Insilico Medicine announced on Apr. 9 that its research titled "An Internal Sulfur-Lone Pair Interaction Enabled the Discovery of Potential and Sub-Family Selective PKMYT1 Inhibitors" has been published as a cover feature in ChemMedChem.
The study is significant for oncology because it addresses the challenge of developing selective inhibitors for PKMYT1, a kinase involved in aggressive cancers with CCNE1 amplification. While targeting PKMYT1 has shown promise in synthetic lethal cancer therapy, current drugs have struggled to avoid off-target effects that can lead to side effects and limit dosing.
According to Insilico Medicine, their team used an AI-powered generative chemistry platform called Chemistry42 to design new small molecule inhibitors and highly selective PROTACs. The company said these efforts led to the development of compounds like A4 and its enantiomer A4-ent1, which showed promising selectivity and activity profiles. The key innovation was using noncovalent sulfur–lone pair interactions between specific atoms in the drug structure, which allowed precise control over molecular geometry without relying on traditional hydrogen-bonding strategies.
The company said this approach not only improved selectivity but also enhanced drug-like properties such as permeability and solubility by masking hydrogen-bond donors. This marks a shift toward more rational molecular design using underutilized chemical interactions.
Since its founding, Insilico has published over 200 peer-reviewed papers and achieved six publications in Nature Portfolio journals since 2024. The company also ranked among the Top 100 global corporate institutions for biological sciences publications according to Nature Index's "2025 Research Leaders." Insilico stated that by using advanced AI and automation technologies, it has reduced early-stage drug discovery timelines from several years down to just 12-18 months per program while synthesizing far fewer molecules than traditional methods.
Looking ahead, Insilico Medicine plans to continue leveraging artificial intelligence and automation technologies in drug discovery programs.
