pharmacophore model

PD-1/PD-L1 are key immune checkpoint targets that suppress T-cell-mediated anti-tumor immunity, representing a major focus in cancer immunotherapy. While antibody drugs dominate the clinic, they are limited by administration challenges and immune-related side effects. Small-molecule PD-1/PD-L1 inhibitors, with oral availability, good tissue penetration and low cost, have emerged as a promising next-generation strategy.

A PD-1/PD-L1 lead-like library was built via a five-step virtual screening process. After collecting 8,947 inhibitors from BindingDB and PubChem and filtering by activity and duplicates, AI similarity screening was performed using GeminiMol. Key pharmacophores were extracted from the PPI interface of co-crystal structures, and molecular was screened via a pharmacophore model, effectively enhancing target activity.

Containing 10,000 structurally diverse and drug-like molecules well-matched to the PD-L1 pocket, the library supports virtual docking, high-throughput screening and hit discovery, enabling efficient and rapid development of small-molecule immunotherapies.

Ion channels are key proteins on the cell membrane that regulate the flow of ions across membranes. They participate in nearly all physiological processes, including nerve conduction, muscle contraction, heart rhythm, and pain perception. Abnormalities in their function can lead to various serious diseases such as arrhythmia, epilepsy, hypertension, neuropathic pain, and cancer. Therefore, ion channels are highly valuable drug targets—over 15% of approved drugs target ion channels currently, demonstrating their irreplaceable therapeutic value in cardiovascular, neurological, and analgesic fields.

MCE has collected a library of over 5,000 reported ion channel-related bioactive compounds targeting major sites such as Na+ channels, K+ channels, Ca2+ channels, GABA receptors, iGluRs, and others. Using AI models, these compounds are characterized through both 2D representations (molecular fingerprints, pharmacophores) and 3D representations (3D conformation) to screen for a collection of lead-like compounds highly similar to known active molecules. Additionally, an hERG channel prediction algorithm integrating XGB and ISE mapping strategy is employed to assess and exclude potential cardiotoxicity in the library.. This step significantly reduces safety risks in subsequent screenings, particularly for ion channel drug development related to cardiovascular systems (e.g., Nav1.5, Cav1.2), effectively minimizing failures due to hERG inhibition and serving as a valuable tool for ion channel drug screening.