ISM9077 was created using Chemistry42, Insilico’s generative chemistry platform, which combines structure-based drug design with machine-learning models for molecular generation, activity prediction and compound optimization. According to the company, researchers first characterized the binding pocket of Target Y using in-house co-crystal structures. Generative models then proposed new chemical structures designed to fit the target while balancing pharmacophore alignment, three-dimensional shape, drug-like properties and molecular novelty.

The discovery program also used an AI model trained to predict target-specific activity, allowing researchers to prioritize compounds before extensive laboratory testing. After successive cycles of synthesis, biological evaluation and optimization, the team selected ISM9077 for its activity across species, permeability and exposure characteristics. Insilico says crystallographic studies supported the proposed binding mode and that the molecule has a novel structure with low patent risk. However, the company has not disclosed the identity of Target Y, leaving the precise molecular pathway behind the program confidential.

In preclinical experiments, ISM9077 showed activity following both oral dosing and topical eye-drop administration. The compound reportedly demonstrated favorable oral bioavailability, low-to-moderate clearance in vivo and retinal concentrations between two and 5.5 times higher than plasma levels. These findings are important for ocular drug development, where the blood-retina barrier can restrict delivery to tissues involved in disease. The molecule’s permeability and retinal exposure may support a non-invasive eye-drop formulation, while its oral activity could offer an alternative route for treating conditions involving inflammation beyond the eye.

The strongest results were reported in models of dry AMD, a degenerative retinal disorder and a leading cause of irreversible central vision loss in older adults. Insilico says ISM9077 improved retinal structure and visual function, producing efficacy approximately three times greater than an available therapy on selected study endpoints. The company also reported superior histopathological outcomes, indicating more pronounced improvements in tissue-level disease features. These findings remain limited to animal studies and will require independent validation before the molecule’s relevance to human disease can be established.

In separate uveitis models, ISM9077 reduced ocular inflammation, lowered the release of inflammatory cytokines and helped restore retinal function. Uveitis can damage multiple parts of the eye and, in severe cases, contribute substantially to visual impairment. In dry-eye experiments, the compound increased tear production and reduced corneal inflammation with a rapid onset of action. Insilico reported that these effects exceeded those observed with cyclosporine A, a commonly used treatment for dry eye, although comparisons between preclinical models and clinical standard-of-care medicines should be interpreted cautiously.

The company believes Target Y may connect ocular inflammation with broader biological processes involved in aging. Chronic, low-grade inflammation is implicated in a wide range of conditions, including neurodegenerative disease, inflammatory bowel disease, cardiovascular disorders, metabolic dysfunction-associated steatohepatitis and obesity. By modulating a pathway involved in pathological inflammation, ISM9077 could potentially be developed for multiple indications rather than a single eye disorder. This “dual-purpose” strategy is central to Insilico’s approach of combining disease treatment with interventions aimed at extending healthspan, although evidence for any longevity benefit has not yet been demonstrated in humans.

Insilico also reported a favorable safety profile and a wide safety margin in its preclinical testing. The company has not released detailed toxicology data, exposure limits or the full design of the safety studies, so the significance of the findings cannot yet be independently assessed. Before human trials can begin, ISM9077 will need to undergo formal investigational new drug-enabling studies, including more extensive toxicology, pharmacokinetic characterization, manufacturing development and formulation testing. The feasibility of an eye-drop formulation will depend on factors such as solubility, ocular retention, tissue tolerability and sustained delivery to the retina.

The nomination highlights the growing role of generative AI in early drug discovery, but it also illustrates the distance between computational design and clinical success. Insilico says its programs typically reach preclinical candidate selection within 12 to 18 months, compared with an estimated 2.5 to four years for many conventional early-stage programs, using between 60 and 200 synthesized and tested molecules per program. Of the company’s 32 nominated candidates, 13 have reportedly received investigational new drug approval or clearance. ISM9077 now joins that development pipeline, with its future determined by the rigor of upcoming studies and whether its promising activity in ocular disease models translates into safe and effective treatment for patients.

Subject of Research: AI-designed ISM9077, a potential first-in-class Target Y inhibitor for ocular diseases, inflammatory disorders and aging-related conditions.

Article Title: Insilico Medicine Nominates AI-Designed ISM9077 as Preclinical Candidate for Ocular Disease and Aging-Related Inflammation

Web References: https://www.insilico.com/ ; https://mediasvc.eurekalert.org/Api/v1/Multimedia/79c2d45a-427d-41ce-91f9-fabf5f119cbd/Rendition/low-res/Content/Public

Image Credits: Insilico Medicine

Keywords: Generative AI, artificial intelligence, drug discovery, ISM9077, Insilico Medicine, ocular diseases, dry age-related macular degeneration, dry AMD, uveitis, dry eye disease, inflammation, aging, retinal disease, preclinical candidate, Chemistry42, Target Y, biotechnology, ophthalmology

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