libraries, and existing automation technologies are inadequate for complex, multi-instrument workflows with dynamically changing parameters, necessitating manual customization for each iteration.
Innovation Solution
The use of automated reaction devices combined with machine learning models and workflows to facilitate the dynamic performance of molecular reactions, enabling automation of compound development, synthesis, and screening, allowing for the generation, optimization, and selection of molecular reaction conditions, synthons, and products, and enabling direct communication between software for automatic re-configuration of worklists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual lab processes are used for compound screening, then flexibility and adaptability are maintained, but cost and time consumption increase significantly
Solution Approach 1:
The patent replaces manual mechanical lab operations with an automated liquid handling system that uses robotic mechanisms to perform compound dispensing, plate handling, and reaction setup. This substitution of mechanical manual operations with automated robotic systems directly increases screening throughput while reducing the time required for compound testing.
Solution Approach 2:
The system incorporates self-service features where the automated platform can autonomously perform multiple operations including compound dispensing, reaction monitoring, and data analysis without continuous human intervention. The system automatically optimizes experimental parameters and executes workflows independently, thereby increasing productivity and reducing time loss.
2Extent of automation
If traditional automation worklists are used, then simple single-process tasks can be automated, but complex multi-instrument workflows with dynamically changing parameters cannot be effectively handled
Solution Approach 1:
The patent implements dynamic worklists that can automatically adapt to changing experimental parameters and conditions. The system allows real-time modification of reaction conditions, compound concentrations, and workflow parameters based on preliminary results, enabling the automation system to handle complex multi-instrument workflows with dynamically changing parameters rather than fixed protocols.
Solution Approach 2:
The automated platform is designed with universal functionality to handle multiple instrument types and diverse experimental protocols through a single integrated system. The worklist management software can accommodate various reaction types, detection methods, and processing steps, making the system adaptable to complex workflows that require multiple instruments and dynamically adjusting parameters.
3Ease of operation
If traditional worklists require manual customization for each iteration, then current workflows can be configured, but the complexity and time required for setup increase
Solution Approach 1:
The system includes pre-configured worklists and standardized protocols that can be loaded and executed automatically without manual customization for each iteration. Common experimental workflows are prepared in advance with optimized parameters, allowing researchers to skip the complex setup process and directly execute standardized protocols, thereby reducing configuration complexity while maintaining operational ease.
Solution Approach 2:
The platform enables automatic parameter optimization where the system can automatically adjust experimental parameters such as compound concentrations, reaction times, and temperatures based on predefined criteria or machine learning models. This automatic parameter modification eliminates the need for manual reconfiguration of each workflow iteration, reducing setup complexity while maintaining ease of operation through automated adaptation.
Data Source
AI summary
Systems and methods for improving molecular reaction conversion values for a set of synthons are provided. An initial conversion value for the synthons is obtained for an initial reaction instance that transforms the synthons into compounds under initial reaction conditions using an automated device. When the initial conversion value fails to satisfy a criterion, the synthons are optimized by performing test reaction instances using the synthons, each test instance comprising a corresponding set of normalized conditions. A test conversion value is determined for each test instance. Each test instance having a test conversion value that satisfies the criterion is selected. Systems and methods for selecting synthon sets for optimization of a molecular reaction are also provided. Further provided are systems and methods for determining synthons having target conversion values when transformed by a molecular reaction. Also provided are systems and methods for improving conversion values using multistep molecular reactions.