Lavo

Lavo is an AI-powered computational platform designed to revolutionize the early stages of solid-state drug development by predicting the crystal structures of small-molecule pharmaceuticals. Its core value proposition lies in dramatically accelerating the process of identifying stable, manufacturable solid forms, which is a critical and traditionally slow bottleneck in bringing new drugs to market. By leveraging advanced algorithms, Lavo provides researchers with reliable predictions of a molecule's crystalline landscape, enabling faster, more informed decisions about which forms to pursue in the laboratory.

Key features: The platform enables high-throughput virtual screening of potential crystal polymorphs, salts, and cocrystals. It predicts not only the most stable structure but also assesses crystal stability under various environmental factors like humidity and temperature, which is crucial for manufacturability and shelf life. Specific capabilities include analyzing the risk of form conversion, predicting solubility, and providing detailed lattice energy rankings. For example, it can model how a new API might crystallize with different counterions to find the optimal salt form for development.

What sets Lavo apart is its sophisticated integration of machine learning models with first-principles quantum chemistry algorithms. This hybrid approach combines the speed of AI with the accuracy of quantum mechanical calculations, offering a balance that pure machine learning or pure quantum methods alone cannot achieve. The platform is built on proprietary datasets and algorithms specifically tuned for pharmaceutical solids, and it typically integrates into existing computational chemistry and informatics workflows, providing actionable data rather than just theoretical predictions.

Ideal for computational chemists, solid-state scientists, and formulation developers within pharmaceutical and biotechnology companies. Specific use cases include de-risking late-stage development by identifying stable polymorphs early, accelerating salt and polymorph screening for new chemical entities, and troubleshooting crystallization processes in manufacturing. It is also valuable for academic researchers in crystal engineering and materials science focused on organic molecular crystals.

While the platform operates on a freemium model, offering core prediction capabilities for free, advanced features, higher throughput, and enterprise-level support require a paid subscription. The free tier is suitable for initial exploration and academic use, but industrial R&D teams will typically need a commercial license to integrate it fully into their drug development pipeline.