Crystal Structure Prediction
In silico polymorph discovery of novel molecules using Al/ML, GPU acceleration & DevOps powered automation on cloud infrastructure
Context
Accelerated workflow for a free energy landscape scan
Identifying polymorphs with the desired physiochemical properties demand a comprehensive scan of the free energy landscape of an API molecule in its crystalline solid state.
Such an undertaking requires implementing computationally expensive Quantum mechanical/chemical methods. This expense is compounded multifold if the polymorph studies include salts, cocrystals, hydrates/solvates, etc.
Aganitha utilizes advanced AI/ML models in tandem with GPU-based QM software, all running on elastic/scalable cloud infrastructure, to aid in exploring the energetics of a range of candidate crystal structures within shorter time frames. These can serve as valuable computational aids in the formulation efforts of your product.
Such an undertaking requires implementing computationally expensive Quantum mechanical/chemical methods. This expense is compounded multifold if the polymorph studies include salts, cocrystals, hydrates/solvates, etc.
Aganitha utilizes advanced AI/ML models in tandem with GPU-based QM software, all running on elastic/scalable cloud infrastructure, to aid in exploring the energetics of a range of candidate crystal structures within shorter time frames. These can serve as valuable computational aids in the formulation efforts of your product.
Our Solution
Scalable and accelerated in silico pipeline to identify stable polymorphs
Use our pipeline for a cost-effective in silico crystal structure screening. We combine the concepts of Quantum chemistry with advances in Al/ML, Cloud & DevOps. Key features of our solution include:
- Generative models to navigate the conformational space of an organic molecule
- Diffusion based models to generate candidate crystal structures
- Graph Neural Networks (GNN) based models to predict Lattice Energy
- Accelerated DFT computational pipelines to screen candidate crystal structures
Highlights
Key components of Aganitha’s Crystal Structure Prediction pipeline
Pipeline with comprehensive capabilities
Generation of diverse candidate crystal structures starting from multiple stable 3D conformers for a more comprehensive exploration of the crystal structure landscape
State of the art AI/ML tools & techniques
Diffusion models to generate candidate crystal structures and GNN based for Lattice Energy prediction models
System specific customization
Modules built to leverage open-source packages, AI/ML models, GPU based QM packages
Outcomes
Swift and data safe polymorph discovery
Fast and Cost-effective
Diffusion models & GNN models drive identification of polymorphs thereby rapidly getting you to your end result.
Data Privacy and safety
We bring infrastructure as code to your data in your environment ensuring that your data is safe
Configurable and Scalable
Scalable computational resources with on-demand cloud-based High Performance Computing (HPC) clusters workload management techniques
Download our case study on stable conformer identification
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Our Services
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Case Studies
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