Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers.

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods.

Enhanced Molecular Dynamics Method to Efficiently Increase the Discrimination Capability of Computational Protein-Protein Docking.

Protein-protein docking typically consists of the generation of putative binding conformations, which are subsequently ranked by fast heuristic scoring functions. The simplicity of these functions allows for computational efficiency but has severe repercussions on their discrimination capabilities. In this work, we show the effectiveness of suitable descriptors calculated along short scaled molecular dynamics runs in recognizing the nearest-native bound conformation among a set of putative structures generated by the HADDOCK tool for eight protein-protein systems.

How π back-donation quantitatively controls the CO stretching response in classical and non-classical metal carbonyl complexes.

The CO stretching response upon coordination to a metal M to form [(L) M(CO)] complexes (L is an auxiliary ligand) is investigated in relation to the σ donation and π back-donation components of the M-CO bond and to the electrostatic effect exerted by the ligand-metal fragment. Our analysis encompasses over 30 carbonyls, in which the relative importance of donation, back-donation and electrostatics are varied either through the ligand in a series of [(L)Au(CO)] gold(i) complexes, or through the metal in a series of anionic, neutral and cationic homoleptic carbonyls. Charge-displacement analysis is used to obtain well-defined, consistent measures of σ donation and π back-donation charges, as well as to quantify the σ and π components of CO polarization.

When the Tolman electronic parameter fails: a comparative DFT and charge displacement study of [(L)Ni(CO)₃](0/-) and [(L)Au(CO)](0/+).

In this study we have examined 42 [(L)M(CO)n](±/0) complexes (M = Ni and Au), including neutral ligands, such as phosphines and carbenes, and anionic ones. For each complex, the carbonyl stretching frequency (ν(CO)) and the amount of charge donated from the ligand to the metal (CT) have been computed on the basis of DFT calculations. For nickel complexes, the two observables nicely correlate with each other, as expected from the theory underlying the Tolman electronic parameter.