I-Corps: Developing predictive computational methods for drug development

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The broader impact/commercial potential of this I-Corps project is to provide the pharmaceutical and biotechnology industries with a software or service that generates accurate predictions for experiments across the life sciences. Drug discovery, biologics design, and other industrial biotechnological efforts often require billions of dollars and decades of research to see projects to completion. Although there has been a major focus over the past two decades to accelerate the drug discovery process using computational tools, the efficacy of existing technologies has typically been limited to a select few experiments which have large amounts of curated data. The proposed technology can provide accurate predictions for experiments across the preclinical discovery space, addressing several steps outside the purview of other contemporary technologies. The flexibility of this technology allows for several time and resource intensive steps to be performed by computer to increase experimental throughput, while allowing researchers to focus on experiments with a high probability for success. This opportunity for improved time and resource management may accelerate the timeline for drugs to enter the clinic and may invigorate therapeutic efforts towards underserved diseases.This I-Corps project will focus on identifying which experiments within the pharmaceutical and biotechnology space are underserved by current computational methods, allowing for these bottlenecks within drug discovery to be addressed. This technology makes use of a novel approach for generating artificial intelligence (AI) models employing information from biophysical simulations. This AI method significantly reduces the amount of data required for generating accurate predictions and has an increased scope of utility compared with traditional AI methods. Proof of concept has been demonstrated through several retrospective studies which demonstrated the technology's ability to provide accurate predictions toward small molecule-protein interactions, protein-protein interactions, therapeutic peptide stabilization, and more. Further exploration of the technology requires insight from potential customers in the pharmaceutical and biotechnology space to understand the specific experiments that this technology needs to address to inform future prospective studies.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.