TRIPOS

SYBYL®

Expert Molecular Modeling Environment

The SYBYL suite of computational informatics software is

designed to enhance the drug discovery workflows and

decision-making of today’s computational chemists and

molecular modelers. An industry standard for over 20 years

and the basis for Tripos’ expert molecular modeling envi-

ronment, SYBYL provides the fundamental components for

understanding molecular structure and properties with an

emphasis on the discovery of lead candidates.

Computational chemists understand the impact that

continual innovation can bring to the technologies used in

their in silico discovery programs. They realize the benefits

of complementing their own chemical intuition with validated

science. Well-designed and frequently updated software

can save valuable time and often means the difference

between success and failure.Tripos’ premier computational

informatics offering, SYBYL, leads the way in innovation

with its ever-growing suite of technologies, commitment to

applied science, tradition of frequent product enhancements,

and industry-leading support.

Key Benefits

■ Constant and consistent scientific innovation

■ Comprehensive suite of task-based solutions

■ Fully integrated, easy-to-use interface

■ Industry-leading technical support

■ Comprehensive training workshops

■ Availability on multiple platforms

■ Flexible licensing options

SYBYL Base

Core set of applications and comprehensive tools for molecularmodeling that enable structure building, optimization, and comparison;visualization of structures and associated data; annotation, hardcopy,and screen-capture capabilities; and a wide range of force fields.SYBYL Base technologies are fully integrated with SYBYLapplications ensuring ease-of-use.

SYBYL Applications

Complete suite of computational chemistry applications that simplify and accelerate the discovery of lead compounds and development candidates. SYBYL applications include validated solutions for key computational chemistry and molecular modelingtasks such as ligand-based design, receptor-based design, structural biology, library design, and cheminformatics.

CONTENTS Page

SYBYL BASE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

SYBYL APPLICATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2-12

LIGAND-BASED DESIGN . . . . . . . . . . . . . . . . . . . . . . . . . . .2-3

SAR and ADME . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2

Pharmacophore Perception and Molecular Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3

RECEPTOR-BASED DESIGN . . . . . . . . . . . . . . . . . . . . . . . . .4-5

Docking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

de novo Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

STRUCTURAL BIOLOGY & BIOINFORMATICS . . . . . . . . . . . .6-7

LIBRARY DESIGN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8-9

Library Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8

Molecular Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9

CHEMINFORMATICS . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10-11

Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10

Structure Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11

SUPPLEMENTAL TECHNOLOGIES . . . . . . . . . . . . . . . . . . . . .12

SYBYL

COMPLETELY INTEGRATED ENVIRONMENT TO ENHANCE YOUR DRUG DISCOVERY RESEARCH

SYBYLExpert Molecular

ModelingEnvironment

Supplemental Technologies

Library Design

Library Creation

Molecular Diversity

Ligand-BasedDesign

Pharmacophore Perception & Molecular Alignment

SAR & ADME

Cheminformatics

Data Mining

Structure Representation

Receptor-Based Design

Docking

de novo Design

Structural Biology& Bioinformatics

SYBYL Base 1

The inhibitor, methotrexate, bound to dihydrofolatereductase. A MOLCAD-generated surface has beencreated for methotrexate and is color-coded by electrostatic potential. Methotrexate is rendered ascapped sticks, while the protein is shown as lines.

SYBYL Base

SYBYLComplete Computational Chemistry andMolecular Modeling Environment

SYBYL, the heart of Tripos’ expert molecular

modeling environment, provides the funda-

mental components for understanding

molecular structure and properties with a

special focus on the creation of new chemical

entities. SYBYL provides essential construc-

tion, editing, and visualization tools for both

large and small molecules. Data organization

and analysis rely on the Molecular

Spreadsheet™, which integrates chemical

information with standard data manipulation

tools. SYBYL’s programming language (SPL)

and open architecture facilitate customized

drug-design methods.

MOLCAD™Advanced Visualization of MolecularSurfaces and Properties

MOLCAD1 exploits the power of the human

eye by creating graphical images that reveal

the properties of molecules essential for

molecular recognition. Van der Waals and

solvent-accessible surfaces can be calcu-

lated, and a broad range of properties can be

mapped onto these surfaces: lipophilic

potential, electrostatic potential, hydrogen-

bonding ability, local curvature, and distance.

Dynamics™Easily Investigate and VisualizeConformational Space and Mobility

Dynamics provides a number of techniques

for producing ensembles of conformers for a

particular structure or following the evolution

of a system over time. It also provides the

graphical tools required for organizing and

analyzing the resulting conformations.

Advanced Computation™Explore the Conformational Properties of Compounds

Industrial-strength molecular design had its

real start with the Advanced Computation

tools from Tripos. This SYBYL application

delivers the industry’s fastest and most

flexible systematic conformational searching

algorithm. Random and grid search tech-

niques are also provided. The user selects

the technique based on the goal of the

conformational analysis.

Savol™Calculate Molecular Surface and VolumeProperties

Savol2 models the effects of molecular

size and shape on chemical processes.

For example, total molecular surface area

and total molecular volume are often useful

as descriptors in QSAR and QSPR models.

Using Savol’s calculations, such as accessible

atomic surface areas, to weight atomic

properties used in atom-based QSPRs

often yields significantly better results.

When combined with SYBYL

applications, SYBYL Base

provides a completely integrated

environment for computational

chemistry and molecular modeling.

SYBYLBase

ENHANCED

Ligand-based design techniques

use information about one or several

known actives (ligands) as a basis

for the design of lead compounds.

Tripos’ core science and workflow-

centric applications address critical

ligand-based design tasks such

as structure-activity relationship

modeling, pharmacophore hypothesis

generation, molecular alignment, and

ADME prediction.

Ligand-BasedDesign

SYBYLBase

QSAR with CoMFA®

Build Predictive Structure-Activity andStructure-Property Models

QSAR with CoMFA builds statistical and

graphical models that relate the properties of

molecules (including biological activity) to

their structures. These models are then used

to predict the properties or activity of novel

compounds. Tripos’ patented Comparative

Molecular Field Analysis (CoMFA) has been

used as the method of choice in hundreds of

published QSAR studies. A wide variety of

structural descriptors can be calculated,

including EVA and the molecular fields of

CoMSIA.3

Advanced CoMFA®

Refine and Enhance 3D QSAR Models

Advanced CoMFA provides access to

specialized types of CoMFA fields that

assist in refinement of predictive models.

HQSAR™Perform Automated QSAR Analyses

Hologram QSAR (HQSAR) uses molecular

holograms and PLS to generate fragment-

based structure-activity relationships. Unlike

3D-QSAR methods, HQSAR does not require

alignment of molecules, allowing automated

analysis of very large data sets. Validation

studies have shown that HQSAR has predic-

tive capabilities comparable to those of much

more complicated 3D-QSAR techniques.

VolSurf™Predict ADME Properties

VolSurf 4 predicts ADME properties using

pre-calculated models; computes unique,

ADME-relevant descriptors; and creates

QSAR models of bioactivity or property.

VolSurf’s built-in ADME models have been

developed from published experimental data

and include Caco-2 cell absorption, human

CYP3A4 metabolic stability, hERG inhibition,

human serum albumin binding, thermodynamic

solubility, blood-brain barrier permeation,

and more.

2 Ligand-Based Design

Ligand-Based Design

SAR AND ADME

The hydrophilic surface of an anti-inflammatory drugas determined by VolSurf.

Almond™Calculate and Utilize Alignment-IndependentMolecular Descriptors

Almond 4 generates and uses GRIND

descriptors (GRid INdependent Descriptors).

GRIND are alignment-independent 3D

descriptors interpretable with the help of

interactive graphic tools. This new genera-

tion of 3D molecular descriptors is useful in

3D-QSAR, virtual screening, and design

of combinatorial libraries.

Distill™Determine and Visualize SARs

Distill clusters compounds according to their

common substructures and displays the results

to reveal structure-activity relationships. The

interactive dendrogram produced by Distill

allows visual comparison of compounds in the

same cluster or between compounds in differ-

ent clusters. Statistical tools assess the impact

of substructure on property or activity.

Molconn-Z™Compute Descriptors for Use in QSAR/QSPRModels to Produce Chemically InterpretableResults

Molconn-Z5 calculates structural descriptors

for use with statistical methods in order to

create QSAR and QSPR models that predict

biological activity or physical properties of new

molecules. Given the structure of a molecule,

Molconn-Z calculates more than 300 structure-

based descriptors that are useful in chemical,

biological, and environmental studies.

ClogP/CMR™Include Molar Refractivity and logP in QSARand ADME Models

The ClogP/CMR6 application provides highly

accurate calculated logP and molar refractivity

values. Octanol/water partition coefficients

(logP) are often a critical piece of information

in the compound discovery process. Molar

refractivity (MR) serves as a convenient

estimate of molecular polarizability. Both

properties are useful descriptors in establishing

QSAR relationships.

COMPLEMENTARY TECHNOLOGIESFOR LIGAND-BASED DESIGN

Receptor-Based DesignSurflex-DockCScoreEA-Inventor

Library DesignDiverseSolutionsLegion/CombiLibMakerOptDesignSelector

CheminformaticsConcordConfortMM4UNITY

GALAHAD™Generate Rapid, High-Quality PharmacophoricPerception and Molecular Alignments

GALAHAD7 allows researchers to automati-

cally develop pharmacophore hypotheses

and structural alignments from a set of

molecules that bind at a common site. No

prior knowledge of pharmacophore elements,

constraints, or molecular alignment is

required, making it ideal for exploring new

targets and new modes of action. GALAHAD

uses a sophisticated new genetic algorithm

(GA) that defines each molecule as a core

structure plus a set of torsions. To overcome

limitations in existing pharmacophore tools,

GALAHAD’s genetic algorithm was

developed on real-world data sets.

Tuplets™Pharmacophore-Based Virtual Screening

Tuplets facilitates the retrieval of compounds

from molecular structure databases that are

likely to exhibit biological activity. Additionally,

Tuplets can be used to prioritize virtual

combinatorial libraries for pharmaceutical

research and can provide a biasing

descriptor to allow the design of focused

combinatorial libraries.

GASP™Use Full Conformational Flexibility toDevelop Pharmacophore Hypotheses

GASP8 performs pharmacophore elucidation

without requiring prior knowledge of

pharmacophore elements or constraints.

Using a genetic algorithm, GASP automati-

cally allows conformational flexibility and

maps features among molecules.

DISCOtech™Elucidate Pharmacophore Models fromPrecalculated Conformers

DISCOtech9 performs pharmacophore

elucidation from a set of active compounds.

Starting from a set of representative

conformers for each molecule, DISCOtech

considers all possible mappings of features

to create a set of alignments, each of which

is a hypothesis for the pharmacophore and

its geometry.

Ligand-Based Design 3

PHARMACOPHORE PERCEPTION AND MOLECULAR ALIGNMENT

GALAHAD model derivedfrom four cyclin-dependentkinase (CDK2) inhibitors(left) vs. the overlay basedon the corresponding X-raycrystal structures (right).

ENHANCED

Receptor-Based Design

Surflex-Dock™Ligand-Receptor Docking and VirtualScreening

Surflex-Dock10 offers unparalleled enrich-

ments in virtual high-throughput screening

combined with state-of-the-art speed,

accuracy, and usability. It uses an empirical

scoring function (based on the Hammerhead

docking system) that has been updated and

re-parameterized with additional negative

training data, along with a search engine

that relies on a surface-based molecular

similarity method.

Surflex-Dock employs an idealized active

site ligand, called a protomol, as a target

to generate putative poses of molecules or

molecular fragments. These putative poses

are scored using the Hammerhead scoring

function, which also serves as an objective

function for local optimization of poses.

Flexible docking proceeds by a crossover

procedure that combines pieces of poses

from intact molecules. Molecular alignment

is done through a patented similarity-based

algorithm.

The Surflex-Dock scoring function is a linear

combination of non-linear functions of protein-

ligand atomic surface distances. Protein-ligand

interactions include steric, polar, entropic,

and solvation terms.

CScore™Rank the Affinity of Compounds Bound to a Target with Consensus Scoring

CScore uses multiple types of scoring

functions to rank the affinity of ligands bound

to the active site of a receptor. The strengths

of individual scoring functions combine to

produce a consensus that is more robust

and accurate than any single function for

evaluating ligand-receptor interactions.

DOCKING

4 Receptor-Based Design

Receptor-BasedDesign

SYBYLBase

Influenza virus neuraminidase (1B9V) in complex withan inhibitor (purple-capped sticks). The minimizedinhibitor has been redocked by Surflex-Dock into theprotein (yellow-capped sticks) with an rms deviationof 0.645 Angstroms.

Receptor-based design techniques

use information about the structure

of a drug target (receptor) as a basis

for the design of lead compounds.

Tripos’ core science and workflow-

centric applications address critical

receptor-based design tasks such as

ligand docking, virtual screening,

de novo design, and lead optimization.

NEW

EA-Inventor™Invent New Compound Ideas and Lead HopUsing Novel de novo Design Engine

EA-Inventor2 is a new and different approach

to de novo design. It enables researchers to

invent new compounds, new R-groups

around a fixed scaffold, or new scaffolds.

Unlike typical de novo design programs,

EA-Inventor gives the user control over how

the new structures are generated and scored,

and is useful for in silico lead discovery, lead

exploration, and lead- or scaffold-hopping.

RACHEL™Sophisticated Tools for Optimization of Lead Compounds

Starting from a ligand/receptor complex,

RACHEL11 performs automated combinatorial

optimization of lead compounds by systemi-

cally derivatizing user-defined sites on the

ligand. These compounds are conformationally

searched within the active site, evaluated, and

only those that bind tightly with the receptor

are retained. This new population of com-

pounds is then processed to form the next

generation of derivatives. Over time, a lead

compound is iteratively refined into a set of

high-affinity structures.

LeapFrog®

Perform de novo Ligand Design

LeapFrog uses a receptor site or a CoMFA

model as the basis for de novo ligand

design, and can be used to optimize lead

compounds or to generate novel structures.

In stepwise fashion, LeapFrog makes small

structural changes to an existing ligand or

creates new ligands from scratch. Ligand

structures are evaluated based on their

binding energies relative to their immediate

precursor.

de novo DESIGN

The X-ray structure of wild type tern N9 influenzavirus neuraminidase (2QWK) shown with five ligands generated using RACHEL that are predictedto be active.

Receptor-Based Design 5

COMPLEMENTARY TECHNOLOGIESFOR RECEPTOR-BASED DESIGN

Ligand-Based DesignAdvanced CoMFAGALAHADQSAR with CoMFAVolSurf

Structural Biology & BioinformaticsAdvanced Protein ModelingBiopolymer

Library DesignLegion/CombiLibMaker

CheminformaticsConcordConfortMM4UNITY

ENHANCED

Structural Biology & Bioinformatics

Biopolymer™Predict, Build, Visualize, and AnalyzeMacromolecular 3D Structure

Biopolymer provides the tools needed for

building and manipulating peptide, protein,

DNA/RNA, and carbohydrate structures.

Biopolymer is fully integrated with SYBYL

for structure building, refinement, visualiza-

tion, and analysis. Protein structures can

be analyzed in detail using the SYBYL

Molecular Spreadsheet.

Structures, spreadsheets, and graphs are

linked to help identify and explore correlations

between physical and geometric attributes.

Interesting or unusual residues can be

colored-coded in a structure. Any value

computed can be visually mapped onto 3D

tube or ribbon drawings by varying either

the tube color or thickness.

Identification of potential binding sites within

or at the surface of biological targets can

also be determined using a cavity detection

algorithm. Protein pockets are rapidly identified

by solvating the structure to locate regions

where solvent-spheres tend to cluster.

Biopolymer’s rendering of the methyl glycoside of theLewis b human blood group determinant complexedwith Lectin IV of Griffonia simplicifolia.

6 Structural Biology & Bioinformatics

Structural Biology &

Bioinformatics

SYBYLBase

Structural biology techniques aid

discovery researchers in the study

of protein structure and function.

Predicting, constructing, and

assessing 3D protein models are

critical components to a better

understanding of receptor-ligand

interactions. Tripos applies homolog

recognition, structure and function

prediction from sequence, ligand

binding site analysis, and 3D modeling

techniques in workflow-centric

applications that address critical

structural biology design tasks

such as macromolecular modeling

and bioinformatic analysis.

ENHANCED

Advanced Protein ModelingFind Homologs by Sequence-StructureComparison and Construct 3D Models fromProtein Sequences

Advanced Protein Modeling enables the

user to perform both homolog finding and

comparative modeling through a streamlined

interface. The recognition of homology

between protein sequences and known

structures provides invaluable information

toward understanding the biological behavior

and biochemical function of uncharacterized

sequences. This recognition also enables

prediction of three-dimensional structures

through comparative modeling.

Using the FUGUE™12 technology, structural

homologs of a target sequence can be

identified by sequence-structure comparison

using a database of detailed structural profiles

of all known protein families. The key ele-

ments of FUGUE are environment-specific

substitution tables, structure-dependent gap

penalties, automated alignment method

selection, and HOMSTRAD, a database

of protein-structure alignments.

Using the ORCHESTRAR™13 technology,

comparative models can be built from a

target sequence using single or multiple

structural homologs found by FUGUE or

provided by the user. The ORCHESTRAR

interface in SYBYL allows the user to

perform sequence alignment and homolog

structural alignment, identify structurally

conserved regions, model loops (structurally

variable regions) using knowledge-based

or ab initio approaches, and to model

sidechains.

Key elements include the use of local

structural environment and geometric criteria,

homology and rmsd, and detailed analysis

tools for model analysis and refinement.

Structural Biology & Bioinformatics 7

COMPLEMENTARY TECHNOLOGIESFOR STRUCTURAL BIOLOGY

Receptor-Based DesignCScoreSurflex-DockEA-Inventor

A set of structurally aligned oxidoreductase structuresof 8% sequence identity, with the structurally conserved regions highlighted in gray.

A section of a FUGUE sequence alignment of target prpB with the isocitrate lyase family from HOMSTRAD decorated using JOY annotations.

NEW

Chemical library design techniques

allow researchers to develop

combinatorial or focused compound

collections useful in lead identification

and optimization. Truly diverse, repre-

sentative, and synthetically feasible

compound sets speed the identification

of active small molecules. Tripos’

core science and workflow-centric

applications address critical library

design tasks such as library creation

and molecular diversity enhancement.

Library Design

Legion™/CombiLibMaker™Construct Virtual Compound Libraries

Legion and CombiLibMaker2 provide the

capability for building and storing combin-

atorial libraries of compounds. Libraries of

compounds can be defined and enumerated

with full control of stereochemistry. Library

structures can be output singly (as SLNs)

or in a searchable combinatorial format

(as cSLN). The libraries can be stored in

a variety of formats.

OptDesign®

Design and Edit Combinatorial Libraries

Starting from a virtual combinatorial library

(cSLN file), OptDesign selects compounds

that balance the practical constraints of

combinatorial library design, such as cost

and ease of synthesis, with diversity and

representativeness, thereby performing true

double-objective optimization.

LIBRARY CREATION

8 Library Design

LibraryDesign

SYBYLBase

A virtual library of potential matrix metalloproteinaseinhibitors built using CombiLibMaker. The core common to all products is shown in the foreground.R1, R2, and R3 are the sites of variation.

Selector™Characterize and Sample CompoundLibraries

Selector characterizes, compares, and

samples sets of compounds. Clustering tools

identify relationships between compounds

based on their similarity. Selector can create

diverse or representative subsets, filter

compound lists based on properties, find

compounds similar to a lead compound, and

compare the diversity of sets of compounds.

MOLECULAR DIVERSITY

Library Design 9

COMPLEMENTARY TECHNOLOGIESFOR LIBRARY DESIGN

Ligand-Based DesignClogP/CMRMolconn-ZQSAR with CoMFAVolSurf

Receptor-Based DesignSurflex-Dock

CheminformaticsConcordStereoPlexUNITY

A 9,600-compound library (yellow) filling diversitymissing from three 12,800-compound libraries (gray, green, and red) using DiverseSolutions.

DiverseSolutions®

Design, Compare, and Select CompoundLibraries

DiverseSolutions2 assesses the chemical

diversity of a population of molecules,

selects diverse or representative subsets,

and compares the diversity of two or more

different populations of molecules.

DiverseSolutions generates metrics relevant

to inter-molecular interactions, and then

identifies those metrics that best distinguish

structural differences between compounds.

A novel, reactant-biased, product-based,

library design algorithm generates full

synthetic arrays that preserve diversity and

satisfy user-specified constraints. These

constraints may include the number of

reactants, their cost and availability, and

the plate format of a robotic synthesizer.

10 Cheminformatics

A UNITY query constructed at the active site of thestreptavidin/biotin complex (1STP).

Cheminformatics

DATA MINING

UNITY®

Locate Compounds in Databases that Matcha Pharmacophore or Fit a Receptor Site

UNITY is a search and analysis system for

exploring chemical and biological databases.

UNITY’s 2D searching capabilities offer

exact, substructure, and similarity searching.

Conformationally flexible 3D searching rapidly

finds molecules that can satisfy queries

regardless of the conformation stored in a

database. Structural queries may be based

on molecules, molecular fragments, pharma-

cophore models, or receptor sites. The tight

integration between UNITY and SYBYL

facilitates analysis by other methods, such

as QSAR with CoMFA.

Cheminformatics

SYBYLBase

Cheminformatics tools help

researchers extract usable information

from the volumes of data generated

by high-throughput and combinatorial

technologies that characterize modern

research methods.These tools address

the need to facilitate, store, and

explore the chemical and biological

data that is key to the success of

drug discovery programs. Tripos’ core

science and integrated applications

address critical cheminformatic tasks

such as data mining and analysis,

as well as structure representation

and optimization.

ENHANCED

Cheminformatics 11

COMPLEMENTARY TECHNOLOGIESFOR CHEMINFORMATICS

Ligand-Based DesignDistillGALAHADHQSARQSAR with CoMFA

Receptor-Based DesignSurflex-DockCScoreEA-InventorRACHEL

Structural Biology & BioinformaticsBiopolymer

Library DesignLegion/CombiLibMaker

STRUCTURE REPRESENTATION

Concord®

Generate Accurate 3D Coordinates

Concord2 sets the industry standard for

extremely rapid conversion of 2D (or crude

3D) input to accurate, geometry-optimized

3D structures. Although most often used for

the conversion of large corporate and com-

mercially distributed databases, Concord is

also a productivity tool useful for molecular

modeling and QSAR/QSPR research.

Confort™Generate Sets of Diverse, Low-EnergyConformers

Confort2 is a powerful conformational analysis

tool that performs exhaustive, yet rapid,

analysis of drug-sized molecules. It can be

used to identify the global minimum energy

conformer, all local minima within a user-

specified energy range, or a maximally

diverse subset of conformers. Confort can

be applied to entire molecular structures

or to user-specified substructures and is

equally adept at searching rings or acyclic

rotatable bonds.

StereoPlex®

Expand the Stereochemical Diversity of a Database

StereoPlex2 generates multiple stereoisomers

of each input compound structure according

to a user-specified limit on the number

of stereoisomers and a user-specified

priority rule which tells the program which

stereoisomers to generate if the complete

set would exceed the user’s limit. StereoPlex

multiplexes both atom-centered and bond-

centered chirality, while employing a novel

and unique algorithm to exclude topologically

impossible stereoisomers.

ProtoPlex™Rapidly Generate Protomers and Tautomersof Chemical Compounds

ProtoPlex2 provides a mechanism for making

accurate and realistic structural representa-

tions of chemical compounds. ProtoPlex

allows users to control the protonation,

deprotonation, or tautomerization for each

chemical class of proto-centers, as well as

the overall rules for formal charge and

number of proto-centers to multiplex.

MM4™Apply Molecular Mechanics to CalculateAccurate Molecular Structures and Energies

MM414 is the latest in a well-known series of

molecular mechanics programs for generating

high-quality geometries and energies for

calculating small molecule structures. MM4

offers increased accuracy in calculating

molecular structures, performing conforma-

tional analyses, and computing spectroscopic

and thermodynamic properties.

A sampling (left) from a larger Confort-generated set of diverse conformers of taxol and a 2D view of the molecule (right). This analysis identified conformers within 10 kcal of taxol's global minimum and included all 23 acyclic rotatable bonds.

Supplemental Technologies

12 Supplemental Technologies

Supplemental technologies span the

SYBYL expert molecular modeling

environment and impact multiple

workflows.These core scientific SYBYL

applications address critical tasks

and provide molecular descriptors

and interactions, tools for molecular

modeling and visualizing, and high-

volume data-set exploration.

AMPAC™Rapidly Calculate Transition States andSpectral Properties Using SemiempiricalQuantum Mechanics

AMPAC15 is an application for the determina-

tion of 3D structures and electronic properties

of small molecules using semiempirical

modeling methods. It is recognized by

researchers worldwide as a fast and robust

semiempirical modeling package, with a

wide variety of computational methods and

an extensive set of tools to aid in the study

of both molecular structure and chemical

reactions.

GSSI™Model Solution-Phase Properties Throughthe Consideration of Solute-SolventInteractions

GSSI2 is a novel, general approach to

modeling solution-phase properties through

a fairly rigorous, yet efficient, consideration

of solute-solvent interactions. It is useful

for predicting various partition coefficients

and membrane permeability coefficients in

support of ADME-related efforts. GSSI can

also be used to estimate the free energy

of desolvation which accompanies ligand-

receptor interaction.

hint!®

Quantitate and Visualize Non-CovalentInteraction Energies

hint!5 provides tools to visualize and calculate

the relative strengths of non-covalent

interactions between and within biological

molecules. These “hydropathic” interactions,

which are the primary determinants of

molecular recognition, include hydrogen-

bonding, acid-base, Coulombic, and

hydrophobic interactions.

HiVol™Analyze and Filter High-Volume Data Sets

HiVol works within the SYBYL environment

to facilitate the exploration of a high volume

of data such as that generated by high-

throughput synthesis or screening. Large

quantities of chemical and relational data

can be viewed, sorted, and filtered. A num-

ber of chemical descriptors can be computed

and added directly into the database.

HSCF™Semiempirical Molecular Orbital Information Server

HSCF2 is a unique semiempirical molecular

orbital (MO) “information server.” It can be

used as a stand-alone MO program and is

distributed with an easy-to-use GUI. HSCF is

ideally suited for use within QSPR applica-

tions in support of ADMET-related objectives,

as well as many other purposes related to

compound discovery.

ZAP™Rapid, Robust, and Accurate Simulation ofElectrostatic Interactions

ZAP16 uses the Poisson-Boltzmann equation

to calculate the electrostatic potential

surrounding a molecule in a medium of

varying dielectric. This is the situation when

drugs, proteins, or other organic molecules

are dissolved in water. ZAP’s calculations

provide insight into the solvation properties

of molecules and interaction energies

between molecules.

Molecular orbitals of vitamin B2 calculated by AMPACand viewed in SYBYL.

SupplementalTechnologies

SYBYLBase

Continual innovation creates well-designed, frequently updated software

that saves valuable time, enhances productivity, and can mean the

difference between success and failure for in silico discovery programs.

Tripos’ SYBYL molecular modeling environment leads the way in innova-

tion with an ever-growing suite of technologies and a firm commitment

to regularly scheduled software updates for existing applications.

Innovation and enhancement to SYBYL is fueled by customer feedback,

Tripos’ own discovery and development expertise, and scientific

collaboration with thought-leaders in both academia and industry.

TRIPOS SOFTWARE AND SCIENTIFIC PARTNERS:

1. Software Partner: Darmstadt University ofTechnology, GermanyScientific Partner: Professor Dr. Jürgen Brickmann

2. Scientific Partner: Professor Robert S. Pearlman

3. Software Partner: Philipps University of Marburg, GermanyScientific Partner: Professor Dr. Gerhard Klebe

4. Software Partner: Molecular Discovery Ltd., UKScientific Partners: Professor Gabriele Cruciani, Professor Manuel Pastor

5. Software Partner: eduSoft, LC, Richmond, VAScientific Partner: Dr. Glen Kellogg

6. Software Partner: BioByte Corporation, Claremont, CAScientific Partners: Dr. Albert Leo, Dr. CorwinHansch, Mr. David Hoekman

7. Software Partner: University of Sheffield, UKScientific Partners: Professor Peter Willett, Dr. Nicola Richmond

8. Software Partner: University of Sheffield, UKScientific Partners: Professor Peter Willett, Dr. Gareth Jones, Professor Robert Glen

9. Software Partner: Abbott Laboratories, Inc., Abbott Park, ILScientific Partner: Dr. Yvonne C. Martin

10. Software Partner: BioPharmics, San Mateo, CAScientific Partner: Professor Ajay N. Jain, Ph.D.

11. Software Partner: Drug Design Methodologies,LLC, St. Louis, MOScientific Partner: Chris Ho, M.D., Ph.D.

12. Software Partner: University of Cambridge, UKScientific Partners: Professor Sir Thomas Blundell,Dr. Kenji Mizuguchi, Dr. Jiye Shi

13. Software Partners: University of Cambridge, UK Scientific Partners: Professor Sir Thomas Blundell

14. Scientific Partner: Professor Norman L. Allinger

15. Software Partner: Semichem, Inc., ShawneeMission, KSScientific Partner: Professor Andrew Holder

16. Software Partners: eduSoft, LC, Richmond, VA;OpenEye Scientific Software, Inc., Santa Fe, NMScientific Partners: Dr. Glen Kellogg, Dr. Anthony Nicholls

QUICK REFERENCE INDEX Page Page

Advanced CoMFA . . . . . . . . . . . . . . . . . . . . . . . . 2, 5

Advanced Computation . . . . . . . . . . . . . . . . . . . . . . 1

Almond4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

AMPAC15 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Biopolymer . . . . . . . . . . . . . . . . . . . . . . . . . . 5, 6, 11

ClogP/CMR6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2, 9

CombiLibMaker 2 . . . . . . . . . . . . . . . . . . . . 3, 5, 8, 11

Concord 2 . . . . . . . . . . . . . . . . . . . . . . . . 3, 5, 7, 9, 11

Confort 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . 3, 5, 7, 11

CScore . . . . . . . . . . . . . . . . . . . . . . . . . . . 3, 4, 7, 11

DISCOtech9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Distill . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2, 11

DiverseSolutions 2 . . . . . . . . . . . . . . . . . . . . . . . . 3, 9

Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

EA-Inventor 2 . . . . . . . . . . . . . . . . . . . . . . . 3, 5, 7, 11

FUGUE 12 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

GALAHAD 7 . . . . . . . . . . . . . . . . . . . . . . . . . . 3, 5, 11

GASP 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

GSSI 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

hint! 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

HiVol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

HOMSTRAD13 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

HQSAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2, 11

HSCF 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12

LeapFrog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Legion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3, 5, 8, 11

MM414 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3, 5, 11

MOLCAD1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1, 4

Molconn-Z 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2, 9

OptDesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3, 8

ORCHESTRAR13 . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

ProtoPlex 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

QSAR with CoMFA . . . . . . . . . . . . . . . 2, 5, 9, 10, 11

RACHEL11 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5, 11

Savol 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Selector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3, 9

StereoPlex 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9, 11

Surflex-Dock10 . . . . . . . . . . . . . . . . . . . . 3, 4, 7, 9, 11

SYBYL . . . . . . . . . . . . . . . . . . . . . . . . . . 1, 6, 10, 12

Tuplets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

UNITY . . . . . . . . . . . . . . . . . . . . . . . . . . 3, 5, 7, 9, 10

VolSurf 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2, 5, 9

ZAP16 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

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