1. Sanjeevini : Sanjeevini represents a massive ongoing scientific endeavor to provide to the user, a freely accessible state-of-the-art software suite for protein and DNA targeted lead molecule discovery. It builds in several features, including automated detection of active sites, scanning against a million-compound library for identifying hit molecules, all atom-based docking and scoring, and various other utilities to design molecules with desired affinity and specificity against biomolecular targets. Each of the modules is thoroughly validated on a large dataset of protein/DNA drug targets.

Ref.: B. Jayaram, N. Latha, T. Jain, P. Sharma, A. Gandhimathi, and V. S. Pandey, "Sanjeevini: A comprehensive Active-Site directed lead design software", Ind. J. Chem., 2006, 45A, 1834-1837. http://niscair.res.in/sciencecommunication/researchjournals/rejour/ijca/ijca2k6/ijca_jan06.asp#p21

B. Jayaram, Tanya Singh, Goutam Mukherjee, Abhinav Mathur, Shashank Shekhar, and Vandana Shekhar, "Sanjeevini: a freely accessible web-server for target directed lead molecule discovery", BMC Bioinformatics, 2012, 13, S7.http://biomedcentral.com/1471-2105/13/S17/S7

2. Dhanvantari : Dhanvantari is a pipeline to incorporate novel scientific methods and highly efficient algorithms along with combining principles of Chemistry and Biology with Information Technology for target directed Drug Designing. The pipeline covers all the aspects from genome through genes, proteins, active site to a final proposed lead like compound.

Ref.: Ruchika Bhat, Rahul Kaushik, Ankita Singh, Debarati DasGupta, Abhilash Jayaraj, Anjali Soni, Ashutosh Shandilya, Vandana Shekhar, Shashank Shekhar, B. Jayaram, " A comprehensive automated computer-aided discovery pipeline from genomes to hit molecules" Chemical Engineering Science, 2020. https://doi.org/10.1016/j.ces.2020.115711

3. Binding Affinity Prediction of Protein-Ligand Server(BAPPL) : BAPPL server computes the binding free energy of a non-metallo protein-ligand complex using an all-atom energy based empirical scoring function.

Ref.: T. Jain, and B. Jayaram, "An all atom energy based computational protocol for predicting binding affinities of protein-ligand complexes", FEBS Letters, 2005, 579, 6659-6666.http://febsletters.org/article/S0014-5793%2805%2901291-3/abstract

4. Binding Affinity Prediction of Protein-Ligand complex containing Zinc Server  (BAPPL-Z) : Binding Affinity Prediction of Protein-Ligand complex containing Zinc [BAPPL-Z] server computes the binding free energy of a zinc containing metalloprotein-ligand complex using an all-atom energy based empirical scoring function.

Ref.: T. Jain, and B. Jayaram, "A computational protocol for predicting the binding affinities of zinc containing metalloprotein-ligand complexes", PROTEINS: Struct. Funct. Bioinfo., 2007, 67, 1167-1178. http://onlinelibrary.wiley.com/doi/10.1002/prot.21332/abstract

5. Improved predicting protein-ligand affinities (BAPPL+) : Bappl+ is an improved methodology for predicting the binding affinities of protein-ligand and metalloprotein-ligand complexes. It computes binding affinity based on the most important energetic contributors such as electrostatics, van der Waals, hydrophobicity and entropy of protein and ligand. For metalloprotein-ligand complexes, it uses the explicitly-derived quantum-optimized charges for various metal ions (Zn, Mn, Mg, Ca and Fe). It uses the Random Forest to derive the final score.

Ref.: Anjali Soni, Ruchika Bhat, B. Jayaram, "Improving the binding affinity estimations of protein-ligand complexes using machine-learning facilitated force field method" J Comput Aided Mol Des, 2020. https://doi.org/10.1007/s10822-020-00305-1

6. Predict DNA-Drug Interaction strength by Computing ΔTm and Affinity of binding (PreDDICTA) :A hybrid molecular mechanics-statistical mechanics-solvent accessibility-based computational protocol is developed to calculate DNA-ligand binding affinity without any database training and is validated on 50 DNA-ligand complexes. The calculated binding energies yield high correlation coefficients of 0.95 (R2 = 0.90) and 0.96 (R2 = 0.93) in linear plots against experimental binding free energies (DeltaGo) and DeltaTm, respectively.

Ref.: S. A. Shaikh and B. Jayaram, "A Swift all-atom energy based computational protocol to predict DNA ligand binding affinity and ΔTm", J. Med. Chem., 2007, 50, 2240-2244. http://pubs.acs.org/doi/abs/10.1021/jm060542c

7. Automated Server for Protein Ligand Docking (ParDOCK): ParDOCK is an all-atom energy-based Monte Carlo, rigid protein ligand docking, implemented in a fully automated, parallel processing mode which predicts the binding mode of the ligand in receptor target site. The structural input data for the ParDOCK are optimized reference complex i.e. protein bound with a ligand and a candidate ligand to be docked.

Ref.: Gupta, A. Gandhimathi, P. Sharma, and B. Jayaram, "ParDOCK: An all atom energy based monte carlo docking protocol for protein-ligand complexes", Protein and Peptide Letters, 2007, 14, 632-646. http://eurekaselect.com/78655/article

8. Improved version of Automated Server for Protein Ligand Docking (ParDOCK+) : This is an advanced version of ParDOCK with improved docking algorithm and scoring functions.

Manuscript in preparation

9. Bioactivity of Indian Medicinal Plants (BIMP) :The creation of a comprehensive databank on bioactivities of compounds in Indian medicinal plants involves the systematic collection, curation, and organization of data from various sources. This databank aims to consolidate information on the chemical constituents of medicinal plants, their bioactive properties, and their potential applications in the treatment of various diseases.

Manuscript in preparation

10. Active Site Prediction: Active Site Prediction of Protein server computes the cavities in a given protein.

11. Automated Active Site Prediction (ASF & AADS): Predicts 10 binding sites in a protein target and docks the uploaded ligand molecule at all 10 sites predicted in an automated mode.

Ref.: Tanya Singh, D. Biswas, and B. Jayaram, “AADS - An automated active site identification, docking and scoring protocol for protein targets based on physico-chemical descriptors”, J. Chem. Inf. Modeling, 2011, 51, 2515-2527. http://pubs.acs.org/doi/abs/10.1021/ci200193z

12. Non Redundant Database of Small Molecules(NRDBSM):NRDBSM database is aimed specifically at virtual high throughput screening of small molecules and their further optimization into successful lead-like candidates. It has been developed giving special consideration to physicochemical properties and Lipinski's rule of five, which determine the solubility, permeability and transport characteristics across membranes. Some of these are molecular weight, number of hydrogen bond donors and acceptors, log P and molar refractivity. Fixed precincts for these properties have been employed as filters to assemble the database.

Ref.: S. A. Shaikh, T. Jain, G. Sandhu, N. Latha, and B. Jayaram, "From drug target to leads- sketching, A physicochemical pathway for lead molecule design in silico", Curr. Pharma. Des., 2007, 13, 3454-3470. http://eurekaselect.com/66010/article

13. Lipinski Rule of Five:Lipinski rule of 5 helps in distinguishing between drug like and non-drug like molecules. It predicts high probability of success or failure due to drug likeness for molecules complying with 2 or more of the following rules.

Ref.: B. Jayaram, Tanya Singh, Goutam Mukherjee, Abhinav Mathur, Shashank Shekhar, and Vandana Shekhar, "Sanjeevini: a freely accessible web-server for target directed lead molecule discovery", BMC Bioinformatics, 2012, 13, S7. http://biomedcentral.com/1471-2105/13/S17/S7

14. Molecular Volume Calculator : This tool calculates volume of small molecules (less than 500 atoms).

15. DNA Sequence to Structure : This tool generates double helical secondary structure of DNA using conformational parameters taken from experimental fiber-diffraction studies..

16. DNA Ligand Docking: Rigid Docking predicts the binding mode of the ligand in the minor groove of DNA.

17. Wiener Index Calculator: This tool is useful for calculating Wiener index. This will predict Wiener Index of ligand/molecule. It is an important topological descriptor of a molecule. Wiener index can be further used for deducing various other properties of a ligand/molecule which can be useful in drug designing.

18. Rapid Screening with Physicochemical Descriptors + Machine Learning (RASPD):RASPD+ (RApid Screening with Physicochemical Descriptors + Machine Learning) is a computationally fast protocol for identifying lead-like molecules based on predicted binding free energy against a target protein with a 3D structure and a defined ligand binding pocket.

Ref.: Goutam Mukherjee and B. Jayaram, "A Rapid Identification of Hit Molecules for Target Proteins via Physico-Chemical Descriptors", Phys. Chem. Chem. Phys., 2013 15, 9107-16. http://pubs.rsc.org/en/Content/ArticleLanding/2013/CP/C3CP44697 &

Stefan Holderbach, Lukas Adam, B Jayaram, Rebecca C. Wade and Goutam Mukherjee, "RASPD+: Fast protein-ligand binding free energy prediction using simplified physicochemical features", Front. Mol. Biosci., 2020. https://doi: 10.3389/fmolb.2020.601065

19. Transferrable Partial Atomic Charge Model - up to 4 bonds (TPACM4): This software is used for deriving the partial atomic charges of small molecules for use in protein/DNA-ligand docking and scoring. The main idea of TPACM4 is based on a look up table of template fragments consisting of 4-bond paths around the atom being charged. This method overcomes the limitations of time complexity of assigning the partial atomic charges of a given molecule

Ref.: G. Mukherjee, N. Patra, P. Barua and B. Jayaram, "A Fast empirical GAFF compatible partial atomic charge assignment scheme for modeling interactions of small molecules with biomolecular targets (TPACM4)", J. Comput. Chem., 2011, 32, 893-907. http://onlinelibrary.wiley.com/doi/10.1002/jcc.21671/abstract

20. BAITOC: Bioactivity information to organic chemists: Druggable biomolecules are limited but the number of small molecules capable of moderating their activities is huge. Instead of searching for a molecule for a given target, a new approach to finding a target for a known molecule can also be utilized. There are many molecules that are synthesized in laboratories across the globe but are never tested for their bioactivity. Also, cases exist where bioactive compounds are known but their biomolecular targets are unknown. Baitoc/FishBAIT is an application/software that aims to fill this gap, by a quick examination against a databank of pathogen’s protein structures. The application screens thousands of protein structures at a time against input molecules using the RASPD+ logic and provides information on potential protein targets for molecules under investigation.

Ref.: Manuscript in preparation

21. Sites of metabolism (SOM) : Knowledge of sites of metabolism (SOM) of a molecule and its biotransformation products can help not only in optimizing the lead molecule with favourable metabolic profile but also in reducing toxicity and enhancing bioavailability and bioactivity.

Ref.: G. Mukherjee, P. L. Gupta, B. Jayaram, "Predicting the binding modes and sites of metabolism of xenobiotics", *Molecular BioSystems*, 2015, 11, 1914 - 1924. DOI:10.1039/C5MB00118H

22. Intercalate: Intercalate is a web server, dedicated for DNA intercalation process, which predicts the structure and energetics of DNA-intercalator complexes. It also incorporates an algorithm for creating the DNA structure having the intercalation site from the given nucleotide sequence and intercalation site information. Followed by, it performs Monte Carlo docking and scoring for the prediction of ligand binding modes and binding free energy estimations.

Ref.: Anjali Soni, Pooja Khurana, Tanya Singh, B. Jayaram, "A DNA Intercalation Methodology for an Efficient Prediction of Ligand Binding Pose and Energetics",Bioinformatics, 2017. https://doi.org/10.1093/bioinformatics/btx006

23. Multi Target Ligand Design (MTLD): Multi Target Ligand Design (MTLD) is a web server that helps to identify common leads for any two protein targets. The basic principle of identifying common small molecules inhibiting multiple targets, remains the active site similarity and common interactive residues in their binding pockets. The protocol is based on the utilization of already established and validated softwares which are harnessed here to provide a set of common ligands for multiple proteins.

Ref.: Jayaraj A., Bhat R., Pathak A., Singh M., Jayaram B. "Development of a Web-Server for Identification of Common Lead Molecules for Multiple Protein Targets". In: Methods in Pharmacology and Toxicology. Humana Press, 2018: 1-18. DOI: 10.1007/7653_2018_9.

24. Sanjevini Android application: Sanjeevini application for smart phones for Computer Aided Drug Design (CADD)