Lavo AI
Health care
Maximize Your Reach: Unleashing the Potential of Promote Your Tool
Drug development is a complex and costly process that involves many steps, such as discovering potential drug candidates, optimizing their properties, testing their safety and efficacy, and manufacturing them at scale. One of the key challenges in this process is predicting the crystal structure of drug molecules, which can affect their stability, solubility, bioavailability, and patentability.
Crystal structure prediction (CSP) is the task of finding the most stable arrangement of atoms in a solid state. It is a computationally intensive problem that requires solving the Schrödinger equation for each possible configuration of atoms. Traditionally, CSP has been done using quantum chemistry methods such as density functional theory (DFT), which are accurate but very slow and expensive.
Lavo Life Sciences has developed a novel approach to CSP that leverages AI and cloud computing to make it faster and more robust. Their software uses custom force fields for each target molecule, which are optimized using machine learning techniques. Their AI-accelerated crystal lattice energies are as accurate as DFT and 100 times faster. Their predictions also incorporate hyper-accurate finite-temperature effects, which are important for realistic scenarios.
By using Lavo Life Sciences' software, pharmaceutical companies can reduce the time and cost of CSP from weeks or months to hours or days. This can help them discover new polymorphs, optimize drug properties, avoid patent infringement, and accelerate drug development. Lavo Life Sciences is currently working with several leading pharmaceutical companies to apply their technology to real-world problems.
If you are interested in learning more about Lavo Life Sciences and how their AI-accelerated chemistry simulations can help you with your drug development challenges, please contact them at [email protected].
Pros
- It offers AI-accelerated chemistry simulations that can speed up drug development and reduce costs
- It uses novel techniques to predict and rank crystal structures of drug molecules with high accuracy and efficiency
- It leverages cloud computing to scale its software and achieve rapid turnaround times
Cons
- It is an early-stage startup that may face challenges in funding, regulation, and market competition
- It relies on quantum chemistry and AI models that may have limitations or uncertainties in some cases
- It may have a narrow focus on solid state drug development and miss other opportunities in the pharmaceutical industry