Prescience in silico Solution Suite (PRinS3)
Our software platform (PRinS3) for Scientific Applications is a new platform developed
to host applications (APPs) to perform and provide solutions in healthcare, enery and materials
We also develop APPs that are hosted on our SWP for providing solutions to the industries working in materials, chemicals, energy, and pharmaceutical domains. These APPs perform different tasks e.g., new chemical (drug) entity screening for potentials target (protein, DNA, RNA), screening of molecules for specific use (additive, paints, adhesive surfactant), predict bulk propertied (of polymers, small molecules, solvents, ionic liquids), design surfactants for oil recovery, etc. The SWP provides all the necessary backend support to these APPs. The SWP consists of three major backend supports, 1. Data-Connector 2. Modules 3. Visualization tools.
The Data-Connector supports the user to upload, download data, manage files, and connect to public
cloud such as Google Cloud. It also manages all the local servers, at the premise, clusters, and
automatically runs (and load balance) a large number of calculations. Data-Connector is a major
component of the SWP as this is developed to aid users to scale up a number of calculations without
any manual intervention and without any in-house computational resources.
The modules in the SWP are the backend types of machinery for the APPs. The SWP currently consists
of QM, MD, MC, file conversion, analysis modules which can be called (integrated) in any APP. Mostly
these modules are open-source well tested and scalable in high-performance computing (HPC)
environments and public cloud.
The SWP could host visualization tools for user interactions with the data and analysis of outputs. This layer of SWP currently populated with molecular visualizers and plotters.
Matereial Property Predictor
We are working on an Application for computational prediction of material properties with high accuracy.
We provide an efficient Monte Carlo tool based on smart algorithms to predict phase equilibria and diagram of pure fluids and their mixtures in the presence of other components.
Finding a catalyst that can provide desired product with high selectivity and yield is often the beginning of a many chemical/energy/materials industries.
Deep Learning suite for Drugs
Drug discovery is an extremely long, expensive and challenging process. Our drug discovery pipeline of Apps aims to accelerate the development process that includes identifying new chemical entities (NCE) or rapid repurposing of drugs for direct therapeutic approaches.
Open source Database for Computations Drug Design