www.criver.com

EVERY STEP OF THE WAY

Computer-aided Drug Design (CADD) ServicesCharles River’s Computer-aided Drug Design (CADD) team unites industry-seasoned scientists with an average of 14 years

of biotech/pharma experience with modern software and hardware resources. This combination enables us to support drug

discovery projects all the way from hit identification through lead optimization.

HTS SupportCharles River’s CADD group works closely with our high-throughput screening (HTS) team to provide an optimal service

to our clients. CADD input to HTS campaigns includes the selection of compound subsets for screening. This can be

carried out at either a compound or plate level using various computational methods in combination with our extensive

in-house compound collections. Once a screen has been run, the CADD team can assist with data triage using a range

of cheminformatics and visualization tools. We can also apply our proprietary hit expansion toolkit to identify additional

compounds for screening that can enhance SAR and lead to the discovery of new scaffolds.

Structure- and Ligand-based Virtual ScreeningOur virtual screening campaigns frequently lead to the rapid and cost-effective identification of excellent starting points

for hit-to-lead chemistry. Given one or more compounds with proven activity against the target of interest and/or an X-ray

structure of the target, we apply the appropriate computational techniques in conjunction with in-house and/or external

compound collections to identify a set of molecules for biochemical screening.

CADD Capabilities

• HTS support

• Structure- and ligand-based virtual screening

• Structure-based drug design

• Ligand-based drug design

• Molecular dynamics

• QSAR and QSPR modelling

• Compound library design

SummaryCharles River’s CADD team can

work in a standalone manner,

often for bespoke modeling or

virtual screening projects, or act

as an integrated project team

member, collaborating closely

with colleagues in medicinal

chemistry, structural biology and

allied disciplines.

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Questions for our chemists? Visit https://www.criver.com/

consult-pi-ds-questions-for-our-

chemists

DISCOVERY

askcharlesriver@crl.com • www.criver.com © 2020, Charles River Laboratories International, Inc.

Structure-based Drug Design Our CADD team is accomplished at exploiting structural information in the design of novel molecular entities and working

alongside in-house or client structural biology groups or utilizing public domain structures from the Protein Data Bank.

When an experimental structure of a target, or anti-target, is not available, it may be possible for us to build a computational

(so-called “homology”) model to help guide molecular design. Working closely with our medicinal chemistry colleagues, we

routinely use protein-ligand docking to help rank proposed synthetic targets and to stimulate new design hypotheses. Using

software to predict the positions and thermodynamic properties of active site water molecules can also provide valuable

insights to the design process.

Ligand-based Drug Design When protein targets are not amenable to structural determination by experimental or computational approaches, we can

assist projects by calling on a range of ligand-based CADD techniques. One such approach is pharmacophore modeling,

in which we seek to elucidate key molecular features that are responsible for the activity of a set of compounds and then

exploit that knowledge for molecular design. We can also use 3D shape similarity searching to assess design ideas by

molecular alignment or to search for new ideas in compound library collections. In addition, ligand-based scaffold hopping

is a powerful technique for suggesting novel replacements for parts of ligands which can lead to new compound series for

exploitation by our chemistry teams.

Molecular DynamicsWith the advent of graphics processing unit (GPU) capabilities, molecular dynamics (MD) simulations have rapidly become

an integral part of our CADD team’s toolbox. We can use this powerful methodology to examine the flexibility of protein

structures, which can be important since X-ray crystallography typically provides just a single “snapshot” of what is a highly

mobile system. This can lead to the identification of an ensemble of binding site models, sometimes used in structure-based

virtual screening. MD simulations can also be applied to assess the stability of binding modes proposed by protein-ligand

docking studies and for conformational analysis of ligands, including an accurate treatment of their solution environment.

QSAR and QSPR ModellingIn this era of “big data”, it is vital that we have the tools to exploit the various types of experimental information that are

generated on molecules during the discovery process. These data are typically classified as “activity” such as IC50 or Ki

values from a biochemical assay, or “property,” such as physicochemical or in vitro ADMET measurements, giving rise to

quantitative structure or property relationship modelling, respectively. The aim in both cases is to use the available data to

generate mathematical models relating the chemical structure of a molecule to the biological activity or molecular property

of interest. The resulting models can then be used predictively to help prioritize compounds for synthesis and testing, saving

time and resources. Charles River is actively investigating techniques based on artificial intelligence (AI) to see if they can

be beneficial in this area.

Compound Library DesignOur CADD team has a long history of contributing to the design of compound libraries (e.g., SoftFocus®) targeting gene

families. More recently, the group has assisted in the selection of various sets of fragments that we offer to clients for

fragment-based screening campaigns. More generally, we can help clients build or augment their compound collections by

selecting compounds from the ever-increasing number of commercially available samples subject to various constraints

relating to molecular diversity and/or predicted molecular properties. Compound selections can be additionally focused

using protein-ligand docking, molecular similarity ranking, or Bayesian activity models.

Questions for our chemists? Visit https://www.criver.com/

consult-pi-ds-questions-for-our-

chemists