ANI neural network potentials for small molecule p prediction.

The p value of a molecule is of interest to chemists across a broad spectrum of fields including pharmacology, environmental chemistry and theoretical chemistry. Determination of p values can be accomplished through several experimental methods such as NMR techniques and titration together with computational techniques such as DFT calculations. However, all of these methods remain time consuming and computationally expensive.

An active machine learning discovery platform for membrane-disrupting and pore-forming peptides.

Membrane-disrupting and pore-forming peptides (PFPs) play a substantial role in bionanotechnology and can determine the life and death of cells. The control of chemical and ion transport through cell membranes is essential to maintaining concentration gradients. Likewise, the delivery of drugs and intracellular proteins aided by pore-forming agents is of interest in treating malfunctioning cells.

Martinoid: the peptoid martini force field.

Many exciting innovations have been made in the development of assembling peptoid materials. Typically, these have utilised large oligomeric sequences, though elsewhere the study of peptide self-assembly has yielded numerous examples of assemblers below 6-8 residues in length, evidencing that minimal peptoid assemblers are not only feasible but expected. A productive means of discovering such materials is through the application of screening methods, which often benefit from the use of coarse-grained molecular dynamics (CG-MD) simulations.

Alkali Metal Dihydropyridines in Transfer Hydrogenation Catalysis of Imines: Amide Basicity versus Hydride Surrogacy.

Catalytic reduction of a representative set of imines, both aldimines and ketimines, to amines has been studied using transfer hydrogenation from 1,4-dicyclohexadiene. Unusually, this has been achieved using s-block pre-catalysts, namely 1-metallo-2-tert-butyl-1,2-dihydropyridines, 2-tBuC H NM, M(tBuDHP), where M=Li-Cs. Reactions have been monitored in C D and tetrahydrofuran-d (THF-d ).

Short Peptide Self-Assembly in the Martini Coarse-Grain Force Field Family.

Pivotal to the success of any computational experiment is the ability to make reliable predictions about the system under study and the time required to yield these results. Biomolecular interactions is one area of research that sits in every camp of resolution vs the time required, from the quantum mechanical level to studies. At an approximate midpoint, there is coarse-grained molecular dynamics, for which the Martini force fields have become the most widely used, fast enough to simulate the entire membrane of a mitochondrion though lacking atom-specific precision.

Integrating Computation, Experiment, and Machine Learning in the Design of Peptide-Based Supramolecular Materials and Systems.

Interest in peptide-based supramolecular materials has grown extensively since the 1980s and the application of computational methods has paralleled this. These methods contribute to the understanding of experimental observations based on interactions and inform the design of new supramolecular systems. They are also used to virtually screen and navigate these very large design spaces.

Constant pH Coarse-Grained Molecular Dynamics with Stochastic Charge Neutralization.

pH dependence abounds in biochemical systems; however, many simulation methods used to investigate these systems do not consider this property. Using a modified version of the hybrid non-equilibrium molecular dynamics (MD)/Monte Carlo algorithm, we include a stochastic charge neutralization method, which is particularly suited to the MARTINI force field and enables artifact-free Ewald summation methods in electrostatic calculations. We demonstrate the efficacy of this method by reproducing pH-dependent self-assembly and self-organization behavior previously reported in experimental literature.

Synthesis of ()()-Tiruchanduramine.

The absolute stereochemistry of the marine alkaloid ()-()-tiruchanduramine was established via a convergent total synthesis in six steps and 15.5% overall yield from Fmoc-D-Dab(Boc)-OH.

Beyond Tripeptides Two-Step Active Machine Learning for Very Large Data sets.

Self-assembling peptide nanostructures have been shown to be of great importance in nature and have presented many promising applications, for example, in medicine as drug-delivery vehicles, biosensors, and antivirals. Being very promising candidates for the growing field of bottom-up manufacture of functional nanomaterials, previous work (Frederix, et al. 2011 and 2015) has screened all possible amino acid combinations for di- and tripeptides in search of such materials.

Artificial Intelligence and Health in Nepal.

The growth in information technology and computer capacity has opened up opportunities to deal with much and much larger data sets than even a decade ago. There has been a technological revolution of big data and Artificial Intelligence (AI). Perhaps many readers would immediately think about robotic surgery or self-driving cars, but there is much more to AI.

Size-dependent melting point depression of nickel nanoparticles.

We investigate the phase-transition behaviour of nickel nanoparticles (3-6 nm) dynamic TEM. The nanoparticles were synthesized within a reverse microemulsion and then monitored dynamic TEM simultaneously while undergoing controlled heating. The size-dependent melting point depression experimentally observed is compared with, and is in good agreement with existing thermodynamic and molecular dynamic predictions.