Solid Form De-risking

Bridging Molecular Structure to Drug Performance and Stability

Context

The Physical Form of Your Drug is Its Destiny

From molecule to medicine, the solid form determines success.
Selecting the right polymorph, salt, or co-crystal is critical to achieving optimal bioavailability, manufacturability, and long-term stability—while avoiding costly late-stage failures. Regulatory approval demands a fully characterized, controlled, and validated solid-state strategy, making solid form selection one of the highest-risk decisions in drug development.

Our solutions span solid-form discovery and stability de-risking, through formulation-ready molecular and physicochemical insight, to data-driven product design that supports scalable, regulatory-ready manufacturing—and beyond.

Key Solution Areas

At Aganitha, we empower R&D teams to navigate the journey from molecule to medicine with computational aids that blend rigorous API Solid-State Engineering with data-driven Formulation Design to enable  material stability and product performance optimization  in tandem. 

Our methods combine AI-driven modeling with first-principles physics to rapidly and reliably characterize solid-state behavior. Deep learning accelerates polymorph screening, while DFT, electronic structure methods, and Molecular dynamics (MD) simulations provide high-fidelity assessment of stability and transformation risk.

Solution area

API-Centric Solutions: Solid Form Engineering & De-risking

We have developed and benchmarked a proprietary, machine-learning model designed for the high-throughput prediction of Lattice Energies (LE) of organic molecular crystals to facilitate formulation design.

Our tool empowers drug developers with high-speed solutions needed to identify the most stable and viable API solid forms earlier in the discovery pipeline.

Crystal Structure Prediction (CSP)

  • Proprietary deep learning pipeline for lattice energy prediction, which is trained on an in-house curated database of high-accuracy DFT energies for drug-like molecules.
  • Identify and rank stable polymorphs using high-throughput & accelerated Quantum Mechanics (QM) calculations
  • This enables high-throughput screening upstream, ensuring the most stable forms are prioritized early.

Molecular conformer ensemble generation

  • A computational pipeline to generate the necessary conformational ensembles of drug-like molecules across diverse solvent environments and physicochemical conditions. 
  • Utilizes advanced cheminformatics, self-supervised learning, enhanced MD simulations, and rigorous High-Throughput DFT calculations to ensure the generated conformers are both accurate and relevant to the solid state landscape.
  • Provides the essential input for all downstream CSP and physicochemical property estimation.

Stability of Periodic Systems

  • Study the dynamic behavior of crystalline systems to validate physical stability and process viability using advanced QM and MD simulations on periodic structures.

Solid Form Property Estimation

  • Characterize the API by estimating critical solid-state properties such as: lattice energy, true density, and free energy of fusion—providing the essential data needed to predict downstream processability.
Solution area

Formulation-Centric Solutions: Data-Driven Product Design

Our formulation capabilities bridge the gap between the drug substance and the final drug product, leveraging historical data and advanced simulation to guide excipient selection.

Intelligent Surrogate Search

  • Identify proven formulation routes and compatible excipients for novel compounds, providing a confident starting point for design.
  • A searchable database of FDA-approved formulations that allows users to identify “surrogate” molecules based on physicochemical similarity. 

Amorphous Solid Dispersions (ASD)

  • Thermodynamic profiling of drug-polymer complexes to predict miscibility and stability, guiding the development of stable ASDs.

Ionic Liquid (IL) Screening for Biologics

  • We develop informatics pipelines for biologic-compatible Ionic Liquid (IL) formulations, predicting critical properties (e.g., viscosity, stability) of Ab-IL systems. This is a vital alternative route for formulating high-concentration biologics by proactively addressing persistent challenges like aggregation and poor stability in conventional aqueous solutions.

Physicochemical Property Profiling

  • Computational pipelines to estimate properties crucial for formulation and process design, including Non-Aqueous pKa for precise crystallization solvent selection, lipophilicity, and solubility across diverse pure organic solvents & solvent mixtures.
Highlights

Key components of Aganitha’s Solid State Chemistry Solutions

Solutions with comprehensive capabilities

Acceleration of solid-state development by predicting critical properties and optimizing drug forms.

State of the art AI/ML tools & techniques

Advanced AI/ML models and computational techniques to predict critical solid-state properties and accelerate drug form optimization.

System specific customization 

Advanced computational and modeling workflows, including Force Field selection and parameter refinement, to the unique characteristics of your specific API and excipient systems, ensuring the highest predictive accuracy for your project needs.

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

Discover our offerings across the biopharma value chain

Our Solutions

Customizable platforms and solutions across the drug discovery value chain from target discovery to therapeutic development
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Our Services

Offering services in computational sciences and technology to complement biopharma R&D
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Case Studies

Helping clients accelerate drug discovery and development by building computational biology, computational chemistry, technology, and cloud solutions
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Learn more about Crystal Structure Prediction

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