PDB to 2D Matrix Conversion for AI Drug Discovery
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Solution Overview
Problem
Current techniques are inefficient in converting protein data bank (PDB) files into a format that can be directly processed by artificial intelligence models, leading to poor prediction of binding affinity and complexity in drug development.
Innovation Solution
A method and system that extract PDB files, analyze interatomic interactions, generate a comma-separated values (CSV) file, and perform featurization to create a two-dimensional numerical matrix, allowing for efficient processing and prediction of binding affinity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional shell-based techniques (Onionnet) are used to create features from PDB files, then binary contact values are generated, but the technique is limited to protein-ligand complexes and does not provide efficient processing for artificial intelligence models
Solution Approach 1:
The patent introduces an intermediary coordinate system transformation process that converts 3D atomic coordinates into 2D numerical matrices. This intermediary representation serves as a bridge between the raw PDB file format and AI model inputs, enabling efficient processing while maintaining versatility across different protein complex types including protein-protein and protein-ligand complexes
Solution Approach 2:
The patent transforms the data representation parameters from 3D spatial coordinates to 2D numerical matrices through coordinate system transformation. This parameter change enables the data to be directly processed by AI models while preserving the essential structural and interaction information from the original PDB files
2Productivity
If Prodigy technique is used to predict binding affinity, then linear regression model is applied, but the simple linear model oversimplifies the problem and leads to poor prediction accuracy
Solution Approach 1:
The patent replaces the mechanical linear regression model with a neural network-based AI model. This substitution allows the system to capture non-linear relationships in protein interactions while maintaining computational efficiency, thereby improving prediction accuracy without sacrificing productivity
Solution Approach 2:
The patent transforms the feature representation from traditional contact-based metrics to 2D numerical matrices that encode spatial and interaction information in a different dimensional space. This dimensional transformation enables AI models to process the data more effectively and extract more meaningful patterns for accurate binding affinity prediction
3Loss of information
If PDB files are processed directly without transformation, then original 3D structure information is preserved, but the data cannot be directly processed by artificial intelligence models
Solution Approach 1:
The patent introduces a 2D numerical matrix as an intermediary representation that preserves the essential structural and interaction information from 3D PDB files while making the data compatible with AI model inputs. This intermediary format maintains information fidelity while enabling direct processing by machine learning algorithms
Solution Approach 2:
The patent creates a copied representation of the 3D protein structure in 2D numerical matrix form. This copy contains the necessary interaction and spatial information in a format suitable for AI processing, allowing the original 3D structure to be preserved while enabling efficient computational analysis
Data Source
AI summary
A system and a method for converting a protein data bank file into a two-dimensional numerical matrix is provided. The method also includes extracting PDB files from a PDB, using a data extraction module. The method further includes analyzing the PDB files using an analysis module for calculating and visualizing interatomic interactions in protein structures. The method further includes generating, using a file generation module, a comma separated values (CSV) file based on the analysis. The method further includes performing a featurization of the CSV file using a featurization module. The method further includes generating, using a matrix generating module, the two-dimensional numerical matrix based on the featurization.