WHITE PAPER

Accelerating DMTA
cycles with fast, pushbutton free energy
calculations available
to entire project teams
Single-edge FEP+ integrated in LiveDesignExecutive Summary
FEP+ is a widely adopted technology for accelerating small molecule drug
discovery programs, with application in hit discovery, hit-to-lead, and lead
optimization. The accuracy and utility of FEP+ as a computational assay forthe prediction of binding energies is validated extensively, with predictions
falling within 1.0 kcal/mol of experimental values on average.1,2 With accuracy
approaching lab experiment, FEP+ serves as a digital binding affinity assay
for both on- and off-targets which enables drug discovery teams to efficiently
optimize molecular profiles and pursue novel chemistry with confidence.
Given the computational cost and complexity of running free energy
calculations, adoption is largely driven by expert computational chemists whothen manually share results with the project team. To amplify the impact ofthese methods and improve the agency of individual team members, theyshould be accessible directly to medicinal chemists and be able to be run at ascale comparable with traditional virtual screening methods.
Toward these goals, developments in expanding the accessibility of freeenergy calculations — in particular single-edge FEP+ (SE-FEP+) — enable a newparadigm in structure-based molecular design. SE-FEP+ simulations can be
run in a fraction of the time of full cycle-closure FEP+ (CC-FEP+) simulations
and can be conceptualized as ‘push-button’ for execution. This makes it easily
deployable in LiveDesign, a cloud-native, collaborative enterprise design
platform. This workflow enables all project team members to integrate FEP+
within their design workflows, resulting in a highly interactive and fully in silico
design-make-test-analyze (DMTA) cycle where chemists are empowered todigitally test hypotheses and iteratively improve designs prior to compound
synthesis (Figure 1).
In one such oncology program within Schrödinger’s Therapeutics Group, theproject team was able to improve compound potency over 100-fold by runningmany iterative in silico DMTA cycles using FEP+ in LiveDesign over the course ofa four week period.

In silico ‘DMTA’ cycle

VALIDATE
SE-FEP+ MODELwith known
experimental binders

Rapidly iterate over hundreds
to thousands of designs
PREDICT
Point-and-click torun SE-FEP+

Select promising
compounds toprogress to richer
calculations

SYNTHESIZEin the lab
ANALYZE
View SE-FEP+ results
and other properties

DEPLOY
SE-FEP+ model in
LiveDesign
DESIGN
Ideation by chemists

Run CC-FEP+ &prioritize compounds
for synthesis queue
based on predictions

Integrate experimental
and computational
assay for further design

TEST
in the lab

Analyze for further
design work orcompound progression

Progress
CompoundFigure 1: Process for deploying and running in silico DMTA Cycles via SE-FEP+ and its potential fortight integration with traditional experimental DMTA cycles.
2Single-Edge FEP+:
Accurate prediction of relative binding
affinities in a fraction of the time
As compared to docking or MM-GBSA calculations, full cycle-closure FEP+ (CCFEP+) simulations enable highly accurate rank-ordering of compounds, but
are compute intensive and complex to set up, reducing their ability to be set
up and executed by non-expert molecular modelers.
Single-edge FEP+ (SE-FEP+) simulations have been shown to maintainhigh predictive accuracy but can often be run ~10x faster than CC-FEP+
simulations in an automated fashion to triage new designs efficiently. Once
the SE-FEP+ model is validated, it can be used in a push-button fashion to test
new designs that share a common scaffold (i.e., common core or commonR-group) with the reference compound and enable efficient scoring of large
numbers of idea compounds (Figure 2).
When deployed via a collaborative platform such as LiveDesign, SE-FEP+
gives project teams the ability to run rapid design cycles and prioritize ideas
with confidence. The most promising design ideas can then be run throughfull CC-FEP+ calculations to obtain the most robust potency predictions for
informing synthesis queue decisions. If a compound falls outside the criteria
established by the computational chemist for running the SE-FEP+ models,that molecule can be added to a queue for running full CC-FEP+ in a moremanual setup by a computational chemist.

Figure 2: Schematic representation of singleedge FEP+ and cycle-closure FEP+. During
SE-FEP+, idea compounds are evaluated against
a single reference compound and usually for afraction of the simulation length compared to full
CC-FEP+ calculations, resulting in a faster albeit
less accurate calculation. During CC-FEP+, designideas are usually evaluated against multiplereference compounds in an interconnected map
with longer simulation lengths for the highest
accuracy results.

3Augmented 2D and 3D Design

A complete digital
molecular design lab
LiveDesign is a flexible, cloud-native collaborative
working environment for entire discovery teams.
The platform democratizes digital design processes
and enables more productive design cycles byharnessing the power of medicinal chemistrystrategies, advanced cheminformatics, cutting-edge
computational chemistry workflows, virtual ‘design
and test’ technologies, and centralized access to live
project data — all in a single interface.

SE-FEP+ Results View

The platform empowers creativity by democratizing
access to powerful predictive modeling workflows,
such as push-button free energy calculations, andallows teams to efficiently capture and progress
their most promising design ideas. LiveDesign
also centralizes team collaboration and decisionmaking — enabling crowdsourcing of design ideas,interactive team-based design, and easy sharing ofdata with internal and external partners.

Experimental Assays Alongside Digital Data

Interactive Project Dashboards

4In an early stage oncology drug discovery
program driven by Schrödinger’s Therapeutics
Group, the project team faced a design
challenge to find and optimize an initial hit
series. Of the 200 compounds synthesized in
the program to date, the most potent molecule
had a modest binding affinity of 2.7 µM, which
failed to meet the criteria for the hit-to-leadtransition. Traditional design strategies had
failed to yield significant potency improvements
after several months of DMTA cycles, even in
the most promising series.
With a new cocrystal structure for thispromising series in hand, the team turned toFEP+ to address the potency challenge. AnSE-FEP+ model was developed and validated
retrospectively using known experimental
binding affinities. The SE-FEP+ model wasthen deployed in LiveDesign, allowing
medicinal chemists to design new ideas andindependently run SE-FEP+ calculations with theclick of a button.
Using this validated model, the project team
iteratively designed molecules and digitallyassessed their ability to improve potencyfrom the current best Series A potency of
4 µM. During one particular four week period,
approximately 400 designs were profiledwith medicinal chemistry team members
contributing and running SE-FEP+ in LiveDesignfor ~65% of the designs. No new compounds
were experimentally synthesized and tested
in this month, but the team was able tosignificantly optimize compound potency insilico as predicted by FEP+. Each week, thepredicted potency of the designs made by thechemistry team improved until the top scoringcompounds were then prioritized for synthesis
and experimental profiling (Figure 3).
By prioritizing 24 compounds for synthesis
based on SE-FEP+ predictions in LiveDesign
over this one month period, the project teamdramatically enriched the synthesis queue withpotent designs. In fact, 21 compounds exhibited
improved measured binding affinities of lessthan 4 µM (Figure 4). Based on this significant
progress, the team moved the program forwardfrom the hit identification stage to the hit-tolead stage in a highly accelerated time frame.

AInitialseries A hit
Compound:
4 µM

SE-FEP+ pKiB

Initialseries A hit
Compound:
4 µM
WeekC87Experimental pKd

Case Study:
Oncology Program

Initialseries A hit
Compound:
4 µM

654321

Prior toSE-FEP+Hit finding(N=179)Series A
Series A(not profiled by (prioritized bySE-FEP+)
SE-FEP+)(N=57)(N=24)

Figure 3: Rapid potency improvements from SE-FEP+ in LiveDesign over a fourweek period. A) Initial retrospective validation of SE-FEP+ model performancevs experiment (known binders). B) Predicted binding affinity by week for newdesigns profiled directly in LiveDesign over a four week period demonstrates rapid
improvement as the team iterated on idea compounds in silico. C) Confirmed
experimental binding demonstrates over 100-fold improvement from compounds
prioritized by the SE-FEP+ in LiveDesign workflow compared to those designed
prior to or not profiled by SE-FEP+.
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Series A“Hit” compound
SPR Kd 8 µM

Series A
24 compounds
SPR Kd 4 µM - 250 µM

Series A
SE-FEP+ inLiveDesign
Deployed

Series A
Co-crystalStructure

Series A
Prioritized by SE-FEP+ inLiveDesign. 21 compoundsSPR Kd 30 nM - 4 µM

2022
AprMayHit
Identifcation
Initiation

JunJulAug“Traditional” Drug Design

Traditional design strategies before SE-FEP+ model failedto yield significant potency improvements after one
quarter of DMTA cycles.

Sep

Oct

FEP-Guided Design

Nov

Dec

Hit-to-LeadTransition

Modern approach with LiveDesign-launched SE-FEP+
enabled rapid potency progression of lead series allowing
the team to reach a key milestone.

Figure 4: Impact of SE-FEP+ in LiveDesign on program hit identification timeline.

Conclusion
Deployment of SE-FEP+ in LiveDesign presents a new paradigm for computationally-guided
design by democratizing access to powerful, push-button FEP+ calculations to teams ofmedicinal chemists. It enables chemists to think broadly and creatively when designing ideas
by lowering the barrier to in silico testing of ideas before synthesis. Additionally, the workflow
enables expert computational chemists to create guardrails around the use of FEP+ bychemistry teams, and focus time and effort on the more complicated full-cycle challenges to thehighly triaged compounds.
This workflow can assist in rapid potency progression, optimization of selectivity against known
off-targets, and maintenance of potency while optimizing other physicochemical properties. Thisallows project teams to quickly reach key milestones and design the best possible molecules
to meet the target product profile while dramatically reducing the number of compounds
synthesized and tested.

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How it works
Succcesful deployment of SE-FEP+ in LiveDesign follows these key steps:
1.

A scientist trained in FEP+ creates and validates an FEP+ model on a specific protein-ligand series ofinterest.

2.

A model execution strategy is determined around permitted structural modifications, licenses, andavailable compute hardware as these calculations are computationally significant.

3.

The FEP+ model is uploaded into LiveDesign and shared via a user-input constrained model.

4.

In LiveDesign, collaborators design structural modifications to the reference molecule and run
SE-FEP+ calculations through the push of a button.

5.

Results are provided directly in LiveDesign, including the corresponding predicted changes in freeenergy for each design idea, simulation statistics, and the 3D pose of the ligand in the protein.

6.

Teams can continue to ideate and explore modifications to identify promising compounds. The
most promising compounds are then progressed and further scored with CC-FEP+ for the highest
accuracy binding affinity prediction.

Best Practices
Key points to consider before deploying SE-FEP+ in LiveDesign:•Ensure your SE-FEP+ model is well-validated using known experimental binding affinities.

•Educate all project team members on accurate interpretation of model predictions and to build
appropriate confidence in the SE FEP+ model. The Schrödinger Online Course, “Free Energy
Calculations for Drug Design”, is a useful resource to learn more about FEP+, including a hands-on
case study.

•Develop template reports in LiveDesign that include automated safeguards around what structuralmodifications can be run successfully using the SE-FEP+ models.

•Ensure sufficient cluster compute resources are secured to run SE–FEP+ simulations efficiently basedon anticipated throughput.

•Schrödinger is happy to partner with users to share best practices and provide guidance on
optimizing FEP+ workflows for discovery programs. Reach out to your local Schrödinger teamfor assistance.

7Publications
1.

Impacting Drug Discovery Projects with Large-Scale Enumerations, Machine Learning Strategies, andFree-Energy Predictions. Knight Jennifer L., Leswing Karl, Bos Pieter H., Wang Lingle, ACS Symposium
Series, Vol. 1397, (2021) 1397: 205-226

2.

Advancing Drug Discovery Through Enhanced Free Energy Calculations. Abel et al. Acc. Chem. Res.
2017, 50, 7, 1625–1632.

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Learn more about LiveDesign atwww.schrodinger.com/products/livedesign/drug-discovery

Copyright © 2023 Schrödinger, Inc.

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