Schrödinger’s Statement Regarding FDA Plan to Phase Out Animal Testing Requirement for Monoclonal Antibodies and Other Drugs

April 14, 2025 at 08:00 AM EDT

Schrödinger, Inc. (Nasdaq: SDGR) strongly supports the U.S. Food and Drug Administration’s (FDA) plan to reduce, refine or potentially replace current animal testing requirements with new approaches designed to improve drug safety and accelerate the evaluation process, while reducing animal experimentation. The FDA’s roadmap encourages a number of computational approaches to predict drug properties. Schrödinger’s widely used computational platform enables highly accurate in silico predictions of key molecular properties for small molecules and biologics and has broad application across all biological targets.

“The role of computational methods is changing rapidly in the pharmaceutical industry, and it is exciting to see these methods recognized as a powerful solution for optimizing drug candidates for both efficacy and safety,” stated Ramy Farid, Ph.D, chief executive officer at Schrödinger. “We have been pioneering computational molecular discovery for nearly 35 years and continue to develop new solutions that integrate physics with AI/machine learning to accelerate drug discovery, reduce development risk and lower costs. Importantly, this includes advancing our predictive toxicology initiative. We believe our computational solutions will play a vital role in significantly reducing the use of animal models in preclinical development.”

In 2024, Schrödinger announced a major initiative, funded by a grant from the Bill & Melinda Gates Foundation, to predict toxicology risk early in drug discovery. The goal of the initiative is to develop a computational solution designed to improve the properties of novel drug development candidates and reduce the risk of development failure associated with binding to off-target proteins. Schrödinger has already generated computational predictive models for a number of key off-targets. The company’s recent advances characterizing the structure of safety-related proteins such as hERG (recently published in Cell) and cytochrome P450 enzymes are examples of these efforts. The company expects to launch its predictive toxicology solution to customers in the second half of 2025.

Additionally, several computational solutions for small molecule and biologics drug discovery are already available as part of Schrödinger's platform, including solutions to predict protein aggregation, which is critical for assessing developability and potential immunogenicity of biologics, as well as methods for predicting binding affinity, which can be used to evaluate selectivity and the potential for off-target interactions.

About Schrödinger

Schrödinger is transforming molecular discovery with its computational platform, which enables the discovery of novel, highly optimized molecules for drug development and materials design. Schrödinger’s software platform is built on more than 30 years of R&D investment and is licensed by biotechnology, pharmaceutical and industrial companies, and academic institutions around the world. Schrödinger also leverages the platform to advance a portfolio of collaborative and proprietary programs and is advancing three clinical-stage oncology programs. Founded in 1990, Schrödinger has approximately 900 employees operating from 15 locations globally. To learn more, visit www.schrodinger.com, follow us on LinkedIn and Instagram, or visit our blog, Extrapolations.com.

Cautionary Note Regarding Forward-Looking Statements

This press release contains forward-looking statements within the meaning of The Private Securities Litigation Reform Act of 1995 including, but not limited to those statements regarding the potential advantages of Schrödinger’s computational platform, the long-term potential of its business, its ability to improve and advance the science underlying its platform, including the ability to predict off-target activity, its ability to improve drug discovery and the timing during which the initiative’s technology will become available to software customers and collaborators. Statements including words such as “aim,” “anticipate,” “believe,” “contemplate,” “continue,” “could,” “estimate,” “expect,” “goal,” “intend,” “may,” “might,” “plan,” “potential,” “predict,” “project,” “should,” “target,” “will,” “would” and statements in the future tense are forward-looking statements. These forward-looking statements reflect Schrödinger’s current views about its plans, intentions, expectations, strategies and prospects, which are based on the information currently available to the company and on assumptions the company has made. Actual results may differ materially from those described in these forward-looking statements and are subject to a variety of assumptions, uncertainties, risks and important factors that are beyond Schrödinger’s control, including the uncertainties inherent in drug development and commercialization, such as the demand for its software platform, its ability to further develop its computational platform, its reliance on third-party providers of cloud-based infrastructures to host its software solutions, the conduct of research activities and the timing of and its ability to initiate and complete preclinical studies and clinical trials, whether results from preclinical and early clinical studies will be predictive of the results of later preclinical studies and clinical trials, factors adversely affecting the life science industry and other risks detailed under the caption “Risk Factors” and elsewhere in the company’s Securities and Exchange Commission filings and reports, including its Annual Report on Form 10-K for the fiscal year ended December 31, 2024, filed with the Securities and Exchange Commission on February 26, 2025, as well as future filings and reports by the company. Any forward-looking statements contained in this press release speak only as of the date hereof. Except as required by law, Schrödinger undertakes no duty or obligation to update any forward-looking statements contained in this press release as a result of new information, future events, changes in expectations or otherwise.