Inductive Bio provides a virtual laboratory platform that uses AI chemistry assistants and ADMET models to predict molecular behavior in the body. This capability allows drug discovery teams to run millions of _in silico_ experiments to surface the strongest hypotheses for wet lab testing. The platform accelerates development candidate selection by front-loading critical safety and efficacy insights before synthesis.
Funding
$8.5M 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.
Founders
Product
Problem
Optimizing the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of small molecule drug candidates is a time-consuming and challenging aspect of drug discovery. Traditional methods often rely on limited datasets and lack the predictive power needed to efficiently guide compound design. This can lead to costly failures later in the development process.
Solution
Inductive Bio offers a machine learning platform designed to accelerate compound optimization by providing scientists with advanced ADMET analysis capabilities. The platform leverages proprietary datasets and purpose-built machine learning models to predict ADMET properties with high accuracy. Its intuitive chemistry design software provides real-time predictions as compounds are drawn, enabling data-driven decision-making throughout the design process. By integrating comprehensive data, advanced models, and user-friendly software, Inductive Bio empowers teams to rapidly identify and optimize promising drug candidates.
Target Audience
The primary users are pharmaceutical companies and research organizations involved in small molecule drug discovery, specifically medicinal chemists, DMPK scientists, and other researchers focused on optimizing ADMET properties.
Features
- Proprietary ADMET dataset, normalized and continuously growing to cover diverse chemotypes and assays
- High-performing machine learning models built on deep learning architectures, tailored for heterogeneous ADMET data
- Chemistry design software with real-time ADMET predictions as compounds are drawn
- Software accessible to the entire team with minimal ramp-up time
- Integration of machine learning, medicinal chemistry, synthetic chemistry, and DMPK expertise