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1. Conventional Drug Discovery Methods

2. Bioinformatics & Computational Biology

3. Training & Consultation

4. Custom Solutions

Future Targeted Services:
1. Literature to Pathways mining
2. Reaction Mining
3. Microarray Data Analaysis

1. Conventional Drug Discovery Methods

RASA provides a wide array of methods for conventional drug discovery to match the complex and urgent needs of industry. RASA offers high quality non-turnkey approach which emphasizes the importance of expert knowledge of the target field and structural domain. RASA offers services in designing and optimizing virtual library, scaffold hopping, predicting bioactivity, toxicity and ADME properties, protein-protein, protein-ligand and ligand-ligand interactions.

Virtual Library design & Scaffold Hopping:
With the advent of sequencing technologies and the advancement of biotechnological process, a huge number of potential drug targets have been identified. Parallel to the development of biological sciences, the chemical science has also expanded and today there are a huge number of drug-like molecules in the chemical space. Even as the cost and synthesis of a single chemical compound has fallen, the cost becomes too high when multiplied by the thousands of combinatorial library members. One of the most widely accepted techniques to solve this bottleneck is to design a virtual library based on pharmacophores The next step is to filter the library members and find out the most probable drug-like molecules. The virtual library design can be either focused (library is designed based on docking scores, similarity scores or QSAR analysis) or diverse(library is designed without redundancies with respect to the chemical space in consideration) .

RASA designs customized virtual library based on scaffolds, Pharmacophore and Functionophore. Virtual library designing helps in creating an array of chemical structures which can be used in structure based or protein based in-silico Drug Designing. RASA’s virtual library designing algorithm aims at identifying isofunctional molecular structures with different molecular backbone.

Deliverables
A detailed delivery report for Virtual Library Design consists of:
1. RASA’s categorized Library depending upon Algorithm selected (Pharmacophore, Functionophore and customized scaffolds)
2. Molecular scaffold similarity and dissimilarity analysis with the trained molecules in case of Pharmacophore and Functionophore (as Excel file)
3. The resultant molecules are docked and checked giving more weight age to analyzed hits by providing which molecules can be dock for further enhance that analyzed hits are highly believable (as Text file)
4. Bioassay and Bio activity analysis (as Text file)
5. Extensive literature study (as Document file)

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Phamacological Activity Prediction:

The design of new medical drugs possessing desired physical and chemical properties is a challenging task in any pharmaceutical industry. If one goes by the traditional approach, then the designer will need to test a very large number of molecules, and look for the desired pharmacological property that can needs to be present in the drug to be formulated. Therefore it is very useful to have tools that can predict and discriminate the pharmacological activity of a given molecular compound so that the designer needs to do his laboratory experiments in a well directed manner, such that they are they are directed to those molecular groups in which there is a high probability of finding new compounds with desired properties.
RASA’s specializes in predicting all possible bioactivity (> 3,000 including drug targets and disease). Our algorithm contains several statistical models for disease and associated drug targets.

Deliverables
A detailed delivery report for Phamacological Activity Prediction consists of:
1. A detailed report of the molecule's pharmacological activity with graphical output
2. Report powered with extensive literature references.

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Toxicity Prediction:

A huge investment is associated with the designing and synthesis of a drug. It is true that these drugs give us a better life. However serious side effects have been observed in some drugs, that raise a social question and resulting in the withdrawal of the drug from the market.The cost incurred in case of a drug that has to be withdrawn from market due to toxic effects is unimaginable. In such cases the cost of development of the drug increases by a big margin. We can minimize the expenditure by determining potential toxicity problems as early as possible. Toxicity if predicted at an early stage, for example before the molecule is synthesized will drastically reduce the cost of the drug which is otherwise incurred due to drug-failure. Thus the use of predictive toxicology is called for. Various types of in-silico studies can be performed to predict side-effects of the molecule, thus giving us firsthand information about the possible unwanted properties of the molecule, hence allowing us to refrain from the synthesis of such molecules as possible drugs. Quantitative structure-activity relationships (QSARs), relating mostly to specific chemical classes, have long been used for this purpose, and exist for a wide range of toxicity endpoints.

RASA’s toxicity prediction service predicts all possible Toxicity (> 150). We use several statistical models for Toxicity and associated drug targets for toxicity thus providing you with a detailed report of the molecules properties.

Deliverables
1. A detailed report of the molecule's toxicity with graphical output.
2. Report powered with extensive literature references.

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ADME Predication:
The "ADME" acronym is commonly used in the pharmaceutical industry to indicate all the phenomena associated with Absorption, Distribution, Metabolism, Elimination. ADME information is critical in all phases of a fully integrated drug development program. A full consideration of all facets of the molecular structure and the impact of that structure on the ADME profile will enable chemists to design out negative ADME attributes (e.g. chemically reactive moieties, avidly metabolized sites) and incorporate "ADME-friendly" attributes (e.g. optimal log P, good membrane permeability).

Hence the prediction of ADME is very critical and helpful in the drug development process. ADME prediction provides the scientist with the following benefits:
1. Focus research efforts on molecules that meet property requirements
2. Investigate the influence of changing inter-related properties to gain a deeper understanding of your compounds
3. Reduce the need for expensive, labor-intensive assays

RASA ADME prediction algorithm calculates :
1. Intrinsic aqueous solubility of organic compounds predicted in logS (Aqueous Solubility), moles/L and grams/L units at 25 degree Celsius and pH 7
2. logP (Partition Coefficient) of organic compounds predicted at 25 degree Celsius and pH 7
3. logD (Distribution Coefficient)of organic compounds predicted at 25 degree Celsius and pH 7
4. pKa - Ionization Constant of organic compounds predicted at 25 degree Celsius and pH 7

Deliverables
A report highlighting important finding of ADME predictive study, detailed result in Excel format. Output will also be provided in an SDF format comprising structure of input molecules and predictive results.

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Drug Repositioning:
Drug repositioning also know as drug profiling is a technique which helps pharmaceutical industries and scientist to turn use an old drug for a new cause. The technique basically concerns the development of either discontinued or off-patent drugs for newly found indications/diseases. Drug repositioning relies on biological redundancy: either a biological target functions in several physiological pathways or drugs have multiple uncharacterized off-target effects. Because repositioned drugs already have undergone extensive toxicological and pharmacokinetic studies, their development for a novel indication is associated with considerably reduced risk, cost and time compared with conventional discovery. RASA offers excellent services in drug-repositioning. Our repositioning services are custom tailored to meet the varying needs of our clients.
RASA’s drug repositioning algorithm helps you understand the similarity between two sets of drugs that can be used for cross target identification. Our data delivers valuable insights into potentially novel indications for established drugs or drugs under development.

Deliverables
A detailed delivery report for Drug Repositioning Prediction consists of:
1. A detailed report of the molecule's multiple pharmacological activity with graphical output.
2. Report powered with extensive literature references.


	Docking Studes: Docking is probably the best known of methods used to identify the fit between a receptor and a potential ligand. Today, docking is used primarily as part of virtual screening protocol, wherein a database of ligands is screened against one or more target receptors. Docking actually consists of two distinct parts, the “docking” part, which is the search scheme to identify suitable conformations or poses, and “scoring”, which is a measure of the affinity of various poses. RASA’s provides stat-of-the-art docking solutions to its clients. We use updated and appropriate softwares to carry out the task and provide our clients with an detailed report of the results Deliverables: 1. Detailed report of the results which includes scores, inhibition constant, etc. 2. Publication quality images of the docked molecule.


	Lead Optimization: The Lead Optimization process compares the properties of various lead compounds and provides information to help select the leads with the greatest potential to be developed into safe and effective medicines, and in alignment with corporate strategy. RASA Lead optimization technique aims to enhance the most promising compounds to improve effectiveness, diminish toxicity, or increase absorption.

Protein Ligand Network Analysis:
Study of interactions between proteins and ligands play a crucial role in drug development. One should be well aware of the proteins in which the selected ligand/drug is likely to dock, because without this knowledge we cannot design specific drugs. The tools available at present only gives us idea about the interaction on the protein with its native ligand, but they provide no information of the interaction of the ligand with other proteins or vice versa. So building a connected network of proteins and ligands will enable the users to get a better view of the related proteins and ligands, which in turn reduces the quantum of work required to find a suitable/ related ligand/protein for in-silico drug designing. We do Protein Ligand Network analysis using our algorithm. This analysis is useful to those who want to design better and more specific drugs.

Deliverables with this service includes the following:
1. A detailed delivery report for Protein-Ligand Network Analysis consists of:
2. The alignment score of the proteins sequences, generated through the alignment algorithms.
3. The similarity score of the ligands calculated using tanimoto Co-efficient and Eucledian Distance.
4. Detailed network showing all possible connections between all the protein and ligand entities.
5. Heat map

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