1. Combining Biology/Chemistry-Based Priors with Machine Learning
Research Overview
In the quest for groundbreaking advancements in molecular science, leveraging the synergy between biology, chemistry, and machine learning is a promising frontier. Traditional machine learning models, particularly deep neural networks, have shown remarkable capabilities in interpolating within large datasets. However, their performance often diminishes when applied to novel scenarios due to a lack of inherent domain-specific knowledge. In the fields of biology and chemistry, we possess rich, physics-based priors—such as functional forms, symmetries, and statistical behaviors—that are grounded in well-established scientific principles.
Our research aims to harness these biology and chemistry-based priors to enhance the predictive power and generalization of machine learning models. By embedding these strong priors into our algorithms, we can create more accurate and efficient models that simulate complex molecular interactions and dynamics. This approach not only bridges the gap between empirical data and theoretical models but also accelerates the discovery and optimization of new molecules and materials.
The integration of domain-specific knowledge with advanced machine learning techniques promises to revolutionize the way we understand and manipulate molecular systems, paving the way for significant innovations in drug discovery, materials science, and beyond.
2. Artificial Intelligence-driven Protein Design and Engineering (AIPDE)
Research Overview
AIPDE combines cutting-edge computational tools and AI algorithms to design novel proteins with specific functions and properties, enabling the creation of proteins that do not exist in nature. By integrating AI-driven approaches with computational platforms like Rosetta, we are able to design proteins from scratch or optimize existing ones for targeted applications, such as molecule binding or catalysis. This approach allows for the efficient design of entirely new protein structures, addressing complex challenges in synthetic biology, drug discovery, and material science.
Our research focuses on developing AI models that not only predict protein structures and functions but also guide the synthesis of proteins with tailored properties. By combining computational protein design with experimental validation, we aim to streamline the process of de novo protein engineering, reducing the time and cost involved in developing innovative proteins for therapeutic, industrial, and biotechnological applications.
Through AIPDE, we aspire to revolutionize the field of protein engineering, creating versatile, customizable proteins that can be used in a wide range of applications, from targeted drug delivery to renewable energy solutions. This integrated approach holds the potential to unlock new frontiers in biotechnology and precision medicine, offering solutions to some of the world’s most pressing scientific and health challenges.
3. Artificial Intelligence-driven Drug Discovery (AIDD)
Research Overview
AIDD leverages vast amounts of chemical and biological data to build predictive models that can identify promising drug candidates more efficiently and accurately. By integrating AI with existing data, we can better predict compound performance, optimize molecular properties, and streamline the drug discovery pipeline. This approach minimizes human bias, reduces the need for manual intervention, and accelerates the hit-to-lead and lead optimization stages, significantly cutting down the time and cost required to bring new drugs to market.
Our research focuses on developing sophisticated AI models that can navigate the complexities of small molecule discovery, including handling the vast chemical space, predicting biological activities, and optimizing synthesis routes. We aim to create a seamless integration of computational design and experimental validation, paving the way for more effective and rapid drug discovery processes.
Through AIDD, we aspire to transform drug discovery into a more data-driven, efficient, and scalable endeavor, ultimately leading to the faster development of innovative therapies and improved patient outcomes. This approach not only enhances our ability to discover new drugs but also opens up new possibilities for personalized medicine and targeted treatments, addressing some of the most challenging health issues of our time.
4. Biomaterials Design
Research Overview
We focus on developing high-performance biomaterials for various applications, including drug delivery systems, tissue engineering scaffolds, and biosensors. Our approach combines domain-specific knowledge with cutting-edge machine learning to create robust, predictive models that guide the design of new materials. This synergy not only accelerates the discovery and optimization of biomaterials but also ensures their safety and efficacy in real-world applications.
Through our work in biomaterials design, we aim to push the boundaries of what is possible, creating innovative solutions that address critical challenges in healthcare and beyond. Our ultimate goal is to develop biomaterials that improve patient outcomes, enhance the quality of life, and contribute to sustainable technological advancements.
