Schrödinger, Inc. (Nasdaq: SDGR) presented compelling new preclinical data for two of its investigational oncology compounds at the American Association for Cancer Research (AACR) Annual Meeting 2025 in Chicago, demonstrating the potential of its computational platform to develop differentiated cancer therapeutics.
The presentations featured SGR-3515, a first-in-class Wee1/Myt1 co-inhibitor currently in clinical development, and SGR-4174, a novel SOS1 inhibitor targeting KRAS-driven cancers, both showing promising efficacy and safety profiles in preclinical models.
SGR-3515: Optimized Wee1/Myt1 Co-inhibition
Preclinical data presented for SGR-3515 demonstrated superior anti-tumor activity compared to existing Wee1 inhibitor ZN-c3 and Myt1 inhibitor RP-6306 across multiple tumor models. The poster (Abstract #3025) highlighted how SGR-3515 can be optimized through intermittent dosing schedules to maximize efficacy while minimizing on-target side effects.
Key findings showed that three to five days of dosing within a two-week cycle preserved anti-tumor efficacy while allowing complete recovery from reversible myelosuppression in preclinical models. This optimized dosing approach could potentially address limitations seen with other cell cycle checkpoint inhibitors.
"The preclinical profile of SGR-3515 suggests it may offer advantages over existing Wee1 inhibitors by simultaneously targeting both Wee1 and Myt1, which could potentially overcome resistance mechanisms and expand the therapeutic window," said Karen Akinsanya, Ph.D., President of R&D Therapeutics at Schrödinger.
SGR-3515 also demonstrated synergistic effects when combined with chemotherapy in preclinical models, suggesting potential combination strategies for clinical development.
Novel Machine Learning Approach for Combination Therapy
In a separate presentation (Abstract #3660), Schrödinger showcased a machine learning model that successfully identified both known and novel synergistic drug combinations with Wee1 inhibitors. The model, built on data from large cell line combination screening studies, predicted effective combinations with tyrosine kinase inhibitors in ovarian and breast cancers, and with chemotherapy in head and neck cancers.
This computational approach demonstrates Schrödinger's ability to leverage artificial intelligence to identify promising combination strategies that might otherwise remain undiscovered through traditional methods.
SGR-4174: A Selective SOS1 Inhibitor for KRAS-Driven Cancers
The company also presented its first preclinical characterization of SGR-4174, a selective SOS1 inhibitor designed to disrupt the interaction between SOS1 and KRAS, the most frequently mutated oncogene in human cancers.
Data presented in poster session (Abstract #4376) showed that SGR-4174 demonstrates high selectivity for SOS1 over SOS2 and other kinases, with superior binding, selectivity, and drug-like properties compared to MRTX0902, another SOS1 inhibitor in development.
SGR-4174 showed robust suppression of RAS signaling pathways and potent cell-killing activity across multiple cancer types harboring diverse KRAS mutations, as well as EGFR and SOS1 mutations. In preclinical models of pancreatic and non-small cell lung cancer, SGR-4174 achieved dose-dependent target engagement and tumor growth inhibition as monotherapy, and induced tumor shrinkage when combined with MEK or KRAS inhibitors.
"SOS1 inhibition represents a promising approach for targeting KRAS-driven cancers, which have historically been difficult to treat," explained Dr. Akinsanya. "The preclinical profile of SGR-4174 suggests potential applications not only in cancers such as lung adenocarcinoma but also in RASopathies like Neurofibromatosis Type 1."
Clinical Development Timeline
Schrödinger expects to report initial data from the ongoing Phase 1 clinical trial of SGR-3515 in patients with advanced solid tumors in the second half of 2025. The company has not yet disclosed a timeline for advancing SGR-4174 into clinical development.
Computational Platform Driving Innovation
The preclinical profiles of both SGR-3515 and SGR-4174 highlight the potential of Schrödinger's computational platform to design molecules with differentiated properties compared to existing therapeutics.
"The preclinical data for SGR-3515 and SGR-4174 further demonstrate that molecules discovered and developed by Schrödinger have favorably differentiated molecular profiles compared to existing development-stage molecules," said Dr. Akinsanya. "The preclinical profiles of these development candidates reinforce the power of our computationally-driven approach to designing molecules that meet challenging target product profiles and have the potential for meaningful benefit to patients."
Founded in 1990, Schrödinger has built its computational platform on more than 30 years of R&D investment. The company currently employs approximately 900 people across 15 global locations and is advancing three clinical-stage oncology programs as part of its growing therapeutic pipeline.
About Schrödinger's Technology
Schrödinger's computational platform enables the discovery of novel, highly optimized molecules for drug development through physics-based modeling and machine learning approaches. The platform allows researchers to explore chemical space more efficiently than traditional methods, potentially reducing the time and cost of bringing new therapeutics to patients.
The company's business model includes licensing its software platform to biotechnology, pharmaceutical, and industrial companies while also leveraging the technology to advance its own internal pipeline of drug candidates.
As Schrödinger continues to advance its clinical programs, the data presented at AACR 2025 reinforces the potential of its computational approach to address significant unmet needs in oncology through the development of novel, differentiated therapeutics.
