19th German Conference on Cheminformatics 2026

Mitigating off-target liabilities using physics-based simulations

Speaker:
Daniel Cappel, Senior Principal Scientist, Applications Science, Schrödinger

Abstract:
By one estimate, unmanaged toxicity is responsible for roughly 30%[1] of all drug discovery project failures. The adoption of experimental screening panels has contributed to the overall improved safety profile of drugs on the market. However, the high cost and latency associated with performing these screens mean that such panels are run later in the pre-clinical discovery process and cannot be effectively incorporated into hit finding and lead-optimization stages of the project. To meet the demand for off-target screening during the design process, many teams deploy digital toxicology screening in the form of ligand-based machine learning models. These models, which are fast and inexpensive to operate are typically limited by poor generalizability to ligand matter dissimilar from the data used to train the models and lack the protein context to help designers rationally dial out liabilities.

Here we present a novel in-silico, physics-based approach that constructs a full 3D, atomistic representation of the ligand interacting with the off-target using induced-fit docking and molecular dynamics simulations and leverages free energy calculations to model off-target binding affinity. This workflow has been applied to a wide range of targets across different protein classes (kinases, hERG or CYPs).[2] Recently this has been extended to nuclear receptor RXRɑ and retrospectively validated with literature data.