Data from physical experimentation can be fed into models that then predict novel chemical-protein or protein-protein interactions for targets of interest.
Computational chemistry. Early-stage biotechs are researching ML models to bridge the gap between lab experimentation and computer simulation. The hope is to enable a fit-for-purpose chemical and biologics design that supplants inefficient and expensive library screening methods. Data from physical experimentation can be fed into models that then predict novel chemical-protein or protein-protein interactions for targets of interest. Start-ups are designing automated systems that can feed precise biochemical data into models for training. Some approaches are even trying to simulate the entire cellular environment when designing bespoke therapeutic molecules. Series A companies in this space raised more than $350 million in 2022.