Most pharmaceutical companies use software to model chemical interactions, with the hope of speeding up the drug development process. But it’s typically a small component of a complex array of approaches. Nimbus Discovery, a startup based in Cambridge, Massachusetts, is using computational chemistry to drive the entire process.
The company emerged from a partnership with Schrödinger, a maker of computational drug discovery software, and venture capital firm Atlas Venture. Nimbus will use Schrödinger’s software, computing power, and modeling experts to develop drugs for disease-linked proteins that have historically been difficult to target.
If successful, this computationally driven approach could make drug development faster and cheaper by making much of the trial and error process virtual. Nimbus recently raised $24 million in venture funding. Bill Gates was one of the investors.
Schrödinger’s software, which is used by many pharmaceutical companies, models the various chemical forces that drive a candidate drug molecule to bind to a specific spot on the target protein. That allows drug developers to predict how well various candidate molecules bind to targets of interest. While this approach has been in use for about two decades, it has yet to truly transform the drug-discovery process.
Nimbus researchers think that part of the reason is that most tools fail to incorporate the thermodynamics of the resident water molecules in the protein’s binding site. “The need for improved water models is a widely acknowledged yet seldom-addressed limitation of current methods,” says Christopher Snow, a postdoctoral researcher at Caltech who is not involved with the company. It’s difficult to model the energy of water molecules.
WaterMap, a new tool from Schrödinger that predicts how water will affect the binding reaction, could overcome that barrier. “We think we can use our technology to transform the way drug development is done,” says Ramy Farid, president of Schrödinger and cofounder of Nimbus. Researchers have used WaterMap to explain the success or failure of some molecules, as well as to develop new candidate molecules. “It led in a number of cases to rapid development of drug candidates that were of higher quality than what appeared to be otherwise possible,” says Farid.