Dynamic AI Drug Design Workflow for Lead Molecule Optimization
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Solution Overview
Problem
Conventional drug design techniques are inefficient, inaccurate, and time-consuming, particularly in integrating in-silico, in-vitro, and in-vivo approaches, and lack clear workflows for using Generative Artificial Intelligence (GenAI) to identify novel molecules.
Innovation Solution
A processor-implemented method and system that utilize trained GenAI and AI models to generate, filter, and optimize pharmacological target-specific molecules by integrating protein and nucleic acid databases, bibliographic databases, small molecule databases, fragment libraries, reaction rules, and binding affinity databases, employing clustering, ADMET properties, and multi-target machine learning to select and optimize lead molecules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional techniques are used for drug design, then the process can be completed, but it is inefficient, inaccurate, and time-consuming
Solution Approach 1:
The patent replaces conventional mechanical/computational drug design methods with Generative AI models that use neural networks to create and evaluate molecules. The system uses AI-based molecular generation, virtual screening, and docking scoring to automate the drug design process, significantly improving efficiency and reducing time consumption compared to traditional approaches
Solution Approach 2:
The patent transforms the drug design process by changing key parameters such as using AI-generated molecular structures instead of conventional screening, employing deep learning models for property prediction, and implementing iterative optimization cycles. These parameter changes enable the system to handle complex multi-objective optimization more effectively
2Measurement precision
If conventional techniques are used for virtual screening and molecular docking scoring, then the process can be completed, but accuracy is insufficient
Solution Approach 1:
The patent replaces conventional molecular docking scoring methods with AI-based scoring functions that use neural networks trained on experimental data. The system employs AI models to predict binding affinities, ADMET properties, and molecular interactions, providing more accurate and reliable results than traditional scoring functions
Solution Approach 2:
The patent implements feedback mechanisms where AI models continuously learn from experimental results and refine their predictions. The system uses iterative optimization cycles where generated molecules are evaluated against multiple criteria, and the AI models are updated based on performance data, progressively improving accuracy and reliability
3Adaptability or versatility
If GenAI models are used to generate molecules, then novel molecule identification improves, but the workflow integration with in-silico, in-vitro, and in-vivo approaches is unclear
Solution Approach 1:
The patent segments the drug design workflow into distinct modular components: GenAI-based molecular generation module, in-silico virtual screening module, in-vitro validation module, and in-vivo testing module. Each module handles specific tasks and can be independently optimized or replaced, making the complex integrated workflow more manageable and adaptable
Solution Approach 2:
The patent creates a universal AI-driven platform that can perform multiple functions including molecule generation, property prediction, virtual screening, and optimization. The system uses a unified AI architecture that can handle different tasks and integrate with various experimental approaches, providing versatility without requiring separate specialized systems for each function
Data Source
AI summary
The disclosure relates generally to methods and systems for dynamic drug design of a pharmacological target. Conventional techniques in the drug design that use in-silico, in-vitro, and in-vivo approaches are not explicitly mentioned workflow related details for identifying novel molecules. The methods and systems of the present disclosure make the drug design dynamically by integrating the in-silico, in-vitro and in-vivo approaches through the dynamic generative artificial intelligence (GenAI) and artificial intelligence (AI) technologies. The integration of in-silico (molecular modeling and AI), in-vitro and in-vivo approaches helps in designing the novel optimized lead molecules. Optimization and prediction of ADMET based on QM based descriptors help in filtering the molecules.