Pose-Ensemble Polymer Binding Models for Accurate Bioactivity Prediction
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Solution Overview
Problem
Conventional structure-based deep learning methods for characterizing interactions between compounds and target polymers fail to account for enthalpic and entropic components, leading to pose insensitivity and inaccurate predictions.
Innovation Solution
Implementing a vHTS machine learning model with a conditional multi-task architecture and an attention mechanism to enforce sensitivity to distinct ligand poses, using neural networks to process multiple poses concurrently and derive interaction scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning methods are used to predict bioactivity from static ligand poses, then the model structure is simple and easy to implement, but the model becomes pose insensitive and produces inaccurate predictions
Solution Approach 1:
The model segments the pose representation into multiple independent pose embeddings, each capturing specific pose characteristics. This allows the system to process multiple poses concurrently while maintaining computational efficiency, resolving the contradiction between prediction accuracy and model complexity
Solution Approach 2:
The patent transitions from single static pose representation to multi-dimensional pose ensemble representation. By adding the dimension of pose variability and using attention mechanisms to weight different poses, the model achieves pose sensitivity without excessive computational complexity
2Reliability
If static ligand pose prediction is used, then the computational time is short, but the entropic contribution to free energy change is ignored
Solution Approach 1:
The system performs preliminary pose generation and embedding creation during the docking process, preparing pose representations in advance. This preliminary action allows the attention mechanism to efficiently evaluate entropic contributions without requiring extensive computational resources during the final prediction step
Solution Approach 2:
The patent changes the parameter representation from single static pose to ensemble of poses with varying conformational states. By representing multiple poses and using attention mechanisms to weight them according to their thermodynamic relevance, the model captures entropic effects while maintaining reasonable computational efficiency
3Measurement precision
If pose sensitivity is added to the model, then the bioactivity prediction accuracy improves, but the model becomes more complex and harder to train
Solution Approach 1:
The attention mechanism provides feedback by dynamically weighting pose embeddings based on their relevance to the binding interface. This feedback loop allows the model to automatically identify important poses during training, simplifying the training process while maintaining pose sensitivity
Solution Approach 2:
The pose embedding layer serves multiple functions simultaneously: it represents pose geometry, captures pose variability, and provides features for the attention mechanism. This multi-functionality reduces the need for separate components, thereby reducing overall model training complexity while achieving pose sensitivity
Data Source
AI summary
Systems and methods for characterizing an interaction between a compound and a polymer include obtaining a plurality of sets of atomic coordinates. Each set of atomic coordinates comprises the compound bound to the polymer in a corresponding pose in a plurality of poses. Each respective set of atomic coordinates, or an encoding thereof, is sequentially inputted into a neural network, to obtain a corresponding initial embedding as output, thereby obtaining a plurality of initial embeddings. Each initial embedding corresponds to a set of atomic coordinates in the plurality of sets of atomic coordinates. An attention mechanism is applied to the plurality of initial embeddings, in concatenated form, to obtain an attention embedding. A pooling function is applied to the attention embedding to derive a pooled embedding. The pooled embedding is inputted into a model to obtain an interaction score of the interaction between the compound and the polymer.