Protein Sequence Generation With Smoothed Energy-Based Models
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Solution Overview
Problem
Existing computational protein design methods face challenges in efficiently generating diverse and therapeutically viable protein sequences due to overfitting and limited exploration of the high-dimensional data distribution, particularly in large molecule drug discovery, where evaluating all possible sequences is computationally expensive and resource-intensive.
Innovation Solution
A protein design computation model is trained using a noisy training set to approximate the data distribution, employing energy-based models (EBMs) and Markov Chain Monte Carlo sampling to generate protein sequences that capture a smoothed energy landscape, enabling efficient sampling from higher density regions and avoiding repetitive outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing computational protein design methods are used to generate protein sequences, then the generation process can be completed, but the methods suffer from overfitting and limited exploration of the high-dimensional data distribution, resulting in repetitive outputs and reduced diversity
Solution Approach 1:
The method performs preliminary actions by adding noise to training sequences before training the energy-based model. This preprocessing step smooths the energy landscape in advance, enabling better exploration of the data distribution during the generation phase and preventing overfitting to the original training data.
Solution Approach 2:
The invention changes the parameter representation by working with noisy versions of the training sequences. By transforming the input data with added noise, the model learns a smoothed energy function that generalizes better to the high-dimensional protein sequence space, improving both diversity and therapeutic viability of generated sequences.
2Reliability
If all possible protein sequences are evaluated to ensure therapeutic viability, then the most viable sequences can be identified, but the computational cost and resource requirements become prohibitively expensive
Solution Approach 1:
The energy-based model provides feedback through the smoothed energy function to guide the generation process. Instead of exhaustively evaluating all possible sequences, the model uses the learned energy landscape to efficiently identify promising regions in the sequence space, dramatically reducing computational resources while maintaining therapeutic viability assessment.
Solution Approach 2:
The invention replaces the mechanical exhaustive evaluation system with a learned energy-based model. Instead of systematically checking all possible sequences, the model uses the smoothed energy function to predict therapeutic viability, substituting computational brute force with a more efficient learned approximation.
3Measurement precision
If the energy-based model is trained on clean training data, then the model can learn the data distribution, but the model overfits and fails to explore the high-dimensional space effectively
Solution Approach 1:
Noise is added to training sequences as a preliminary action before model training. This smoothing operation prevents the model from memorizing exact training examples while still capturing the underlying data distribution, enabling effective exploration of the high-dimensional protein sequence space.
Solution Approach 2:
The noise added to training sequences acts as an intermediary that mediates between the training data and the model. This intermediate noisy representation allows the model to learn the essential patterns of the data distribution without overfitting to specific training examples, improving generalization to the high-dimensional space.
Data Source
AI summary
A training set may be generated to include a plurality of noisy sample sequences. Each noisy sample sequence in the training set may be generated by adding noise to a corresponding sample sequence from a data distribution. A protein design computation model may be trained by at least applying the protein design computation model to generate one or more output sequences, and adjusting the protein design computation model to reduce a difference between the one or more output sequences and the plurality of noisy sample sequences in the first training set. The trained protein design computation model may be applied to generate an output sequence by at least modifying an input sequence.