Data-driven generation of perturbation networks for relative binding free energy calculations. (14th October 2022)

• Record Type: Journal Article
• Authors: Scheen, Jenke; Mackey, Mark; Michel, Julien
• Is Part Of: Digital discovery. Volume 1:Number 6(2022)
• Page Start: 870
• Page End: 885
• Publication Date: 2022-10-14
• Subjects: Chemistry -- Data processing -- Periodicals; Medical sciences -- Data processing -- Periodicals; Machine learning -- Periodicals; 542.85
• DOI: 10.1039/d2dd00083k
• Languages: English
• ISSNs: 2635-098X

Abstract

Abstract : A data-driven approach for predicting networks for affinity calculations offers a new route for automated molecular simulations in drug discovery. Abstract : Relative binding free energy (RBFE) calculations are increasingly used to support the ligand optimisation problem in early-stage drug discovery. Because RBFE calculations frequently rely on alchemical perturbations between ligands in a congeneric series, practitioners are required to estimate an optimal combination of pairwise perturbations for each series. RBFE networks constitute in a collection of edges chosen such that all ligands (nodes) are included in the network, where each edge represents a pairwise RBFE calculation. As there is a vast number of possible configurations it is not trivial to select an optimal perturbation network. Current approaches rely on human intuition and rule-based expert systems for proposing RBFE perturbation networks. This work presents a data-driven alternative to rule-based approaches by using a graph siamese neural network architecture. A novel dataset, RBFE-Space, is presented as a representative and transferable training domain for RBFE machine learning research. The workflow presented in this work matches state-of-the-art programmatic RBFE network generation performance with several key benefits. The workflow provides full transferability of the network generator because RBFE-Space is open-sourced and ready to be applied to other RBFE software. Additionally, the deep learning model represents the first machine-learned predictor of perturbation reliability in RBFE calculations.