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Schrödinger Launches Major Initiative To Enhance Predictive Toxicology Capabilities

Schrödinger improves drug safety with AI-enhanced computational toxicology tools.

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  • Jul 29, 2024

  • Mrudula Kulkarni

Schrödinger Launches Major Initiative To Enhance Predictive Toxicology Capabilities

Schrödinger, Inc., known for its innovative physics-based computational platform that revolutionizes therapeutic and material discovery, has announced a new initiative aimed at enhancing its platform to assess toxicology risks in the early stages of drug discovery. This effort seeks to create a computational tool that will improve the characteristics of new drug candidates and mitigate the risk of development failures linked to off-target protein interactions, which can lead to significant adverse effects. This initiative expands upon Schrödinger’s existing “predict first” digital laboratory, utilizing its physics-driven platform alongside NVIDIA’s AI technologies.

Implementing predictive toxicology broadly could expedite the transition from target identification to viable drug candidates, significantly lowering the chances of toxicity-related setbacks during preclinical or clinical trials. Safety concerns are a common cause of delays and failures in drug development and are a focal point of the U.S. Food and Drug Administration's Predictive Toxicology Roadmap.

The first year of this project will be supported by a $10 million grant from the Bill & Melinda Gates Foundation, aimed at accelerating the scientific research integral to this initiative. Once the technology is fully developed, it will be made available to the Gates Foundation’s grantees globally, facilitating the rapid advancement of new treatments for diseases that disproportionately impact individuals in low- and middle-income countries. Additionally, these tools will be accessible to Schrödinger’s software clients and will enhance the company’s proprietary drug discovery programs and partnerships.

Ramy Farid, Ph.D., Chief Executive Officer at Schrödinger, said in a statement, “Drug discovery is an extremely challenging endeavor, and off-target toxicity is a significant cause of drug development failure. The application of our technology, at scale against a broad panel of known off-target proteins, has the potential to prevent a significant number of these failures, reducing the potential for safety issues in preclinical and clinical research, and lowering the cost and risk of drug development.”

He further added, “ Advances in structural biology and computer performance, coupled with the increasing accuracy of our computational platform, gives us a unique opportunity to address the need for high quality computational models for predictive toxicology. We appreciate the support from the Gates Foundation, which allows us to immediately scale up our efforts advancing this initiative.”

“Optimizing the safety profile of drug candidates is one of the most difficult challenges in drug discovery, and computational approaches have the potential to revolutionize the way we discover drugs by enabling the prediction of drug toxicity with unprecedented accuracy and efficiency prior to clinical testing. Leveraging computation to predict the toxicological risk of drug candidates could ultimately improve productivity across the pharmaceutical industry and unlock major advances against diseases that continue to plague low- and middle-income countries,” said Trevor Mundel, president, global health, at the Gates Foundation.

Kimberly Powell, the Vice President of health at NVIDIA, also added, “With its world-leading physics-based platform, Schrödinger has spearheaded the last two decades of computational drug and materials discovery. Accelerated computing, which introduces many orders of magnitude in discovery power, combined with generative AI will enhance researchers’ abilities to tackle complex scientific challenges like predictive toxicology, leading to faster, more efficient and effective discovery cycles and transformative medicines for patients.”

Schrödinger has developed multiple computational models that predict off-target activity of drugs. Notable achievements include their recent work on safety-related proteins like hERG, detailed in a publication in Cell, and their studies on cytochrome P450 enzymes, showcasing the company’s commitment to enhancing drug safety through advanced structural characterization.

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