Computational Techniques in the Drug Design

Computational Techniques in the Drug Design Process

David Young
Cytoclonal Pharmaceutics Inc.

The purpose of this document is to outline the drug design process and specifically the role of computational modeling techniques. This is not meant to be a comprehensive review. It is meant to list the most important techniques currently in use.

The process of designing a new drug and bringing it to market is very complex. According to a 1997 government report, it takes 12 years and 350 million dollars for the average new drug to go from the research laboratory to patient use. Pieces of this process are often repeated to create successively better drugs for the same condition. In the case of antibiotics, drugs loose effectiveness as an immunity is built up, thus leading to a continuing "arms race". The major steps in the drug design process "from scratch" are.

1. FIND WHAT IS KNOWN

   Find out all that is known about the disease and existing or traditional remedies. It is also important to look at very similar afflictions and their known treatments.

2. DEVELOP AN ASSAY

   Develop an assay technique to test drug effectiveness. An ideal assay is one in which a compound can be added to tissue samples or micro-organism colonies and there will be a visible indication of an effective treatment. At worst, there must be a way to test the drug on a laboratory animal that is susceptible to the disease. If the only way to test the effectiveness of a trial compound is to inject an untested compound into a human subject then there is no way to proceed in finding a pharmaceutical treatment.

3. CONSIDER FINANCIAL ISSUES

   The next step is to make a financial decision about whether to proceed with the development process. The assay technique will determine the cost of testing compounds. If there are existing chemical treatments, it will be a refinement effort which saves the expense of finding lead compounds. All drugs must go through extensive testing so this is a fairly fixed cost. There may be governmental grants or tax incentives associated with certain diseases. The number of patients requiring treatment and merits of existing treatments will determine the long term profitability of producing a drug.
Steps 4 and 5 of this procedure are often performed simultaneously.

4. FIND LEAD COMPOUNDS

   Lead compounds are compounds that have some activity against a disease. These may be only marginally useful and may have severe side effects. However, the lead compounds provide a starting point for refinement of the chemical structures. Lead compounds may come from many sources, including
1. The isolation of active compounds from traditional remedies.
2. The testing of natural materials followed by an isolation effort.
3. Drugs effective against similar diseases.
4. Use of combinatorial chemistry techniques which produce large numbers of related chemical compounds. This allows testing a large number of compounds at once. When a mixture that is useful is found, a separation must be done to determine which of the related structures has some drug activity. This has been one of the most promising and rapidly growing techniques in recent years.
5. Searching chemical databases to find compounds similar to those found by the above means. This is the only part of the lead finding process that is considered to be a computational technique. There are many different measures of molecular similarity and ways of efficiently handling large databases, so this is not yet a trivial step.

5. ISOLATE THE MOLECULAR BASIS FOR THE DISEASE

   If it is known that a drug must bind to a particular spot on a particular protein or nucleotide then a drug can be tailor made to bind at that site. This is often modeled computationally using any of several different techniques. Traditionally, the primary way of determining what compounds would be tested computationally was provided by the researchers' understanding of molecular interactions. A second method is the brute force testing of large numbers of compounds from a database of available structures.

   More recently a set of techniques, called rational drug design techniques or De Novo techniques have been used. These techniques attempt to reproduce the researchers' understanding of how to choose likely compounds built into a software package that is capable of modeling a very large number of compounds in an automated way. Many different algorithms have been used for this type of testing, many of which were adapted from artificial intelligence applications. No clear standard has yet emerged in this area so it is impossible to say what is best the best technique at this time.

   These techniques have seen quite a bit of active development in recent years. Unfortunately, the complexity of biological systems makes it very difficult to determine the structures of large biomolecules. Ideally a x-ray chrystallography structure is desired, but biomolecules are very difficult to chrystalize. Another very useful technique, called "distance geometry" is to find some of the internuclear distances using NMR Nuclear Overhauser Effect experiments then find molecular geometries that have these distances. If only a protein sequence is known, there are many techniques for predicting how that protein will fold, but none has yet been shown to be 100% reliable. Even once a structure has been determined, identifying the site where a drug must bind is not a trivial task.

   The difficulty in find geometries makes it possible to bring first generation drugs to market by refinement of lead compounds without ever knowing the target site for the drug in the body. As such, these techniques are being used primarily for designing improved treatments for diseases that have already been characterized extensively.

6. REFINE DRUG ACTIVITY

   Once a number of lead compounds have been found, computational and laboratory techniques have been very successful in refining the molecular structures to give a greater drug activity and fewer side effects. This is done both in the laboratory and computationally by examining the molecular structures to determine which aspects are responsible for both the drug activity and the side effects.

   Synthetically, functional groups are removed in order to find out which must be present to give a useful drug and which are not necessary. The back bone of the structure is made more flexible or more rigid. A rigid back bone may hold the functional groups in the exact alignment necessary for the drug to bind. A flexible back bone may be necessary to allow the drug to get into the binding site. Adding bulky groups at other points on the molecule is often done in the hopes that these new groups may hinder the molecule from binding at unwanted sites which are responsible for the side effects.

   Computationally, the technique used is known as QSAR (Quantitative Structure Activity Relationships). It consists of computing every possible number that can describe a molecule then doing an enormous curve fit to find out which aspects of the molecule correlate well with the drug activity or side effect severity. This information can then be used to suggest new chemical modifications for synthesis and testing.

   Another important aspect of the molecular structure is its solubility. Whether the molecule is water soluble or readily soluble in fatty tissue will affect what part of the body it becomes concentrated in. The ability to get a drug to the correct part of the body is an important factor in its potency.

   Ideally there is a continual exchange of information between the researchers doing QSAR studies, synthesis and testing. These techniques are frequently used and often very successful since they do not rely on knowning the biological basis of the disease which can be very difficult to determine.

7. DRUG TESTING

   Once a drug has been shown to be effective by an initial assay technique, much more testing must be done before it can be given to human patients. Animal testing is the primary type of testing at this stage. The scientists doing the testing must be particularly observant of many little details since this is where unexpected side effects can be found. Another question to be answered is whether the drug will work well or poorly with other drugs. This is also where initial data necessary to determine correct dosages is obtained.

   Eventually, the compounds which are deemed suitable at this stage are sent on to clinical trials. In the clinical trials, additional side effects may be found and human dosages are determined. The typical testing process goes like this.

   1. Preclinical testing in animals and test tubes. This takes an average of 6.5 years. Only one compound in 1000 is sent on to clinical testing.
   2. Phase I clinical trials in a few human volunteers. This typically takes a year and a half. Seventy percent of the compounds are sent on to the next step. This is primarily a safety test.
   3. Phase II clinical trials in a few hundred patients. This takes two years and a third of the compounds are passed on to the next step. This is further safety testing and an initial examination of the ability of the drug to have the intended effect in humans.
   4. Phase III clinical trials in a few thousand patients. This step collects more data on safety, dosage, drug activity and side effects. About a quarter of the compounds pass this phase.
   5. An advisory panel of doctors reviews the data and makes recommendations to the FDA.
   6. FDA approval or rejection.
   7. The FDA continues to monitor drug performance long after approval has been given.

8. FORMULATION

   Before a drug can be produced, there must be a means to administer it. Ideally, a tasteless or bland tablet can be created. Alternatively, an oral liquid, intravenous injection or directly applied cream may be created.

   Tablets are created by adding other compounds to minimize stomach upset and control timed release of the drug. A tablet may also have a compound which is a matrix that helps it hold it's shape without crumbling into a powder.

   Oral liquids are often combined with strong flavors and alcohol to mask the taste of the drug and prevent throat irritation.

   A cream may have to be thickened or have a component that the skin will absorb readily.

9. PRODUCTION

   The large scale production of complex molecules can be very difficult. Compounds originally isolated from natural products may continue to be harvested. Often natural products are found in nature only in extremely small quantities necessitating a complex synthesis. One route that has been under development more recently is to have compounds produced by genetically engineered micro-organisms or plants.

   Drugs have a high value per gram. As such production techniques can be viable even though they are far more inefficient than those used by bulk chemical producers. Often all possible production techniques are researched even though only one will be put into practice. This is done so that there are no openings for competing corporations to get around a manufacturers patents by using a different technique.

   Manufacturing regulations have become much more stringent in recent years. It is now also important to determine what by-products will result from production and what environmental impact there will be. It is possible to have a case in which a less efficient manufacturing process is more profitable due to the value of side products and reduced waste disposal costs.

10. MARKETING

    If there is only one available treatment for a disease, it is only necessary to see that physicians know about it. If there are several competing treatments, there may be quite a bit of marketing done so that physicians will understand the relative merits of each.

11. NON-PERSCRIPTION SALES

    After a large amount of experience under a physicians supervision, a drug may be approved for over-the-counter sales. This is often the biggest profit making end of the pharmaceutical industry.

12. GENERIC PRODUCTION

    Once the chemical patents have expired, a drug can be produced by any manufacturer. Generic drugs are often less expensive for the consumer and yield a low profit margin for the producer. The production of generic drugs favors the most cost effective production process.

REFERENCES

A good book over all, and chapter 7 in particular, is
G. L. Patrick "An Introduction to Medicinal Chemistry" Oxford (1995)

A recent review is

L. M. Balbes, S. W. Mascarella and D. B. Boyd, in "Reviews in Computational Chemistry, Vol. 5" K. B. Lipkowitz, D. B. Boyd, Eds., VCH, 337 (1994)

An introduction to computational techniques is

G. H. Grant, W. G. Richards "Computational Chemistry" Oxford (1995)

A more detailed description of computational techniques is

A. R. Leach "Molecular Modelling Principles and Applications" Longman (1996)

L. Balbes' "Guide to Rational (Computer-aided) Drug Design" is at

gopher://www.ccl.net/00/documents/drug.design.guide

There are many links on Soaring Bear's web page at

http://ellington.pharm.arizona.edu/%7Ebear/

An introduction to structure-based techniques is

I. D. Kuntz, E. C. Meng, B. K. Shoichet Acct. Chem. Res. 27 (5), 117 (1994)

An introduction to De Novo techniques is

S. Borman Chemical and Engineering News 70 (12), 18 (1992)

There is more information about clinical testing at

http://rarediseases.info.nih.gov/ord/ct-info-patient.html

and http://rarediseases.info.nih.gov/ord/ct-about.html

An expanded version of this article will be published in "Computational Chemistry: A Practical Guide for Applying Techniques to Real World Problems" by David Young, which will be available from John Wiley & Sons in the spring of 2001.

Combinatorial Chemistry: A Strategy for the Future

NOTICE: This article contains material which originally appeared in the March 1995 issue of the Molecular Connection

For over a year, "combinatorial chemistry" has been discussed throughout the pharmaceutical and biotechnology industries. At MDL, the anticipated release of Project Library highlights MDLI's commitment to this field. But what exactly is combinatorial chemistry?

Combinatorial chemistry is one of the important new methodologies developed by academics and researchers in the pharmaceutical, agrochemical, and biotechnology industries to reduce the time and costs associated with producing effective, marketable, and competitive new drugs. Simply put, scientists use combinatorial chemistry to create large populations of molecules, or libraries, that can be screened efficientlyen masse. By producing larger, more diverse compound libraries, companies increase the probability that they will find novel compounds of significant therapeutic and commercial value. The field represents a convergence of chemistry and biology, made possible by fundamental advances in miniaturization, robotics, and receptor development. And not surprisingly, it has also captured the attention of every major player in the pharmaceutical, biotechnology, and agrochemical arena.

While combinatorial chemistry can be explained simply, its application can take a variety of forms, each requiring a complex interplay of classical organic synthesis techniques, rational drug design strategies, robotics, and scientific information management. This article will provide a basic overview of existing approaches to combinatorial chemistry, and will outline some of the unique information management problems that it generates.

Approaches to Combinatorial Chemistry

As with traditional drug design, combinatorial chemistry relies on organic synthesis methodologies. The difference is the scope--instead of synthesizing a single compound, combinatorial chemistry exploits automation and miniaturization to synthesize large libraries of compounds. But because large libraries do not produce active compounds independently, scientists also need a straightforward way to find the active components within these enormous populations. Thus, combinatorial organic synthesis (COS) is not random, but systematic and repetitive, using sets of chemical "building blocks" to form a diverse set of molecular entities. Scientists have developed several different COS strategies, each with the same basic philosophy--stop shooting in the dark and instead, find ways to determine active compounds within populations, either spatially, through chemical encoding, or by systematic, successive synthesis and biological evaluation (deconvolution).

There are three common approaches to COS. During arrayed, spatially addressable synthesis, building blocks are reacted systematically in individual reaction wells or positions to form separated "discrete molecules." Active compounds are identified by their location on the grid. This method has been applied in scale (as in the Parke-Davis Pharmaceutical DIVERSOMER technique), as well as in miniature (as in the Affymax VLSIPS technique). The second technique, known as encoded mixture synthesis, uses nucleotide, peptide, or other types of more inert chemical tags to identify each compound.

During deconvolution, the third approach, a series of compound mixtures is synthesized combinatorially, each time fixing some specific structural feature. Each mixture is assayed as a mixture and the most active combination is pursued. Further rounds systematically fix other structural features until a manageable number of discrete structures can be synthesized and screened. Scientists working with peptides, for example, can use deconvolution to optimize, or locate, the most active peptide sequence from millions of possibilities. You could say that combinatorial chemistry is a technologically advanced way of finding a needle in a haystack. The whole idea is to remove the guesswork and instead, to create and test as many compounds or mixtures as possible--logically and systematically--to obtain a viable set of active leads.

Managing Combinatorial Chemistry Libraries

As with traditional drug design, the ability to integrate different types of chemical, biological, and corporate information is crucial to combinatorial chemistry techniques. But combinatorial chemistry also generates an enormous amount of information which present day information systems have a hard time managing. Combinatorial chemists also ask different questions in different ways, and their information systems need to adapt to find these answers quickly.

For example, chemists planning a traditional synthesis typically conduct a retrosynthetic analysis to determine the best, and perhaps cheapest, way to obtain the target. And while combinatorial chemists also look at retrosynthetic trees to build combinatorial libraries, their priorities differ. "By which modes of forward synthesis are the most building blocks available or obtainable?" they might ask. "And if I allow the synthesis to proceed by this course, what is the scope and reliability of the necessary reactions?" Combinatorial chemists need a way to access this type of reaction information efficiently. In addition, one of the largest bottlenecks in the construction of combinatorial libraries is in obtaining the basic building blocks necessary to run each reaction. Chemical information systems that can quickly access updated databases of inventory and commercially available reagents are invaluable tools in reagent acquisition (see Figure A).

An Archival Revolution

Once built, combinatorial libraries produce unprecedented amounts of information. Reaction histories for each compound must be archived. Robots and other laboratory instruments need to be controlled, and the data they acquire archived for future reference. Scientists need to integrate screening results and biological data with structural information. As in single-molecule archival systems, the archival of combinatorial libraries and their corresponding data is essential to cost-effective research and development. Basic archival and reporting capabilities can provide managers with the facts they need to justify the costs associated with combinatorial chemistry. And researchers can use scientific information management systems to avoid past mistakes, learn from previous successes, and answer critical questions, such as, "How much of this library overlaps with other corporate libraries?" or "Which building blocks have proven most successful?" or "What is the difference between the biological performance of a molecule produced in a mixture rather than in a discrete format?"

Combinatorial chemistry is a promising new field that stands to revolutionize the chemical industry, and demands completely new scientific information management solutions. Combinatorial chemists will be able to meet their goals if they can find ways to plan libraries quickly, produce libraries that better interrogate biological assays, and learn from past screening results. Using software that can orchestrate the planning, building, screening, and interpretation of synthesized libraries, combinatorial chemistry programs will begin to realize their promise of minimizing the time and cost associated with bringing new molecular entities to market.

Project Library: A New Tool for a New Paradigm in Drug Design

Proper management of combinatorial chemistry libraries requires an application that understands the science behind combinatorial chemistry while managing the chemical and biological data generated by combinatorial chemistry programs. To meet these ends, MDL plans to release Project Library, a complete, ready-to-use desktop software application that supports the multiple combinatorial chemistry research methods in use today, including functions such as:

• Storage of both oligomeric and non-oligomeric structures.
• Tracking of mixture and discrete compound libraries.
• Elucidation of mixtures or discrete compounds from a library derived from any of the active identification strategies in use today.

In addition, information concerning the components or building blocks of the library must be processed, stored, and tracked. And because very large numbers of novel structures have to be considered when designing new combinatorial libraries, researchers must be able to enumerate experimental or virtual libraries (groups of either subgeneric or fully specified structures derived from a single generic structure) for investigation and planning.

MDL's Project Library is an application that not only helps researchers to manage combinatorial libraries, their building blocks, and their associated data at the project level, it also allows them to plan and refine combinatorial libraries by making it easy to build, store, and export "virtual" libraries.

Combinatorial Chemistry Project Management

In order to track information for both mixture and discrete compound libraries, a combinatorial chemistry project management tool must allow the researcher to associate data with:

• the library itself (represented as a generic structure, or parent library) [see Figure 1];
• mixtures of compounds within the library (represented as subgenerics, or child libraries) [seeFigure 2];
• individual compounds in the library [see Figure 2].

Project Library supports each of these activities by allowing combinatorial researchers to organize libraries into project databases, where information on parent and child libraries and discrete compounds can be stored and evaluated and associations between them automatically managed. A quick-loading feature makes it easy to load tables of administrative, biological, physical, and encoding data to the appropriate parent library, child library, or specific structure, all of which are fully searchable by structure or associated data.

Working with Project Library's tools, you can quickly build generic structures--representing hundreds of thousands of compounds-- and assign names and encoding information to the components. Then, using substructural, encoding, or other data constraints, you can search for specific structures within the library [see Figures 1 and 2].

Intelligent Enumeration

Enumeration is the process of automatically generating either subgeneric or fully specified structures (individual compounds) from a generic structure. Enumeration of a parent library enables the researcher to produce structural representations of child libraries, or discrete compounds within the library. Project Library not only generates the appropriate structures on demand, but also automatically maintains the relationships between parent, child, and specific structures. (Data inherited by the child library or specific structure may include encoding, component names, and parent library information.)

This ability to do "intelligent" enumeration also enables Project Library to support the three methods commonly used today to identify the structure of active components within a combinatorial library:

• Arrayed, spatially addressable synthesis (active compound identified by molecular weight, components, or location) [see Figure 3];
• Encoded mixture synthesis using nucleotides, peptides, or other chemical tag (active compound identified by its tag) [see Figure 4];
• Deconvolution (active compound identified by iterative synthesis of mixtures and subsequent screening) [see Figure 5]

Project Library's special navigation tools allow the researcher to move between enumerated parent libraries, child libraries, and specific compounds with ease.

Reagents and Building Blocks

In combinatorial chemistry, the ability to manage the building blocks that make up the libraries is just as important as the ability to manage libraries themselves [see Figure 6]. So, Project Library allows researchers to assign names and codes to specific building blocks incorporated into a library, and to store the blocks and other associated data for future use.

Special processing tools in Project Library let researchers manipulate lists of reagents and quickly turn them into building blocks [see Figure 7]. Also, being able to search for building block structures makes it possible for researchers to gain new insight into the biological effectiveness of individual building blocks by tracking their success across libraries and screens.

Communication Management

Because tracking data means everything in combinatorial chemistry, researchers must be able to enter data by themselves and access it by themselves. They also must be able to generate reports when necessary, and make the data readily available to all other members of the research team.

Using the guided, graphical user interface, researchers can enter data into Project Library, generate standard reports at the click of a button, or easily export data into word processor programs for custom reports. They can also perform data analysis by building a spreadsheet, complete with structures and data for SAR work, or export the data into other software programs for analysis. Project Library runs on Microsoft Windows and Apple Macintosh computer systems.

Cost Management

The process of finding novel, active compounds through combinatorial chemistry is akin to finding the proverbial needle in a haystack, using an array of technologically sophisticated tools. Project Library can help research management contain R&D costs in several ways.

First, Project Library can help you manage the information generated from your automated systems. Capital investment in robots for combinatorial synthesis and high-throughput screening runs high, and laboratories cannot afford the bottlenecks in information processing caused by inadequate data handling. Project Library allows researchers to drive the enumeration of specific structures from a library based on information generated from an ASCII robot file. Users can specify, for example, the components used or location of samples on plates. Project Library also makes it possible to write an ASCII file that can be used to program robots to synthesize compounds elucidated from virtual libraries.

Project Library can also help management plan and manage the entire combinatorial process. Rather than creating an enormous paper trail, Project Library can be used to track reagent costs, planning duration, building duration, and screening effectiveness. Project Library also keeps the combinatorial chemistry work flow going smoothly, complementing MDL's current range of scientific information management products and databases to help combinatorial researchers at every stage of the process.

MDL has done more than create a new software solution with Project Library--it has introduced a new way of developing software. MDL chose to develop Project Library in two phases. Through extensive industry research and a software pilot program, MDL gained an understanding of the information needs presented by combinatorial chemistry programs and ascertained the type of contribution that was needed by this growing field. During the industry research phase, MDL technically assessed the industry requirements and the science involved in combinatorial chemistry by visiting 50 companies in the pharmaceutical, biotechnology, and agrochemical industries. There, MDL interviewed the researchers involved in combinatorial chemistry programs. After discussing possible software solutions on paper, MDL developed prototype software to encourage information exchange and refine software requirements.

In the second phase, MDL placed the prototype software in research groups at pilot sites. Combinatorial chemists were trained on the prototype and asked to use the software in their daily routine. Feedback from this portion of the process helped shape Project Library--at the request of pilot researchers, MDL ensured that the software contains a guided user interface, supports multiple research methods seamlessly, and is complemented by other MDL software and scientific applications.

According to Dr. Sheila DeWitt, a senior research associate in the Bio-Organic chemistry group at Parke-Davis Pharmaceutical Research, the pilot process forged a unique partnership between Parke-Davis and MDL.

"The process was excellent. MDL was always willing to work with us and talk to us about what we needed the software to do. The opportunity to play with a prototype gave our researchers a better idea of how the software would fit into their daily routine. When we encountered resource issues, MDL even provided a computer for us to use."

-- Dr. Sheila DeWitt, Parke-Davis Pharmaceutical Research

Pilot sites are currently working with MDL to refine the beta version of Project Library. The success of this approach to project development has led MDL to plan to additional pilot programs in the development of future MDL solutions.

Figure A

Information sources useful throughout the planning of a combinatorial library. Databases of reaction methodology, reagent availability, and relevant prior-art are extremely useful in combinatorial synthesis. Also portrayed is data capture, discussed in the article on Project Library.

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Return to the paper.

Figure 1

Project Library allows researchers to archive an entire library as a single, searchable generic structure. Here, a single generic structure represents a complete library of benzodiazepine compounds.

Note that in addition to structural information, component ID information is stored (e.g. BZA, MeInd, MeOPh, etc.) (Benzodiazepine library from: DeWitt, et. al., Proc. Natl. Acad. Sci. USA, Vol. 90, pp. 6909-6913, Aug. 1993).

Return to the paper.

Figure 2

Using selective enumeration capabilities available in Project Library, the researcher can automatically create subgeneric or specific compounds from a generic structure.

Here, the subgeneric structure is generated from the benzodiazepine library in Figure 1 by asking Project Library to enumerate R1 = Me and R2 = Ph. The specific structures shown below are four of the 90 created when the library is fully enumerated.

Note that in addition to automatically creating the structures, Project Library automatically creates the appropriate structure identification string from the component names in the original generic structure (e.g. BZA-MeInd-Ph-R3-R4, BZA-Bzl-Chx-H-H, etc.)

Return to the paper.

Figure 3

"Intelligent" enumeration capabilities in Project Library allow the researcher to elucidate quickly the exact structure of active compounds within libraries synthesized using arrayed, spatially addressable synthesis.

With this technique, active compounds are identified by molecular weight, components, or location. Here, Project Library automatically generates the structures for two benzodiazepines from the library above that have a molecular weight between 360 and 370.

Return to the paper.

Figure 4

Because Project Library allows the researcher to associate names and encoding information with individual components of a library, the active structure(s) from an encoded mixture synthesis can be automatically enumerated using a component or encoding string.

Shown here is the specific structure corresponding to a binary synthesis code (110- 101-011-100-010-001) obtained from a gas chromatogram of the tags from an active bead from the library.

Encoded peptide library from: Ohlmeyer, M.H.J., et. al., Proc. Natl. Acad. Sci. USA, Vol. 90, pp. 10922-10926, Dec. 1993.

Return to the paper.

Figure 5

Project Library supports deconvolution experiments through selective enumeration of parent and child libraries which represent the mixtures synthesized and screened.

As structural features are fixed, the mixtures are assayed, and the most active combination is pursued until a manageable number of discrete compounds can be synthesized and screened. Here, Project Library tracks the history of a typical deconvolution experiment where the parent library is the original generic structure and child libraries (mixtures) are represented as subgeneric structures (where successive R-groups have been enumerated to produce each set of subgeneric structures).

Note that the associations between parent and child libraries are automatically tracked and data can be associated with either parent libraries, child libraries, or specific structures. (Non-encoded peptide library from: Houghten, R.A., et. al., Nature, Vol. 354, 7 Nov. 1991, pp. 84-86.)

Return to the paper.

Figure 6

In addition to managing libraries, Project Library allows the researcher to manage sets of building blocks used in library creation.

Reagents for combinatorial synthesis can be processed to form building blocks. Once the building blocks are saved in the database, they can easily be incorporated into a generic structure representing a library. The building blocks can be organized by compound class, and data such as name or encoding information can be associated with individual building blocks and is automatically incorporated into the generic structure.

Return to the paper.

Figure 7

Researchers can automatically process sets of reagents to produce building blocks based on rules they create.

Here, a saved rule (to clip acid halide leaving groups and add the appropriate attachment point) is applied to a list of acid chloride reagents automatically producing the corresponding building blocks. The building blocks can be saved in the database and incorporated into a generic structure representing a real or virtual library.