The Molecular Property Diagnostic Suite (MPDS) (http://mpds.neist.res.in) is a galaxy based open-source indigenous drug discovery and development platform. The idea was conceived in 2004 and the first MPDS platform was launched in 2017. The MPDS platform has been initially developed for the Tuberculosis (MPDSTB) and its robustness allows us to extend to further diseases such as diabetes, metabolic disorders and HIV. All these platforms have four different modules viz. data library, data processing, data analysis and advanced modules. The data library contains all the information about specific diseases including drug targets, literature, approved drugs etc. Data processing module is dedicated to the Chemoinformatics (i.e. 2D, 3D descriptors calculation) analysis. The computational drug discovery modules such as molecular docking, 2D-QSAR are available in the data analysis module. One of the important components of MPDS is compound library and it contains ~130 million compounds along with its properties. The compound library developed with the intention to address the question why the molecule behaves the way it behaves. Once the properties of the molecules have understood, a new molecule can be predicted or synthesized with the desired properties. The MPDS compound library contains a diverse set of molecules that includes drug molecules, metabolites, catalysts etc. The group has developed different models (i.e. antiviral prediction, M.tb. permeability prediction, success and failures of clinical trials) by combining chemoinformatics and machine learning approaches. MPDS portals are available at the following link Go to MPDS

The ACDS (Advanced Computation and Data Science) lecture series is an initiative of the division towards imparting high quality lectures in the field of advanced computation and data sciences. The ACDS lectures aims to impart knowledge on transforming theory into practice. As such, the lecture series not only contains subject matter but also consists of worked out examples and exercises. To enhance the effectiveness of the lecture series, the entire syllabus has been categorized into three modules: a) Background b) Introduction c) Core and d) Technical. The lecture series has been designed in four modules comprising of study materials, 400 worked examples and 2000 exercise questions. The main aim of ACDS lecture series is generate trained manpower who can actively work on the fourth paradigm of scientific discovery which is the current state-of-the art for any research.

1. Centre for Excellence for Advance Computation and Data Sciences BIC at CSIR-NEIST (GPP: 0389) (Budget: Rs. 190.72804 Lakhs Duration: 5 Years Funding Agency: DBT)

The proposed centre of excellence in bioinformatics at CSIR-NEIST is aimed to integrate molecular modelling, bioinformatics, chemoinformatics, system biology and data science approaches to provide solution in various areas. Connection of traditional, bioinformatics, mathematical and statistical approaches to those of AI, ML and DL will be the key strategy for solving many societal problems in near future.

2. Designing advanced functional materials for better performing photovoltaic devices through in-silico study (GPP: 0360) (Budget: 36.54 Lakhs Duration: 3 Years Funding Agency: DST-Nanomission)

Exploiting the size-, shape-, and composition dependent optoelectronic properties of nanostructured materials to design computationally the latest generation photovoltaic devices with higher efficiency. Static energy calculations are performed using SCC-DFTB method and electron transport dynamics are investigated using NEGF formalism. Development of SCC-DFTB parameters for necessary elements using automatic DFTB parameter toolkit (ADPT) based on particle swarm optimization (PSO) algorithm.

3. Advance hybrid nanomaterials and their photocatalytic efficiency towards solar fuel generation: A Quantum Chemical exploration (GPP: 0361) (Budget: 28.71 Lakhs Duration: 3 Years Funding Agency: DST-SERB)

Exploration of photocatalytic properties of composite nanomaterials, composed with inorganic-organic nanohybrids, semiconductor heterostructures and 2D nanomaterials, as a function of size, shape and composition of materials for CO2 reduction and fuel production.