Kernel Methods for Predicting Yields of Chemical Reactions.

The use of machine learning methods for the prediction of reaction yield is an emerging area. We demonstrate the applicability of support vector regression (SVR) for predicting reaction yields, using combinatorial data. Molecular descriptors used in regression tasks related to chemical reactivity have often been based on time-consuming, computationally demanding quantum chemical calculations, usually density functional theory. Structure-based descriptors (molecular fingerprints and molecular graphs) are quicker and easier to calculate and are applicable to any molecule. In this study, SVR models built on structure-based descriptors were compared to models built on quantum chemical descriptors. The models were evaluated along the dimension of each reaction component in a set of Buchwald-Hartwig amination reactions. The structure-based SVR models outperformed the quantum chemical SVR models, along the dimension of each reaction component. The applicability of the models was assessed with respect to similarity to training. Prospective predictions of unseen Buchwald-Hartwig reactions are presented for synthetic assessment, to validate the generalizability of the models, with particular interest along the aryl halide dimension.

Interaction of the maturation protein of the bacteriophage MS2 and the sex pilus of the Escherichia coli F plasmid.

One promising strategy to combat antimicrobial resistance is to use bacteriophages that attach to the sex pili produced by transmissible antimicrobial resistance (AMR) plasmids, infect AMR bacteria and select for loss of the AMR plasmids, prolonging the life of existing antimicrobials. The maturation protein of the bacteriophage MS2 attaches to the pili produced by Incompatibility group F plasmid-containing bacteria. This interaction initiates delivery of the viral genetic material into the bacteria. Using protein-protein docking we constructed a model of the F pilus comprising a trimer of subunits binding to the maturation protein. Interactions between the maturation protein and the F pilus were investigated using molecular dynamics simulations. In silico alanine scanning and in silico single-point mutations were explored, with the longer term aim of increasing the affinity of the maturation protein to other Incompatibility group pili, without reducing the strength of binding to F pilin. We report our computational findings on which residues are required for the maturation protein and F pilin to interact, those which had no effect on the interaction and the mutations which led to a stronger interaction.

Utilizing Grand Canonical Monte Carlo Methods in Drug Discovery.

The concepts behind targeting waters for potency and selectivity gains have been well documented and explored, although maximizing such potential gains can prove to be challenging. This problem is exacerbated in cases where there are multiple interacting waters, wherein perturbation of one water can affect the free energy landscape of the remaining waters. Knowing the right modification a priori is challenging, and computational approaches are ideally suited to help answer the key question of which substitution is best to try. Here, we use Grand Canonical Monte Carlo and the recent Grand Canonical Alchemical Perturbation methods to both understand and predict the effect of ligand-mediated water displacement when more than one water molecule is involved, as well as to understand how exploiting water networks can help govern selectivity.