computational chemistry, data mining, high-throughput

Technical noteComputational chemistry, data mining,high-throughput synthesis and screening—informatics and integration in drugdiscovery{

Charles J. Manly

Discovery Technologies, Neurogen Corporation, Branford, CT, USA

Drug discovery today includes considerable focus of laboratoryautomation and other resources on both combinatorial chemistryand high-throughput screening, and computational chemistry hasbeen a part of pharmaceutical research for many years. The realbene¢t of these technologies is beyond the exploitation of eachindividually. Only recently have signi¢cant e¡orts focused one¡ectively integrating these and other discovery disciplines torealize their larger potential. This technical note will describeone example of these integration e¡orts.

Introduction

Neurogen Corp. is a pharmaceutical company focusingon central nervous system (CNS) disorders. Several yearsago, it began to develop methodology, now named` AIDDsm ’ (accelerated intelligent drug discovery), withthe aim of streamlining and optimizing:

. the generation of lead series;

. the exploration and characterization of lead series;

. the optimization of leads; and

. the optimization of clinical development candidates.

AIDDsm accomplishes this through tight integration (viaintranet deployed informatics) of combinatorial chemis-try, high-throughput pharmacology and computationalchemistry. AIDDsm itself is tightly integrated with thedrug-discovery eå ort and especially with medicinalchemistry itself (® gure 1).

The focus of AIDDsm is on the ability to enhance greatlythe drug-discovery cycle: synthesis, data generation, dataanalysis and modelling and prioritization of both syn-thesis and screeningÐ thus completing the cycleÐ onthousands of compounds every 2 weeks. Additionally,this is accomplished with:

. very small staå resources (20± 25 FTEs);

. the ability to synthesize 400 000 samples per year (aseither mixtures or individual samples) with puri® -cation and quality assessment;

. biological data generation of 300 000 samples permonth;

. a cycle time of 2 weeks;

. targeted eæ ciency gains through computationalchemistry and data-mining of 10£ to well over50£ over random; and

. the ability to prosecute 13± 15 programmes simul-taneously in the above manner.

Virtual library (� gure 2)

The AIDDsm virtual library is managed by Neurogen’ sISLANDSsm technology, and is a representation of allcompounds that can be made from the existing reactivefragment database and synthesis protocols database.Thus, this virtual library is a very speci® c and dynamicset of compounds that can easily be millions or billionsof molecules in size. The ISLANDSsm technology mana-ging the virtual library is key to AIDDsm virtual screen-ing processes as well as to work¯ ow operations. TheISLANDSsm software makes it possible to de® ne andregister 50 000 compounds from the virtual library easilyand quickly (10 min). After de® nition and registration,not only do the compounds exist electronically in data-bases for use in AIDDsm , but also ISLANDSsm hasgenerated all information required in the synthesis itself.The reagents required, the synthesis, reaction work-upand quality control protocols to be used by the synthesis

Journal of Automated Methods & Management in Chemistry , Vol. 23, No. 6 (November–December 2001) pp. 191–192

Journal of Automated Methods & Management in Chemistry ISSN 1463± 9246 print/ISSN 1464± 5068 online # 2001 ISLARhttp://www.tandf.co.uk/journals

{ This technical note was initially presented at the ISLAR 2000Conference and is reproduced here by kind permission of ZymarkCorporation. Figure 1.

191

robotics and all tracking information (sample number,plate number, well locations) have been automaticallygenerated and speci® ed with no further input from theuser required.

Virtual screening

A key concept of AIDDsm is the eå ective prioritization ofboth synthesis and screening resources through virtualscreening. Proprietary, unattended and continuous mol-ecular modelling and data-mining strategies termed ` on-line continuous modelling’ (OLCM) provide models forvirtual screening of both the virtual library and thearchive of actual compounds. These models work inconcert with ISLANDSsm for virtual screening of thevirtual library.

On-line continuous modelling

From the inception of our work on AIDDsm , we plannedto perform computational chemistry modelling with anovel portfolio approach. A portfolio of modelling stra-tegies could be expected to provide useful models in avariety of cases when no one strategy could be expectedto perform well in every situation. Compare this with astock portfolio where the expectation is that the portfoliowill increase in value with time even though this cannotbe expected of any one particular stock. The AIDDsm

portfolio of OLCM includes a variety of both chemicaldescriptor types and modelling methods. Fuzzy methodsand machine methods have been very eå ective. Bothartici® cal neural networks and recursive partitioningmethodologies are also used routinely in AIDDsm OLCMstudies.

A core principle of AIDDsm and OLCM is the prediction,prioritization and targeting of populations of compoundsinstead of individual compounds. This makes it possibleroutinely to achieve signi® cant bene® ts by increasing theprobability of activity in each 2-week cycle. Eæ ciencygains or targeting enhancements seen in AIDDsm fromthis approach are routinely 10£ to more than50£ enhancement.

Results

AIDDsm has been applied to over 20 diverse programmesat Neurogen. In almost every programme, the value ofAIDDsm has resulted in novel leads that were readilyoptimized to signi® cant levels of activity (® gure 3).

For the last few years, Nurogen has been applyingAIDDsm technology to the optimization of drug-likeproperties within projects toward the generation ofdevelopment candidates. OLCM models for several ofthese drug-like properties provide guidance for optimiza-tion of chemical series. These eå orts have resulted inmore eæ cient optimization of candidate ADME, toxico-logical and PK properties such as metabolic half-life,cytochrome P450 activity and others.

Summary

An overview of the AIDDsm drug discovery system atneurogen has been given. Speci® c examples from activeproject areas were presented. The importance of integra-tion of disciplines and of pragmatism in balancing theindividual components of drug discovery was stressed.

Figure 2. Figure 3.

C. J. Manly Information and integration in drug discovery

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