arxiv_ml 90% Match Research Paper Computational chemists,Drug discovery scientists,Machine learning researchers in chemistry 19 hours ago

Prior-Guided Flow Matching for Target-Aware Molecule Design with Learnable Atom Number

generative-ai › flow-models

📄 Abstract

Abstract: Structure-based drug design (SBDD), aiming to generate 3D molecules with high binding affinity toward target proteins, is a vital approach in novel drug discovery. Although recent generative models have shown great potential, they suffer from unstable probability dynamics and mismatch between generated molecule size and the protein pockets geometry, resulting in inconsistent quality and off-target effects. We propose PAFlow, a novel target-aware molecular generation model featuring prior interaction guidance and a learnable atom number predictor. PAFlow adopts the efficient flow matching framework to model the generation process and constructs a new form of conditional flow matching for discrete atom types. A protein-ligand interaction predictor is incorporated to guide the vector field toward higher-affinity regions during generation, while an atom number predictor based on protein pocket information is designed to better align generated molecule size with target geometry. Extensive experiments on the CrossDocked2020 benchmark show that PAFlow achieves a new state-of-the-art in binding affinity (up to -8.31 Avg. Vina Score), simultaneously maintains favorable molecular properties.

Key Contributions

PAFlow is a novel target-aware molecular generation model that uses prior interaction guidance and a learnable atom number predictor. It employs flow matching for efficient generation and incorporates a protein-ligand interaction predictor to guide towards higher affinity, while an atom number predictor aligns molecule size with protein pocket geometry, addressing instability and size mismatch issues in prior models.

Business Value

Accelerates the drug discovery process by enabling the rapid and targeted generation of novel drug candidates with desired properties, potentially reducing R&D costs and time-to-market for new pharmaceuticals.

Paper Metadata

Innovation Type

Novel Generative Model Architecture and Training Strategy

Deployment Feasibility

Requires significant computational resources and specialized domain knowledge for training and application.

Limitations Addressed

Unstable probability dynamics and mismatch between generated molecule size and protein pocket geometry in existing generative models for SBDD.

Performance Gains

Improved generation stability, better alignment of molecule size with protein pockets, and guidance towards higher binding affinity compared to previous methods.

Technical Tags

molecule designdrug discoverygenerative modelsflow matchingprior guidancetarget-awareatom number predictionprotein-ligand interaction3D moleculesbinding affinity

Research Topics

Computational ChemistryDrug DiscoveryGenerative ModelsMachine Learning for Science

Methods & Architectures

Flow matchingConditional flow matchingProtein-ligand interaction predictionAtom number prediction PAFlow (Prior-Guided Flow Matching)

Applications & Tasks

Drug Discovery Computational Chemistry Materials Science De novo molecule generationStructure-based drug designGenerating molecules with specific properties Generating 3D molecules with high binding affinity to target proteinsDesigning molecules with appropriate size for protein pocketsImproving stability and quality of generated molecules

Related Fields

CheminformaticsBioinformaticsDeep Generative Models

Keywords

molecule designdrug discoverygenerative modelsflow matchingtarget-awarebinding affinityprotein-ligand3D moleculesatom numberconditional flow matchingSBDD

Academic Context

#Computational Chemistry#Drug Discovery#Generative Models#Machine Learning for Science

Commercial Potential

Potential Products

AI-powered drug discovery platformsDe novo molecular design software

Target Industries

PharmaceuticalsBiotechnologyChemicals

Use Case Examples

Designing novel small molecule drugsOptimizing lead compounds for specific targetsDiscovering new materials with desired properties

Competitive Edge

Offers a novel approach to molecule generation by integrating target-specific guidance and size prediction within a flow matching framework.

Market Opportunity

The global drug discovery market is valued in billions of dollars.

Revenue Models

Licensing the technology to pharmaceutical companiesoffering services for molecule design.

Resource Requirements

Compute Needs

High computational resources (GPUs) required for training deep generative models.

Data Requirements

Requires large datasets of protein-ligand complexes and molecular structures.

Deployment Constraints

Integration into existing drug discovery pipelines requires validation and potentially specialized hardware.

Scalability

Scalability depends on the efficiency of the flow matching framework and the complexity of the interaction predictor.

Regulatory Considerations

Data privacy (if using proprietary drug data)Intellectual property protection

Production Readiness

Maturity Level

Research Prototype

Time to Market

2-5 years for integration into drug discovery pipelines.

Patent Potential

Potential for patents on the novel PAFlow architecture and training methods.

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