RNA Modeling and Lead Discovery

Structure-guided RNA discovery

RNA is a dynamic and challenging target class. MolSoft ICM gives discovery teams an integrated environment for RNA ensemble modeling, binding-pocket analysis, flexible docking, virtual screening, and protein-RNA docking—helping turn structural uncertainty into practical, testable design hypotheses.

Ligand-guided RNA modeling for splice modulation

MolSoft ICM’s Alibero Method helps identify the RNA conformations most relevant to ligand recognition. In work reported by Novartis scientists, ICM was used to evaluate an ensemble of 150 RNA models and identify the structure that best differentiated known binders from non-binders. The resulting model supported a mechanistic hypothesis for small-molecule stabilization of the SMN2 RNA duplex—an important step toward rational RNA-targeted design.

Read the Nature Chemical Biology publication

PocketFinder and docking for RNA-targeted lead optimization

MolSoft ICM supports a complete structure-guided workflow for RNA ligand discovery. In the PEARL-seq study from Arrakis Therapeutics, ICM PocketFinder identified candidate small-molecule binding pockets on RNA, while flexible ligand docking generated a structural model consistent with experimental cross-linking and structure-activity data. This integrated approach helped translate experimental data into actionable hypotheses for optimizing RNA-targeted small molecules.

Read the ACS Chemical Biology publication

RNA docking and virtual screening

ICM enables virtual screening against RNA structural ensembles, allowing teams to search large chemical libraries while accounting for RNA flexibility. Published studies have shown how ICM-based screening can identify small molecules that bind HIV-1 TAR RNA and inhibit its interaction with the Tat peptide—demonstrating the value of structure-guided RNA hit discovery.

Read the Journal of Computer Aided Molecular Design publication

Protein-RNA docking built into ICM

MolSoft also provides protein-RNA docking capabilities within ICM. The method combines rigid-body docking with RNA-aware electrostatic scoring, optimized rescoring, and contact-fingerprint clustering. In a published benchmark, this approach achieved a top-100 success rate of 0.66 across protein-RNA complexes.

Read the Journal of Chemical Theory and Computation publication