Molecular Reconstruction via Substructure Embeddings and Junction Trees
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Solution Overview
Problem
Pharmaceutical research is hindered by the complexity of protein-ligand interactions, where proteins and ligands have numerous possible conformations and interaction sites, making it computationally challenging to predict valid molecules effectively.
Innovation Solution
A system and method using a transmoler that identifies common substructures of a given 3D conformer and predicts its structural information through substructure embeddings learned via contrastive learning, oriented 3D object regression, and junction tree molecular graphs generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to analyze protein-ligand interactions and predict valid molecules, then comprehensive analysis of all possible conformations and interaction sites is attempted, but the computational complexity becomes intractable
Solution Approach 1:
The patent segments the complex molecule into multiple substructures and represents each substructure as a separate embedding. This segmentation allows the system to analyze individual substructures independently rather than treating the entire molecule as one complex unit, thereby reducing computational complexity while maintaining comprehensive analysis capability.
Solution Approach 2:
The patent introduces substructure embeddings as intermediary representations that bridge the gap between raw molecular structures and high-level molecular properties. These embeddings serve as compressed, informative intermediaries that capture essential features without requiring exhaustive analysis of all atomic details, thus reducing computational burden.
2Reliability
If all possible spatial conformations of proteins and ligands are considered, then accurate prediction of valid molecules is achieved, but the computational resources required become prohibitively large
Solution Approach 1:
The patent changes the representation parameters from atomic-level details to substructure-level embeddings. This parameter transformation reduces the dimensionality of the search space while preserving the essential information needed for accurate prediction, enabling the system to maintain reliability with reduced computational resource consumption.
Solution Approach 2:
The patent performs preliminary encoding of substructures into embeddings before the main prediction task. This preliminary action pre-processes and compresses molecular information into compact representations, reducing the computational resources needed for subsequent analysis while maintaining the ability to accurately predict valid molecules.
3Manufacturing precision
If detailed 3D conformation analysis is performed to identify substructures, then accurate molecular reconstruction is achieved, but the computational tractability is reduced
Solution Approach 1:
The patent replaces traditional mechanical 3D conformation analysis methods with a learned embedding-based approach. Instead of computationally intensive geometric analysis, the system uses neural network-based substructure embeddings that automatically capture 3D structural information, achieving accurate molecular reconstruction with improved computational efficiency.
Solution Approach 2:
The patent creates simplified copies of molecular structures in the form of substructure embeddings. These embeddings are compressed representations that capture the essential 3D conformational information needed for accurate reconstruction without requiring full detailed analysis, thus improving computational efficiency while maintaining manufacturing precision.
Data Source
AI summary
A system and method comprising a transmoler that identifies common substructures of a given 3D conformer and predicts its structural information. First, based on contrastive learning, substructure embeddings are learned in an unsupervised manner. Secondly, a novel oriented 3D object regressor predicts the dimensions and directions of each substructure in a conformer as well as its fingerprint embedding which are used to create differentiable junction tree molecular graphs. Lastly, using the junction tree graphs, molecular representations such as DeepSMILES are generated which represent new and novel molecules. The system may also generate conformers directly from a pocket. A pocket may be input to the model and the model learns to generate structures which can fit that pocket by conditioning the generative system. Furthermore, structure-based contrastive embeddings generated for transmoler can be recycled in structure-based generative modelling.