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Hemant Kumar Srivastava
Department of Chemistry, Indian Institute of Technology Guwahati,

Dr. Hemant Kumar Srivastava has completed his Ph. D in Chemistry in 2004 from RML University and postdoctoral studies (2005-2009) from the Hadassah Medical School, the Hebrew University of Jerusalem, Israel. He worked as Research Scientist at CSIR – Indian Institute of Chemical Technology, Hyderabad for three years (2009-2012). Dr. Srivastava moved to Korea Institute for Advanced Study, Seoul, South Korea in 2012 as Assistant Professor in the Department of Computational Sciences and wroked there for two years. Since February 2016, he is working in the Department of Chemistry, Indian Institute of Technology, Guwahati. His awards and honours include Prestigious Young Scientist Award 2005, DST Fast-Track 2009, Ramanujan Fellowship 2015 etc.

Dr. Srivastava have expertise on various chem/bio-informatics techniques and computer aided drug design approaches. He has effieiently utilized DFT and conceptual DFT based parameters in CADD. He also wroks extensively on transition state calculations for finding reaction path and important intermediates in organic, inorganic & biological systems. High-level QM & QM/MM calculations, molecular dynamics simulations and free energy calculations on various macromolecules and are his main research areas. Dr. Srivastava has published around 40 research papers in SCI journals and have around 900 citations.

Abstract OpenTox Asia 2019

DFT Based CADD Approach for Designing Lead Molecules

Drug discovery and development is a challanging, time consuming and expensive process. Computer-aided drug discovery (CADD) tools can help to expedite this process and may reduce the required infrastructure and cost of drug discovery. We utilised Density Functional Theory (DFT) for accurate and efficient CADD process. Ligand and receptor interactions provide useful information and thus we included molecular docking and molecular dynamics (MD) simulation based parameters along with DFT based parameters in CADD process. Our plan is to develop a global predictive model which works not only for one kind of disease but also for various diseases. We selected disease which are leading killers that has plagued mankind for centuries like tuberculosis, various cancer, HIV and malaria [1].

The importance of modeling in drug discovery has been revealed by numerous QSAR, pharmacophore, and/or docking based studies [2-5]. However, most of the studies available in literature are either local models which focus on a specific class of compounds or based on a small dataset. To overcome this lacuna, we collected around 1000 anti-TB molecules, 700 anti-cancer molecules, 137 antimalarial molecules and 220 anti-HIV molecules from the literature with their experimental activity values. All the collected molecules were optimized at B3LYP/6-31G(d) and M06/6-31G(d) level of theory to calculate and compare quantum chemical and conceptual DFT based descriptors. Molecular docking calculations were performed in order to generate docked poses on the basis of their ability to form favorable complexes in the active site of the receptor. We also performed MD simulations to validate the stability of docked poses. MM-PBSA/GBSA calculations were performed to estimate the interaction energy values. Around 400 decriptors were calculated using CODESSA program and conceptual DFT, docking and MD based descriptors were included in the decriptor database. Analogue based models were generated by using optimum number of important descriptors for each class of disease. All the developed models were tested using several statistical parameters to rigorously validate the findings. We predicted a few new and potent anti-TB, anti-cancer, anti-HIV and anti-malarial candidates on the basis of our study. Further work and analysis of the predicted compounds are ongoing.


  1. (1) WHO World Healt Organization. Factsheet on tuberculosis. (Global tuberculosis report 2017).
  2. (2) H. K. Srivastava, G. N. Sastry, Molecular Dynamics Investigation on a Series of HIV Protease Inhibitors: Assessing the Performance of MM-PBSA and MM-GBSA Approaches., Journal of Chemical Information and Modeling, 2012, 52, 3088−3098.
  3. (3) H. K. Srivastava, M. Chourasia, D. Kumar, G. N. Sastry, Comparison of Computational Methods to Model DNA Minor Groove Binders., Journal of Chemical Information and Modeling, 2011, 51, 558−571.
  4. (4) H. K. Srivastava, C. Choudhury, G. N Sastry, The efficacy of conceptual DFT descriptors and docking scores on the QSAR models of HIV protease inhibitors., Med. Chem., 2012, 8, 811−825.
  5. (5) H. K. Srivastava, G. N. Sastry, Efficient estimation of MMGBSA based binding energies for DNA and aromatic furan amidino derivatives’ Journal of Biomolecular Structure and Dynamics, 31(5), 522-537, 2013.