3D Molecular Structure Prediction With Alignment-Based Generative Training
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Solution Overview
Problem
Existing machine learning models face challenges in accurately predicting the three-dimensional (3D) structures of protein-ligand complexes due to scarcity of training data and computational complexity, particularly in integrating protein and ligand structures into a single predictive model.
Innovation Solution
A modular machine learning architecture comprising separate generative models for proteins and ligands, trained on abundant protein and ligand data, generates initial 3D structures which are then combined to predict the 3D structure of protein-ligand complexes, utilizing denoising techniques and physics-informed neural networks for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a single integrated model is used to predict protein-ligand complex structures, then the model can directly output the final structure, but the model requires abundant training data and high computational resources which are scarce
Solution Approach 1:
The patent divides the complex prediction task into separate modular components: a protein structure prediction model and a ligand binding pose model. Each module is trained independently on specialized data, reducing the overall computational burden while maintaining prediction accuracy through coordinated operation of the separate models
2Measurement precision
If abundant training data is used to train a comprehensive model, then prediction accuracy improves, but the computational resources and training time increase significantly
Solution Approach 1:
The patent pre-trains the protein structure prediction model on abundant protein structure data before fine-tuning it for complex prediction. This preliminary training establishes a strong foundation that reduces the amount of additional training data and time needed for the final application, while still achieving high accuracy
3Adaptability or versatility
If specialized generative models are used for proteins and ligands separately, then the model can leverage abundant specialized data, but the integration of multiple models increases system complexity
Solution Approach 1:
The patent employs a unified transformer architecture that serves multiple functions: it processes protein sequences, ligand structures, and predicts complex formations within a single model framework. This multi-functional design allows the system to leverage specialized data for each component while maintaining architectural simplicity through code reuse and unified processing
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a generative model by a machine learning training technique using an alignment objective. In one aspect, a method comprises, at each of a sequence of training steps: obtaining data characterizing a set of one or more molecules for the training step; processing, by the generative model, the data characterizing the set of one or more molecules to generate a plurality of alternative predicted 3D structures of the set of one or more molecules; and determining a respective alignment score for each of the plurality of alternative predicted 3D structures of the set of one or more molecules for the training step; and training the set of generative model parameters of the generative model to optimize the alignment objective.