In a significant breakthrough that harnesses the cutting-edge capabilities of artificial intelligence (AI) in drug discovery, Insilico Medicine, a leading clinical-stage biotechnology company, has announced the development of novel pan-KRAS inhibitors featuring new chemotypes. These inhibitors were designed through an intricate interplay of structure-based drug design, scaffold hopping, and advanced molecular modeling techniques, empowered distinctly by Chemistry42, Insilico’s proprietary generative chemistry platform. This milestone marks a defining moment in overcoming the longstanding challenges posed by targeting KRAS, an oncogene long deemed "undruggable" due to its biophysical characteristics and elusive binding pockets.
KRAS mutations are among the most prevalent oncogenic drivers implicated across a broad spectrum of human cancers. The protein regulates critical cellular processes by orchestrating proliferation and survival pathways, making it a prime therapeutic target. However, the development of inhibitors capable of robustly modulating pan-KRAS activity has been hindered for decades by the exceptionally high affinity of KRAS for guanine nucleotides (GDP/GTP) and the scarcity of well-defined, druggable pockets on its surface. These properties have rendered conventional small-molecule approaches largely ineffective, spurring demand for novel strategies that can circumvent these biological constraints.
Recognizing this challenge, the team at Insilico Medicine applied their AI-driven platform, Chemistry42, to design a diverse set of compound libraries featuring unique central cores or chemotypes. The generative chemistry models embedded within Chemistry42 span over 40 distinct generative frameworks, integrating diverse AI architectures to propose molecular structures with tailored physicochemical and biological properties. This exhaustive exploration enabled scaffold hopping—a process of identifying and substituting core scaffolds of molecules—allowing the team to traverse chemical space beyond what traditional medicinal chemistry methods might achieve.
The initial phase of the project began with a precursor molecule exhibiting selectivity toward a specific KRAS mutant variant. From this starting point, the design process expanded swiftly, leveraging virtual screening modules of Chemistry42 that integrated rigorous structure-activity relationship (SAR) evaluations. In silico modeling refined potential candidates by simulating molecular interactions within the KRAS binding landscape, and iterative optimization was applied meticulously to the amino side chains to optimize binding affinity and selectivity profiles.
Following computational refinement, synthetic chemistry and biochemical assays validated the AI-generated candidates. The selected compounds demonstrated pan-KRAS inhibition with potencies measured in the upper nanomolar range, a noteworthy achievement given the high bar for KRAS-targeting agents. Importantly, these inhibitors exhibited mild selectivity favoring KRAS mutants over wild-type variants—up to a 4-fold difference in potency—signifying potential therapeutic indices that could mitigate off-target effects on normal cells. Furthermore, cell-based assays confirmed robust inhibitory activity in KRAS mutant tumor cell lines, emphasizing translational promise.
Metabolic profiling at this stage revealed the inhibitors possessed acceptable cytochrome P450 (CYP) inhibition profiles, thereby reducing concerns of adverse drug-drug interactions in vivo. This pharmacokinetic property is crucial for clinical development, where metabolic stability and interaction potential directly influence dosing, safety, and efficacy. The integration of AI-enabled predictive models in Chemistry42 likely expedited this aspect by foreseeing and flagging metabolic liabilities prior to synthesis.
Alex Zhavoronkov, PhD, Founder and CEO of Insilico Medicine, highlighted the transformative role of AI throughout this research. He emphasized how the convergence of generative chemistry and sophisticated molecular modeling has enabled the navigation of complex target landscapes, especially for proteins like KRAS that were once deemed refractory to drug discovery. This synergy underscores a paradigm shift where artificial intelligence is not merely a supplementary tool but a driving force accelerating the transition from computational concepts to tangible clinical candidates.
The research draws its lineage from Insilico’s foundational work dating back to 2016, when the company first articulated the concept of using generative AI to design novel therapeutic molecules in a peer-reviewed article. This pioneering vision laid the groundwork for the development of the Pharma.AI platform, which has since evolved into a comprehensive generative AI-powered ecosystem spanning biology, chemistry, medicine development, and research science. To date, Insilico Medicine has contributed over 200 scientific publications and holds more than 600 patents and patent applications, emphasizing its leadership at the intersection of AI and biotechnology.
By applying this sophisticated platform, Insilico Medicine is pushing the boundaries of what is achievable in medicinal chemistry, setting a new benchmark for computationally driven drug design. The successful development of pan-KRAS inhibitors of novel chemotypes signals a turning point in the fight against cancers driven by KRAS mutations. It also showcases the vital role AI can play in unlocking targets traditionally considered “undruggable,” potentially transforming treatment modalities across oncology and beyond.
As these novel inhibitors advance through preclinical validation, they represent promising candidates not only for therapeutic intervention but also as a testament to the growing impact of AI-driven approaches in modern drug discovery. The ability to integrate deep learning, molecular simulations, and automated synthesis in a tightly coupled pipeline may shorten drug development timelines, reduce costs, and increase the success rates of novel therapeutics.
Moreover, Insilico’s work hints at future applications where generative AI platforms might tackle an even wider array of targets with similar complexities and challenges. The collaboration between human expertise and AI augments medicinal chemistry with unparalleled speed and precision, potentially ushering in a new era of personalized and precision medicine.
For the broader scientific community, this success serves as a clarion call to embrace AI methodologies as not only complementary but essential in reimagining drug discovery workflows. Insilico Medicine’s Chemistry42 platform continues to evolve, integrating the latest technological innovations and expanding its reach across diverse therapeutic areas such as fibrosis, immunology, pain, obesity, metabolic disorders, and even novel fields like advanced materials and agriculture.
In essence, this breakthrough marks a remarkable convergence of computational chemistry, molecular biology, and artificial intelligence, delivering tangible progress against a historically elusive target and illuminating the pathway for future innovations in cancer therapeutics.
Subject of Research: Development of novel pan-KRAS inhibitors using generative AI-driven structure-based drug design and scaffold hopping.
Article Title: Identification of novel pan-KRAS inhibitors via Structure-Based drug design, scaffold hopping, and biological evaluation.
Web References:
https://pubs.acs.org/doi/10.1021/acsmedchemlett.5c00080
https://www.insilico.com
References:
Aladinskiy, V. et al. (2025) ‘Identification of novel pan-KRAS inhibitors via Structure-Based drug design, scaffold hopping, and biological evaluation,’ ACS Medicinal Chemistry Letters [Preprint].
Image Credits: Insilico Medicine
Keywords:
Generative AI, pan-KRAS inhibitors, medicinal chemistry, artificial intelligence, molecular modeling, scaffold hopping, drug discovery, oncology, druggable targets, structure-based design
