Multi-Modal Deep Learning for Structure-Based Drug Design
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Solution Overview
Problem
Existing deep learning-based drug design methods, particularly structure-based approaches, face challenges in generating diverse and effective molecules for target proteins with limited known ligand data, as they often rely on existing datasets, limiting their applicability to newer targets.
Innovation Solution
A multi-modal deep learning model comprising a graph attention-based variational auto-encoder (GAT-VAE), simplified molecular input line entry system based variational auto-encoder (SMILES-VAE), conditional molecular generator, and drug-target affinity (DTA) predictor module is used to generate target-specific molecules. The model learns protein structure and molecule grammar, iteratively optimizing molecules through reinforcement learning to achieve high affinity with the target protein.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ligand-based drug design methods are used, then reliable results can be obtained for popular drug targets, but their utility is restricted against newer target proteins with limited known ligand data
Solution Approach 1:
The patent segments the drug design approach into structure-based components (protein 3D structure analysis, binding site identification) that can be applied independently of ligand data availability. This allows the method to be adapted to any target protein with known structure, regardless of ligand data availability.
Solution Approach 2:
The patent introduces deep learning models as intermediaries that bridge the gap between protein structure and ligand design. These models are trained on existing ligand data and can then generate predictions for new targets, serving as a mediator that transfers knowledge from known to unknown targets.
2Manufacturing precision
If structure-based drug design uses traditional fragment growing methods, then molecules can be generated with complementary features, but the exploration of unexplored chemical space is limited
Solution Approach 1:
The patent employs dynamic deep learning models that can adapt and generate diverse molecular structures beyond static fragment assembly. The generative models explore chemical space dynamically by learning from data and generating novel structures that maintain complementary features while exploring uncharted territories.
Solution Approach 2:
The patent changes the parameters of molecular generation by using deep learning models that can vary molecular properties systematically. This allows exploration of diverse chemical space while maintaining the essential complementary features needed for binding, going beyond traditional fragment parameters.
3Adaptability or versatility
If deep learning models are used to explore chemical space, then new molecules can be designed with physicochemical property optimization, but the time from early-stage design to experimental validation remains significant
Solution Approach 1:
The patent performs preliminary actions by pre-training deep learning models on extensive ligand datasets before actual drug design. This preliminary training enables the models to quickly generate optimized molecules for new targets, reducing the time needed during the actual design-validation cycle.
Solution Approach 2:
The patent establishes continuous useful action through iterative optimization loops where generated molecules are evaluated and fed back into the model for improvement. This continuous refinement process accelerates the design-validation cycle by maintaining momentum and avoiding restarts.
Data Source
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AI summary
This disclosure relates generally to method and system for structure-based drug design using a multi-modal deep learning model. The method processes a target protein for designing at least one optimized molecule by using a multi-modal deep learning model. The GAT-VAE module obtains a latent vector of at least one active site graph comprising of key amino acid residues from the target protein. The SMILES-VAE module obtains at least one latent vector from the target protein. Further, the conditional molecular generator concatenates the active site graph with the latent vector to generate a set of molecules. The RL framework is iteratively performed on the concatenated latent vector to optimize at least one molecule by using the drug-target affinity (DTA) predictor module to predict an affinity value for the set of molecules towards the target protein. Further, at least one optimized molecule is designed with an affinity of the target protein.