SMM version 1.0
The software package SMM2018.1 is a versatile high-performance computing platform based on years of research done by the research group of Dr. Andriy Kovalenko. It is highly parallel and can interface with the popular molecular dynamics package AMBER. There are multiple modules associated with the package to deliver on different aspects of computational chemistry, computational biology, and materials research.
The primary development of the RISM theory was to explore the liquid state. The current installation of the code can be used to explore solvation shell(s) around a solute of arbitrary shape, in the presence of co-solvents and additives. We have carefully validated solvent models for different solvents, which are supplied as a prebuilt module for further use in research projects. For detail on these solvents, please check the whitepapers.
Ligand Binding Site Search
The binding site search can be done using multiple fragments as a search probe in a water medium. There are several small fragments available, as a standard library, to search binding sites, based on distributions of the sites of fragment co-solvent/additive around a solute. Note: With a large number of fragments used in a single calculation, the convergence could be really slow. Running multiple searches using different fragments can overcome such issues.
Solvation Free Energy Predictions
The pre-optimized solvent modules are provided with the software. They can be used as is to predict solvation-free energy or can be used to develop new user-defined correction schemes (e.g. “universal correction”). Most of the solvent models are optimized using the GAFF small molecule force field and AM1-BCC atomic charges. Users can rebuild these models with their choice of theoretical accuracy.
The required tools are available in the module.
Molecular Partition Function and QSAR Module
Prediction of log octanol/water (based on computed solvation free energy), PAMPA permeability (permeable or impermeable), and logBB (blood-brain barrier permeability) based on solvation free energy and molecular descriptor based schemes are available. Standard QSAR/QSPR predictions can be done using Python(R) based modules.