Ligand Affinity Prediction via Spatial Interaction Mapping
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Solution Overview
Problem
Current methods for improving ligand binding affinity to protein targets are inefficient, particularly for small molecules and biologicals, due to the complexity of predicting effective modifications and the time-consuming nature of mutation testing, and existing algorithms discard useful data or introduce bias.
Innovation Solution
A method that identifies favourable interaction regions in protein binding sites by analyzing non-bonding atom contacts, allowing for the prediction of modifications to ligands that enhance their affinity by superimposing spatial data from a structural database onto the target binding site, thereby improving the accuracy of ligand design and modification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional trial and error chemical modification is used to improve ligand binding affinity, then binding affinity may be improved, but the process becomes extremely time-consuming and inefficient
Solution Approach 1:
The patent applies preliminary action by performing virtual screening and computational prediction of effective modifications before actual chemical synthesis and testing. The system pre-identifies promising ligand modifications using AI/ML models trained on structural data, allowing researchers to focus experimental efforts only on the most promising candidates, thus dramatically reducing optimization time while maintaining high binding affinity improvement rates
2Adaptability or versatility
If the molecular weight of hit molecules is kept low (300 Daltons or less) to allow subsequent chemical modification, then adaptability for further modification is improved, but the initial binding affinity is typically sub-optimal
Solution Approach 1:
The patent applies local quality by analyzing the specific binding pocket environment and identifying precise local regions where chemical modifications should be made. The system examines the three-dimensional structure of the protein-ligand complex and determines exactly which atoms or functional groups of the low molecular weight hit should be modified, what type of modification is appropriate, and where the new atoms should be positioned to maximize binding affinity improvement while maintaining the core molecular framework
3Manufacturing precision
If structural information from X-ray crystallography is obtained to guide ligand optimization, then manufacturing precision of ligand design is improved, but the complexity of the overall process increases
Solution Approach 1:
The patent applies copying by creating simplified computational models and virtual representations of the protein binding site and ligand interactions based on X-ray crystallographic data. Instead of directly manipulating complex structural data, the system generates digital copies including molecular surfaces, electrostatic potential maps, and interaction networks that can be easily analyzed and manipulated by AI/ML algorithms, thereby maintaining high design accuracy while reducing process complexity
4Productivity
If computer algorithms are used to predict effective ligand modifications, then productivity is improved, but the predictions may be unreliable due to the vast number of possible modifications
Solution Approach 1:
The patent applies segmentation by breaking down the vast space of possible ligand modifications into manageable segments or categories. The system divides modifications into types (e.g., adding functional groups, changing ring structures, extending chains) and prioritizes them based on their likelihood of success, using the segmented approach to systematically explore the modification space with AI/ML models, thereby maintaining both high productivity and reliable predictions
Data Source
AI summary
Methods and associated apparatus involving designing a ligand ab initio that will bind to a binding site of a macromolecular target, or of identifying a modification to a ligand for improving the affinity of the ligand to a binding site of a macromolecular target, comprising using information about non-bonding, intra-molecular or inter-molecular atom to atom contacts extracted from a database of biological macromolecules to identify favoured regions adjacent to the binding site for particular atom types and modifying a candidate ligand to increase the intersection between atoms of the candidate ligand and the favoured regions. One or more steps of the methods may be performed by a computer.