Protein-Ligand Embedding for 3D-Free Binding Property Prediction
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Solution Overview
Problem
Conventional methods for predicting protein-ligand properties, such as molecular docking, are computationally expensive and require advance knowledge of the 3D structures of proteins and ligands, failing to account for conformational changes during binding, leading to inaccurate results.
Innovation Solution
A system that uses an embedding neural network to generate a protein-ligand embedding, jointly trained with a generative model, to predict properties like binding affinity without requiring 3D structures, leveraging rich informational content to accurately predict protein-ligand interactions through a single forward pass.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If molecular docking is used to predict protein-ligand properties, then binding affinity can be estimated, but computational resources are excessively consumed and accuracy is reduced due to inability to account for conformational changes
Solution Approach 1:
The patent replaces traditional molecular docking methods with a neural network-based system that processes protein and ligand sequences directly. The neural network learns conformational changes and binding interactions from training data, eliminating the need for computationally intensive 3D structure calculations and physical docking simulations.
Solution Approach 2:
The system performs preliminary learning of protein-ligand interaction patterns during the training phase. The neural network is pre-trained on large datasets of protein-ligand complexes, enabling it to quickly predict binding properties without requiring extensive computational resources during actual prediction tasks.
2Adaptability or versatility
If conventional molecular docking methods are used, then binding affinity predictions can be obtained, but the method requires advance knowledge of 3D structures which are often unavailable
Solution Approach 1:
The patent substitutes 3D structure-based docking with a sequence-based neural network approach. The network processes amino acid sequences and chemical structures directly, learning to predict binding properties without requiring 3D structural information as input, thereby enabling application to proteins with unknown structures.
Solution Approach 2:
The neural network acts as an intermediary that translates sequence information into binding property predictions. It learns to infer structural and functional characteristics from sequences alone, bridging the gap between available sequence data and desired binding affinity predictions without requiring actual 3D structures.
3Measurement precision
If traditional docking methods account for conformational changes, then prediction accuracy improves, but computational complexity and resource requirements increase significantly
Solution Approach 1:
The patent replaces complex physical docking simulations with a neural network that has been trained to implicitly model conformational changes. The network learns patterns of structural adaptation from training data and applies this knowledge directly to predictions, avoiding the need for explicit conformational sampling and energy minimization calculations.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a predicted property score of a protein and a ligand. In one aspect, a method comprises: obtaining a network input that characterizes a protein and a ligand; processing the network input characterizing the protein and the ligand using an embedding neural network to generate a protein-ligand embedding representing the protein and the ligand, wherein the embedding neural network has been jointly trained with a generative model that is configured to: receive an input protein-ligand embedding; and generate, while conditioned on the input protein-ligand embedding, a predicted joint three-dimensional (3D) structure of an input protein and an input ligand represented by the input protein-ligand embedding; and generating a property score that defines a predicted property of the protein and the ligand using the protein-ligand embedding.