At Prescience In silico we have a technology prototype to zero down to a particular set of molecules (NCE) that could be active against SARS-COV2. So, even we were busy with the development and automation of the platform, we decided to use our technology for finding NCEs and drugs for repurposing for COVID-19.
The current state for designing and developing a new drug candidate is alarming. The time required for a molecule to reach bed side is still more than 10 years. Additionally the expenses are very high, that limits the small pharmaceutical companies to start a program for NCE development. Therefore, in India, even we are having large number of pharmaceutical companies, all the innovations are targeted towards development of generic molecules and new formulations.
Because of this, the development of new molecules are not at par towards the demand and we started realising this more after we got hit my COVID-19. There is a large need of new molecules, even to address orphan diseases, cancer, tuberculosis etc.
Our technology which is a combination of high throughput computational screening, artificial intelligence and large scale computer simulations with enhanced sampling is capable of reducing the time of the drug development and so reduce the expenses. We also get mechanistic understanding of the ligand-target binding and selectivity towards a particular protein target in the pathway (role of other proteins in the similar pathways) along with binding free energies, effect of solvent (we take solvent as well in our calculations), effect of entropy and effect of ions/pH. Our method is best suited for NCEs and drug repurposing.
We are using this revolutionary technology powered by large scale computer simulations and AI to identify the top candidate drugs that are highly likely to be effective in treating COVID-19.
Within last 20 days, we have obtained data from 1.5 million molecules, from which we have selected 30 molecules and subjected to our technology platform. We have identified 3 best molecules (NCE) which show high binding to the target protein. We have compared the data with a molecule which is currently in clinical trail for repurposing and found that our NCEs are much better i.e., potentially efficacious.
We are also working on drug repurposing for COVID-19. Our method is based on finding the targets that are in the pathways for virus and human cell interactions. So, we have taken a holistic approach to find a target and zoom in the space of available FDA approved molecules. Finally, we are using our technology platform for spotting the right combination i.e., molecules with high specificity towards the target in a particular pathway..
Our software platform (SWP) for Scientific Applications is a new platform developed to host applications (APPs) to perform the computation for scientific applications.
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.
Multi Target Multi Ligand Enhanced Sampling Screening (MTMLESS) module has been developed to handle multiple target and ligand systems at the same time. For example, if we have m targets and n ligands, the total number of systems one could screen are (m*n-1). In this case targets (m) could be proteins and nucleic acids and n could be a few thousands of chemical compounds. The number of targets could vary from 1 to n, and n depends on the selection of the targets (which could be inhibited by ligand/s) from the pathways which highly relevant to the disease. These computations are automated in this module and the user only needs to provide the targets and ligands. The automation protocol is highly parallel, so dependent on the computations resource one could scale the total number of ligands and targets. The MTML module uses molecular docking and enhanced sampling MD simulations combined computational approach to screen the ligands for the targeted disease.
Molecule simulator is designed to build and predict material properties for industry relevant molecular systems. A customizable material can be designed by choosing an industrially organic molecule and a relevant solvent. Molecular dynamics simulation is used to predict its physical and structural properties. The simulator is versatile where an organic molecule can be either polymers, lipids, surfactants, rubber, or ionic liquids.
Cheminformatics module is an Artificial Intelligence (AI) powered drug molecule predictor for target proteins, the functional biomolecules that are inhibited by biologically active drug molecule. Generation of a target specific drug molecule dataset and understanding their probability of inhibiting that target is a very important element in modern bio-medical research. This module will provide the user an automated generation of target specific drug dataset either from open-source curated databases of bioactive molecules having drug like properties or by using machine-learning and deep-learning models.