Autoregressive Drug Design With Protein–Ligand Interaction Learning
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Solution Overview
Problem
Existing drug design methods face challenges such as low accuracy, high computational complexity, false positivity/negativity, over-fitting, lack of zero-shot learning ability, and mode collapse, leading to inefficiencies and high costs in drug research and development.
Innovation Solution
A drug design method using an autoregressive model that includes constructing tokenizers through sub-word tokenization, processing and transforming data into a text form, training the model on protein-ligand interactions, generating predicted ligands, and optimizing them with chemical/biological tools to determine candidate molecules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If molecular docking is used to predict binding modes, then interaction between small molecules and proteins can be simulated, but computational complexity increases sharply for multiple active sites or very large molecules
Solution Approach 1:
The patent replaces the traditional molecular docking mechanical simulation system with a deep learning-based predictive system. The model learns binding mode patterns from training data and directly predicts binding modes without performing computationally intensive molecular docking simulations, thereby substituting mechanical computation with intelligent prediction.
Solution Approach 2:
The patent performs preliminary training of the deep learning model on a large dataset of protein-ligand complexes before actual prediction. This preliminary action allows the model to pre-learn binding mode patterns, so that during actual use, predictions can be made quickly without repeating the full docking process for each new molecule.
2Measurement precision
If scoring functions are used in molecular docking to predict interaction intensity, then binding affinity can be estimated, but accuracy is reduced due to simplified physical models that cannot reflect complex biomolecular environment
Solution Approach 1:
The patent replaces simplified physical scoring functions with a deep learning model that has learned complex interaction patterns from training data. The model captures nuanced biomolecular environment effects that simplified physics-based scoring functions miss, by substituting mechanical physics calculations with data-driven intelligent prediction.
Solution Approach 2:
The patent changes the fundamental parameters used for prediction from simplified physical parameters (van der Waals, hydrogen bonds, electrostatic interaction) to learned representations from deep learning. The model transforms input molecular structures through multiple layers of neural networks to generate prediction scores that reflect complex environmental effects without explicitly calculating each physical parameter.
3Measurement precision
If QSAR model is constructed with many descriptors to capture molecular characteristics, then relationship between structure and activity can be established, but over-fitting occurs with limited training samples reducing generalization ability
Solution Approach 1:
The patent segments the feature extraction process into multiple hierarchical layers of the deep learning model. Instead of using a fixed set of descriptors, the model automatically learns and segments relevant features at different levels of abstraction, from basic molecular properties to complex interaction patterns, adapting to the available training data size.
Solution Approach 2:
The patent changes from using a large number of fixed descriptors to using learned embeddings from deep neural networks. The model dynamically adjusts its internal parameters through training, capturing essential structure-activity relationships with fewer effective parameters, thereby avoiding over-fitting while maintaining predictive accuracy.
4Productivity
If traditional drug design methods are used for high flux screening, then large number of compounds can be evaluated, but false positivity and false negativity occur limiting reliability
Solution Approach 1:
The patent replaces traditional molecular docking and scoring systems with a deep learning model that has learned from diverse training data. This substitution maintains high screening throughput while improving reliability by capturing complex patterns that traditional methods miss, reducing both false positives and false negatives through intelligent prediction.
Data Source
AI summary
A drug design method based on an autoregressive model is provided, which relates to the field of drug design technologies. The method includes: applying a sub-word tokenization algorithm to biological text processing, training protein and ligand information in data sets to obtain a protein tokenizer and a ligand tokenizer, and constructing a tokenizer of the autoregressive model; processing and transforming original data in the data sets into a text form, and encoding by the tokenizer to construct a training data set for the autoregressive model; training the autoregressive model by the training data set, so that the autoregressive model can understand SMILES representations of ligands and learn an interaction mode between proteins and ligands; generating predicted ligands by using the trained autoregressive model, and post-processing through a chemical information tool to acquire candidate ligands with specific chemical structures; and evaluating and optimizing the candidate ligands to determine target candidate molecules.