Monica Berrondo
CEO at Macromoltek
Monica Berrondo, Ph.D., is an entrepreneur, scientist, and software engineer. Her expertise lies in using computational approaches to complex scientific problems, in particular, using algorithms for drug discovery and protein design. She is the founder and CEO of Macromoltek, a biotech company using innovative methods to improve research outcomes and reducing the cost associated with drug discovery through computational modeling. Dr. Berrondo began working on the drug discovery process in high school, when she used computational modeling to predict biological processes.
She completed her doctoral degree in Chemical Engineering at the Johns Hopkins University, where she received the NIH’s prestigious Ruth L. Kirschstein fellowship to conduct her doctoral research studying the effects of mutations on protein structure and function.
Dr. Berrondo has authored peer-reviewed scientific articles in journals such as Proteins, JMB, and PLoS One. She has led Macromoltek through Phase I and II SBIR grants from the NSF and the prestigious Y Combinator accelerator in Winter 2018. Dr. Berrondo strives to build a better world by bringing together a multi-disciplinary team to work on big problems.
Title: Taking antibody candidate generation from the lab to the cloud with de novo computational design
Antibodies are a therapeutic class of molecules with enormous potential, but the path to the clinic is beset by pitfalls related to developability and functionality. Display and immunization-based methods can often create high-affinity binders that struggle with aggregation, immunogenicity, and with limited mechanisms for targeting, these candidates often fail to have any functional impact on the target. Furthermore, large libraries of antibodies must be tested and filtered without clear information of whether they bind functionally relevant epitopes. Here we present a de novo computational design platform that integrates physics-based structural modeling algorithms with machine learning to create targeted candidate therapeutics that can be quickly validated in high-throughput systems in the lab.