We can help you with computational / in silico methods.
If you need help and support with an in-silico study or want to use in-silico methods alongside your experimental research, we can assist you with a wide range of computational techniques. The four primary areas of our expertise are as follows.
Computational Chemistry methods for molecular compounds
Computational chemistry encompasses a wide range of in-silico techniques, from semi-empirical and DFT to high-level post-SCF methods. In computational chemistry, we mostly deal with molecular compounds, and the main problem is to solve the Schrodinger equation for the electronic system. By solving the equation, we obtain a clear description of the molecule’s structure, and by analysing the wave function, or electron density, we determine various chemical properties. Computational chemistry also can predict chemical reactions and investigate their mechanism kinetics.
Density functional theory for solid-state materials
Nowadays, we use Density functional theory in many science fields, but the most successful application of DFT is to investigate solid-state materials. Current DFT codes can calculate a wide range of solid-state material structural, chemical, optical, spectroscopic, elastic, vibrational, and thermodynamic properties. DFT calculations can now predict a considerable portion of the properties for a given material, not only under ambient conditions, but also in extremely high-pressure conditions. It is also possible to study solid-state phenomena by simulating the evolution of the material structure over a few hundred picoseconds. This feature lets us explore the phenomena’ dynamics and kinetics and calculate their thermodynamic properties.
Monte Carlo and molecular dynamics simulations
Monte Carlo and Molecular Dynamics simulations are among the most effective computational tools and have numerous applications in physics, chemistry, biochemistry, and materials science. In these simulation techniques, we set up an initial array of atoms and then let the simulation generate an ensemble of representative configurations under specific thermodynamic conditions.
We can explore numerous biological and non-biological phenomena using MC/MD simulations, including protein-ligand interaction, drug-carrier interaction, mutation, drug delivery across the cell membrane, micellization, self-assembly, deformation, dislocation, separation, and adsorption.
in silico drug design and drug discovery
Today, researchers have the luxury of employing in-silico drug discovery techniques to reduce or completely avoid the usage of exhausting, costly and time-consuming traditional drug discovery approaches. We can easily filter thousands of candidate compounds and find potential drugs among them using techniques like pharmacophore modelling, virtual screening, and molecular docking. Using regression techniques like 3D-QSAR, we can also estimate chemical compounds’ biological activity against a target. Finally, molecular dynamics techniques provide us with a vivid insight into the mechanism of action of drugs.
ab initio molecular dynamics
Classical molecular dynamics simulation is a powerful tool for investigating many biological and non-biological systems, but it has a severe flaw. Classical MD needs pre-defined potentials (force fields) to work, which means we can only perform simulation on a restricted range of systems whose force fields have been parameterized before. To add insult to injury, most force fields cannot handle chemically complex situations, such as systems with many different atom types or systems that experience changes in the covalent banding pattern throughout the simulation. Computational scientists developed Ab initio molecular dynamics (AIMD) techniques to address this issue. The basic idea behind AIMD methods is to calculate the forces acting on nuclei from electronic structure calculations performed “on the fly” during the simulation. This approach eliminates the need for pre-defined force fields, allowing us to perform ab initio molecular dynamics simulations on virtually any molecular system, regardless of how chemically complex it is —however, since AIMD simulations are heavily computationally-consuming, we are limited to short time and size scales.
Here in Insilicosci, we can use a software toolset including Gaussian, CPMD, quantum-espresso, VASP, and NWChem to utilize multiple AIMD methods such as Car-Parrinello, Born-Oppenheimer, and atom-centred density matrix propagation molecular dynamics.