Presentation
Generalized Generative Molecular Design: An open source, Modular Tool for De Novo Drug Design
DescriptionThe chemical space of small molecules is vast, making de novo drug design challenging. Traditional methods are slow and costly. While AI advancements have improved this process, we still face limitations in exploring the larger chemical space. In oncological drug discovery, various factors such as selectivity, efficacy, safety, toxicity, and synthesizability must be considered.
We introduce the Generalized Generative Molecular Design (GGMD), an open-source tool that combines generative AI with population-based optimization algorithms for drug design and lead optimization. GGMD’s modular and customizable framework allows users to adjust methods to fit specific research needs, balancing trade-offs like efficacy and synthesizability. Designed for accessibility, GGMD is transportable and provides tools for visualizing results and refining parameters.
We’ve successfully used GGMD to optimize properties such as LogP and toxicity, leading to the discovery of new molecules.
We introduce the Generalized Generative Molecular Design (GGMD), an open-source tool that combines generative AI with population-based optimization algorithms for drug design and lead optimization. GGMD’s modular and customizable framework allows users to adjust methods to fit specific research needs, balancing trade-offs like efficacy and synthesizability. Designed for accessibility, GGMD is transportable and provides tools for visualizing results and refining parameters.
We’ve successfully used GGMD to optimize properties such as LogP and toxicity, leading to the discovery of new molecules.
