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. On the contrary, for gold complexes a more complex pattern can be observed, with an apparent differentiation between phosphine ligands and carbon-based ones. Such differences have been explained analyzing the Au-L bond in terms of Dewar-Chatt-Duncanson bonding constituents (σ donation and π back-donation). Our analysis demonstrates that in linear gold(I) complexes, ν(CO) depends only on the metal-to-ligand π back-donation.

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.

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. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.

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) n M(CO)] m 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)]0/+ 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. It is found that all complexes feature a comparable charge flow of σ symmetry (both in the M-CO bonding region and in the CO fragment itself), which is therefore largely uncorrelated to CO response. By contrast, π back-donation is exceptionally variable and is found to correlate tightly with the change in CO bond distance, with the shift in CO stretching frequency, and with the extent and direction (C → O or C ← O) of the CO π polarization. As a result, we conclusively show that π back-donation can be an important bond component also in non-classical carbonyls and we provide the framework in which the spectroscopic data on coordinated CO can be used to extract quantitative information on the π donor properties of metal-ligand moieties.