Automated relative binding free energy calculations: from SMILES to ΔΔG

In drug discovery, computational methods are a key part of making informed design decisions and prioritising compounds. In particular, optimizing compound affinity is a central concern during the early stages of development. In the last 10 years, alchemical free energy (FE) calculations have transformed our ability to incorporate accurate in silico potency predictions in design decisions, and represent the 'gold standard' for augmenting experiment-driven drug discovery. However, relative FE calculations are complex to set up and require significant expert intervention to prepare the calculation and analyse the results. Additionally, some of the commonly employed simulation engines for FE calculation are provided as closed-source software, and do not allow for fine-grained control over the underlying settings. In this work, we introduce an end-to-end relative FE workflow based on the non-equilibrium switching approach that facilitates calculation of binding free energies starting from SMILES strings. The workflow is implemented using fully modular steps, allowing various components to be exchanged depending on licence availability. Open-source options for ligand parameterization, embedding, docking, perturbation map generation and FE calculation steps are available to make the workflow fully open-source. We further investigate the dependence of the calculated free energy accuracy on the initial ligand pose generated by various docking algorithms. We show that both commercial and open-source docking engines can be used to generate poses that lead to good correlation of free energies with experimental reference data. Incorporating experimental information about binding pose generally leads to a more accurate set of predictions, particularly in more challenging cases. The code is freely available at https://github.com/MolecularAI/Icolos, where several example relative FE workflows can be found in both commercial and open-source flavours.