In 2023, we worked with an oncology-focused company to improve the potency of their pre-clinical assets. This project required docking, high-level quantum mechanics (QM) calculations, and medicinal/synthetic chemistry. Some of our work is showcased below:

We began by analyzing the company’s structural and experimental data, which contained several inhibitors targeting a key enzyme involved in cancer. The mechanism of inhibition of these ligands, which bind covalently to the target, was an added challenge. We first determined the probable binding modes of these compounds inside the enzyme active site, using our Forecaster tools. We established the relative contributions of contacted amino acids to the binding site to point us in the right direction to improve potency.

Next, we used QM to determine the binding kinetics of the covalent warheads. This was a non-trivial step; the processes of ligand pre-orientation in proximity to the covalent residue and of bond breaking/formation are highly complex. We followed this binding process across several hundred infinitesimal steps and computed key energy terms at every step. Ultimately, we were able to predict in detail how these ligands bind to their target and to correlate the obtained energies with high quality IC₅₀ data that was previously obtained.

With these reference data in hand, we proposed several modifications to the ligands aimed at improving their potency by 1) forming stronger interactions with the key amino acids identified, 2) binding more tightly/quickly to the covalent residue, or 3) having an increased residence time inside the enzyme active site. For each modification we proposed, we obtained the same binding kinetics data as for the reference ligands. Then, together with the company’s scientists, we shortlisted the most promising candidates for testing. Consistent with our docking and QM calculations, several of the proposed ligands showed improved affinity towards the target and a substantial improvement in terms of in vivo performance.

Beyond the fruitful and frequent communication between our teams, we bridged diverse knowledge and skillsets from computational and experimental backgrounds, and from a range of chemistry and biochemistry principles to develop high quality analogues that met the project objectives. We thank our partners for their trust and for placing their faith in us in this project.