Integrated Molecular Docking and Machine Learning Pipeline for Drug Discovery ID: 2021-044

This technology is a novel pipeline that combines molecular docking tools with machine learning to enhance drug discovery processes.

Technology Overview

The invention detailed in "2021-044d_Redacted.pdf" is a cutting-edge technology developed by researchers that integrates various open-source molecular docking tools—rDock, Autodock, and Autodock Vina—with a neural network to simulate and predict the binding affinity of small molecules to proteins. This pipeline is designed to improve the efficiency and accuracy of identifying potential drug candidates by leveraging the strengths of both computational docking simulations and machine learning predictions. The technology is adaptable, allowing for both pre-trained and customizable neural network models based on specific receptor and activity data sets.

Key Advantages

• Integrates results from multiple docking tools for a more accurate prediction of ligand activity
• Employs a neural network to enhance docking simulations, making the technology accessible to non-experts
• Offers dual-format models for flexibility in addressing both new and known protein targets
• Facilitates rapid training of custom machine learning models, demonstrating potential for consulting and sales enablement in biotech

Problems Addressed

• Overcomes the limitations of individual molecular docking tools by providing a comprehensive, integrated solution
• Reduces the barrier to executing advanced machine learning tasks in drug discovery for non-experts
• Improves the speed and accuracy of identifying viable drug candidates
• Enables customization for specific research needs, enhancing the relevance of predictions for novel targets

Market Applications

• Drug discovery and development for pharmaceutical companies
• Academic research in molecular biology and pharmacology
• Consulting services for biotechnology firms, leveraging the technology for rapid model training and demonstration
• Integration into existing computational chemistry and drug discovery platforms

Additional Information

Technology ID: 2022-002
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Date Published: 28 March, 2025