Chemical Space Navigator
This project introduces the "Chemical Space Navigator," a novel AI-based process developed by DeepLS for optimizing pharmaceutical drug candidates. By digitizing significant portions of the traditionally iterative and lab-intensive process, it aims to save costs, time, and reduce chemical waste. This project will enhance the existing GeneSys platform, targeting the optimization phase of drug development. This project is financially supported by the State of Hesse under the Distr@l program.
Key Points
Challenge and Objective:
Drug discovery is a lengthy and costly process, often taking up to 15 years and $2.5 billion.Current processes depend heavily on in vivo and in vitro experiments, which are environmentally damaging and resource-intensive.DeepLS aims to streamline the optimization of drug candidates using AI, making the process faster, cheaper, and more sustainable.
Innovative Approach:
The Chemical Space Navigator leverages large pre-trained language models to optimize drug candidates in silico, reducing the need for in vitro experiments.It focuses on ADMET properties (Absorption, Distribution, Metabolism, Excretion, and Toxicity) to ensure drug candidates meet all necessary criteria.DeepLS excels with its innovative platform, GeneSys, which has already successfully digitized the identification of drug candidates. The Chemical Space Navigator builds on this foundation by optimizing these candidates, significantly enhancing process efficiency.
Conclusion and Recommendations:
Our Focus on Drug Candidate Optimization: The Chemical Space Navigator represents a logical extension of Deep LS’s product portfolio, targeting a critical phase in drug development.
We Leverage AI Capabilities: By continuing to harness the power of AI and large language models, Deep LS can provide unparalleled optimization services, enhancing the drug discovery pipeline.