(709h) Data-Driven Discovery and Optimization of Small Molecules for Immune Modulation Via Machine Learning, Molecular Docking Simulation and Experiment

Innate immune response plays a key role in protecting the body against pathogens and initiating the early stages of defense during infections. Therefore, precise modulation of the innate immune response is critical for maintaining effective pathogen protection while preventing excessive inflammation or immune-related diseases. This modulation is integral to the success of vaccines, which aim to stimulate protective immune responses with minimal adverse side-effects. Similarly, it is vital for the success of immunotherapies, as it can enhance immune stimulation and counteract the immune suppression within the tumor micro-environment. In our previous work, we have identified a short list of small molecules capable of modulating various innate immune responses by significant margins, up to more than 10-fold, utilizing a machine learning-driven pipeline to guide in vitro experimental screenings. Our next objective is to optimize the chemical structure of these small molecules to enhance their immune modulation capabilities and facilitate formulation into diverse delivery methods.

We have identified several potential targets within the innate immune system that our small molecules could interact with. Utilizing molecular docking simulations, we can simulate and elucidate the interactions between our small molecules and these targets. This enables us to guide the design of chemical modifications aimed at achieving enhanced binding affinity, ultimately leading to more potent immune modulation. By implementing a feedback loop between molecular docking simulations and machine learning, we can systematically explore a diverse range of chemical modifications to our initial hit molecules in an automated and efficient manner. This iterative approach yields modified chemical structures with strong binding affinities to potential immune targets, which can then be subjected to experimental validation. Importantly, our machine learning model incorporates considerations of formulation capabilities, such as synthetic accessibility, as well as water and oil solubility. This allows the model to prioritize modifications that are likely to exhibit favorable deliverability characteristics. By biasing towards molecules with optimal formulation properties, we increase the likelihood of successful experimental validation and subsequent clinical application.

This integrated simulation and machine learning optimization strategy not only accelerates the discovery of promising small molecule immune modulators but also enhances our ability to tailor these molecules for practical use in therapeutic interventions. The synergy between computational simulations, machine learning, and experimental validation represents a powerful paradigm for advancing the development of novel vaccine adjuvants and immunotherapeutic strategies, ultimately aiming to improve immune responses against pathogens and tumors while ensuring safety and efficacy in clinical applications.