CAMBRIDGE, Mass., Aug. 12, 2026 /PRNewswire/ -- Insilico Medicine ("Insilico"; HKEX: 3696), a clinical-stage generative artificial intelligence (AI)-driven drug discovery company, today announced the nomination of ISM0900, a potent, AI-driven, highly selective, orally available small molecule inhibitor of lipoprotein(a), or Lp(a), as a preclinical candidate (PCC) for the management of cardiovascular risks. Notably, this marks the company's 9th PCC nomination announced since 2026, showcasing the enhanced efficiency and scalability that generative AI brings to early-stage drug discovery.
Empowered by Insilico's proprietary generative AI platform, ISM0900 was designed with a novel structure to selectively block the assembly of Lp(a), stopping the formation from the beginning. According to research results, Lp(a) is an established independent risk factor for atherosclerotic cardiovascular disease (ASCVD) and major adverse cardiovascular events, and elevated Lp(a) is estimated to affect approximately 20% of the global population.
Despite growing recognition of its role in disease progression, there are currently no approved therapies specifically designed to lower Lp(a). As the global dyslipidemia market is projected to reach approximately $18.7 billion by 2032, small molecule inhibitors of Lp(a), especially those in the oral formation, hold significant opportunities while targeting a huge unmet need in the clinical setting.
"ISM0900 represents an exciting advance in our cardiovascular pipeline, combining a novel AI-enabled structure with strong preclinical data in potency, selectivity, safety, and oral pharmacokinetics," said Feng Ren, PhD, Co-CEO and Chief Scientific Officer of Insilico Medicine. "More importantly, the formation as an oral therapy may also offer important advantages for patients, including convenience and improved adherence."
The development of ISM0900 was enabled by Chemistry42, Insilico Medicine's proprietary generative chemistry platform, which integrates more than 40 carefully selected models and multiple supporting application modules. First, Insilico's R&D team uploaded the crystal structure of the target–small molecule complex to the platform to analyze key binding sites and interaction patterns in detail. The team then leveraged Chemistry42's built-in model assembly to perform de novo fragment generation and structural reconstruction, while systematically optimizing the molecules for pharmacophore fit, drug-likeness, three-dimensional conformational plausibility, and structural novelty. This process ultimately led to the discovery of ISM0900, a highly selective candidate molecule.
In preclinical studies, ISM0900 demonstrates potent and selective inhibition of Lp(a) assembly at nanomolar-level activity, along with more rapid onset in animal models, excellent oral pharmacokinetic properties, and superior efficacy compared to clinical-stage candidates of the same class.
More specifically, ISM0900 achieved an 83.9% reduction in Lp(a), and superior Lp(a) lowering was observed consistently across the study period. At the same dose level, ISM0900 demonstrated superior Lp(a)-lowering efficacy. Also, ISM0900 reached its peak effect by Day 4, signaling an onset time of less than 40% of available clinical-stage candidates.
Moreover, the compound further shows wide safety margins and a projected lower efficacious human dose, positioning it as a potentially differentiated oral therapy for cardiovascular risk reduction in patients with elevated Lp(a).
"Cardiovascular diseases are deeply connected to aging, which is a challenge faced by all of us, and elevated Lp(a) represents an important and insufficiently addressed contributor to that risk," said Alex Zhavoronkov, PhD, Founder and CEO of Insilico Medicine. "By enabling the rapid design and optimization of novel molecules, AI helps us to move faster in discovering new therapies, while improving the probability of success through more options produced by innovation at scale."
As an AI-native biotechnology company, Insilico is redefining the efficiency of preclinical drug development through its advanced AI and automation platform, setting a new standard for the industry. While traditional early-stage drug discovery typically takes 2.5 to 4 years, Insilico has consistently reached preclinical candidate (PCC) nomination in an average of just 12 to 18 months, with only 60 to 200 molecules synthesized and tested per program. Since 2021, the company has nominated 33 PCCs, 13 of which have received IND approval or clearance.
While expanding the practical applications of its technology in drug discovery and life science research, Insilico is also continuously enhancing the performance of its AI platform. Drawing on extensive experience and datasets from its training platform, the company has distilled thousands of benchmarks and integrated them into MMAI Gym. Serving as both a "trainer and benchmark" for scientific AI, MMAI Gym enables organizations to train models for domain-specific reasoning while rigorously evaluating their performance on real-world tasks, advancing the path toward pharma superintelligence. To date, Human Longevity and Liquid AI have collaborated with Insilico, joining as partners of MMAI Gym.
About Insilico Medicine
Insilico Medicine is a pioneering global biotechnology company dedicated to integrating artificial intelligence and automation technologies to accelerate drug discovery, drive innovation in the life sciences, and extend health longevity to people on the planet. The company was listed on the Main Board of the Hong Kong Stock Exchange on December 30, 2025, HKEX:3696.
By integrating AI and automation technologies and deep in-house drug discovery capabilities, Insilico is delivering innovative drug solutions for unmet needs including fibrosis, oncology, immunology, pain, and obesity and metabolic disorders. Additionally, Insilico extends the reach of Pharma.AI across diverse industries, such as advanced materials, agriculture, nutritional products and veterinary medicine. For more information, please visit www.insilico.com.
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SOURCE Insilico Medicine
