MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information.

Federated multipartner machine learning has been touted as an appealing and efficient method to increase the effective training data volume and thereby the predictivity of models, particularly when the generation of training data is resource-intensive. In the landmark MELLODDY project, indeed, each of ten pharmaceutical companies realized aggregated improvements on its own classification or regression models through federated learning. To this end, they leveraged a novel implementation extending multitask learning across partners, on a platform audited for privacy and security.

Development of an Automatic Pipeline for Participation in the CELPP Challenge.

The prediction of how a ligand binds to its target is an essential step for Structure-Based Drug Design (SBDD) methods. Molecular docking is a standard tool to predict the binding mode of a ligand to its macromolecular receptor and to quantify their mutual complementarity, with multiple applications in drug design. However, docking programs do not always find correct solutions, either because they are not sampled or due to inaccuracies in the scoring functions.

Extracting Atomic Contributions to Binding Free Energy Using Molecular Dynamics Simulations with Mixed Solvents (MDmix).

Background: Mixed solvents MD (MDmix) simulations have proved to be a useful and increasingly accepted technique with several applications in structure-based drug discovery. One of the assumptions behind the methodology is the transferability of free energy values from the simulated cosolvent molecules to larger drug-like molecules. However, the binding free energy maps (ΔGbind) calculated for the different moieties of the cosolvent molecules (e.

Accurate Representation of Protein-Ligand Structural Diversity in the Protein Data Bank (PDB).

The number of available protein structures in the Protein Data Bank (PDB) has considerably increased in recent years. Thanks to the growth of structures and complexes, numerous large-scale studies have been done in various research areas, e.g.

Halogens in Protein-Ligand Binding Mechanism: A Structural Perspective.

Halogen atoms have been at the center of many rational medicinal chemistry applications in drug design. While fluorine and chlorine atoms are often added to enhance physicochemical properties, bromine and iodine elements are generally inserted to improve selectivity. Favorable halogen interactions such as halogen bond have been thoroughly studied through quantum mechanics and statistical analyses.