I-Corps: Application of deep generative models for simulating biological systems

The broader impact/commercial potential of this I-Corps project is the development of an artificial intelligence (AI) based platform for simulated biological systems that can predict the true phenotypic outcome of any perturbation prior to wet lab experimentation. The development of the proposed technology addresses the need for a highly predictive, efficient, and cost-effective platform that has potential applications in drug discovery, gene therapy and personalized medicine in the biopharmaceutical industry.

Traditionally, the development of drugs, vaccines, and therapies is carried out in biological wet lab settings from preclinical cell and animal models to clinical phase human trials. This research is often expensive, requires years of effort, and can struggle to achieve suitable efficacy. The proposed technology may offer prediction of biochemical changes with high accuracy and maximum efficacy and safety, thereby reducing the burden on payers and stakeholders.

This I-Corps project leverages artificial intelligence (AI) and machine learning (ML) through deep generative models (DGMs) to accelerate prediction of phenotypic outcomes in biological systems. Deep neural networks combined with progress in stochastic optimization methods have enabled scalable modeling of complex, high-dimensional data and has become the often preferred artificial intelligence method in computer vision, speech and natural language processing, graph mining, and reinforcement learning.

However, there are few examples of its application to biological systems. This project combines the biological processes and DGMs to train the deep neural network in characterizing the phenotype unique to each process. The trained DGMs can then predict the best outcome for any perturbation to these processes in any given environment.

The proposed technology is reproducible and scalable, and designed to provide high-content structural, phenotypic, and morphological profiles of the effects of biological and pharmacological substances.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.