Neural Network Compound Function Prediction via CMAP
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Solution Overview
Problem
Current drug screening systems are limited by high costs, difficulty in model construction, limited model availability, and reliance on manual analysis, which hinders the efficient and accurate prediction of drug efficacy and side effects, especially for new compounds and virtual molecules.
Innovation Solution
A compound function prediction method based on a neural network and connectivity map (CMAP) algorithm, which involves constructing molecular encoding vector neural networks, training autoencoders, and using gene expression variation data to evaluate the correlation between compounds and diseases, enabling high-throughput prediction of drug functions and side effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high-throughput screening (HTS) technology is used to accelerate drug screening, then screening speed is improved, but cost and device complexity increase significantly
Solution Approach 1:
The patent replaces physical HTS systems with automated robots and detection instruments with a computational system based on neural networks and gene expression data analysis. The deep learning model processes molecular structure data and gene expression profiles to predict drug efficacy and side effects, eliminating the need for complex laboratory automation equipment while achieving high-throughput screening capabilities.
2Adaptability or versatility
If existing CMAP technology is used to predict compound functions, then prediction capability is provided, but it is limited to only 1,309 small molecule compounds with known data points
Solution Approach 1:
The patent develops a universal neural network model that can process and predict functions for any small molecule compound based on its molecular structure, rather than being limited to a specific database of 1,309 compounds. The model takes molecular fingerprints and gene expression data as inputs and can predict both therapeutic effects and side effects for novel compounds including virtual molecules that have never been tested experimentally.
Solution Approach 2:
The patent performs preliminary training of the neural network model using existing gene expression data from cell responses to various compounds. This pre-trained model can then rapidly predict functions for new compounds without requiring prior experimental data for each specific compound, enabling predictions for novel molecules before they are synthesized or tested.
3Extent of automation
If machine learning methods are applied to drug screening, then automation and efficiency are improved, but reliance on existing databases and artificially classified features limits accuracy
Solution Approach 1:
The patent changes the input parameters from artificially classified molecular features to molecular fingerprints (structural descriptors) combined with gene expression profiles. The neural network learns the relationship between molecular structure and biological response directly from data, rather than relying on pre-defined classification systems. This approach captures subtle structure-activity relationships that manual classification methods miss, improving prediction accuracy while maintaining full automation.
Data Source
AI summary
The present disclosure provides a compound function prediction method based on a neural network and a connectivity map (CMAP) algorithm. The compound function prediction method is used to predict an efficacy of a compound, and the compound function prediction method includes the following steps: constructing a compound molecule-encoding vector neural network; constructing and training an encoding vector-marker gene expression variation deep neural network; constructing and training a marker gene expression level or gene expression variation-whole genome gene expression level or gene expression variation neural network; constructing upregulated and downregulated gene sets of a disease or a phenotype; and evaluating a correlation between the compound and the disease or the phenotype.