PostEra utilizes machine learning to optimize the Design-Make-Test cycle in medicinal chemistry, enhancing the efficiency of drug discovery. This approach aims to increase the number of viable drug candidates, ultimately delivering more effective treatments to patients.
Funding
$26.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
IGNIFounders
Product
Problem
The traditional drug discovery process is slow and inefficient, often requiring extensive resources and time to identify viable drug candidates. Optimizing the design-make-test cycle in medicinal chemistry presents a significant challenge, hindering the development of effective treatments for patients.
Solution
PostEra leverages machine learning to accelerate and enhance the drug discovery process. Their approach focuses on optimizing the design-make-test cycle, enabling medicinal chemists to more efficiently identify and develop promising drug candidates. By integrating machine learning into various stages of drug discovery, PostEra aims to increase the number of viable drug candidates and ultimately deliver more effective treatments to patients in a timely manner. The company is building a modern biopharma, applying advances in machine learning to bring more cures to patients.
Target Audience
PostEra's primary customers are pharmaceutical companies, research institutions, and medicinal chemists involved in drug discovery and development.
Features
- Manifold software platform for synthesis and search
- Machine learning-driven optimization of the design-make-test cycle