Schrödinger : Schrödinger Corporate Presentation

Pioneering Computational Molecular Design

February 2024

Cautionary Note and Disclaimer

This presentation contains certain "forward-looking statements" within the meaning of the U.S. Private Securities Litigation Reform Act of 1995 that involve substantial risks and uncertainties. All statements, other than statements of historical fact, contained in this presentation, including, without limitation, statements regarding the potential advantages of our computational platform, our research and development efforts for our proprietary drug discovery programs and our platform, the initiation, timing, progress, and results of our proprietary drug discovery programs and the drug discovery programs of our collaborators, the clinical potential and favorable properties of our molecules, including SGR-1505,SGR-2921 and SGR-3515, and other compounds discovered with our platform, the timing of potential IND applications as well as initiation of clinical trials for our proprietary drug discovery programs, the clinical potential and favorable properties of our collaborators' product candidates, including Nimbus Therapeutics and Morphic Holding, our ability to realize milestones, royalties, and other payments from our collaborative and proprietary programs, including our ability to realize returns on any of our investments in the companies we collaborate with, our plans to discover and develop product candidates and to maximize their commercial potential by advancing such product candidates ourselves or in collaboration with others, our plans to leverage the synergies between our businesses, our expectations regarding our ability to fund our operating expenses and capital expenditure requirements with our existing cash, cash equivalents, and marketable securities, and our expectations related to the key drivers of our performance, are forward-looking statements. The words "aim," "anticipate," "believe," "contemplate," "continue," "could," "estimate," "expect," "goal," "intend," "may," "might," "plan," "potential," "predict," "project," "should," "target," "will," "would" or the negative of these words or other similar expressions are intended to identify forward-looking statements, although not all forward-looking statements contain these identifying words.

These forward-looking statements reflect our current views about our plans, intentions, expectations, strategies and prospects, which are based on the information currently available to us and on assumptions we have made. Actual results may differ materially from those described in the forward-looking statements and are subject to a variety of assumptions, uncertainties, risks and important factors that are beyond our control, including the demand for our software solutions, the reliance upon our third-party drug discovery collaborators, the uncertainties inherent in drug development and commercialization, such as the conduct of research activities and the timing of and our ability to initiate and complete preclinical studies and clinical trials, uncertainties associated with the regulatory review of clinical trials and applications for marketing approvals, and other risks detailed under the caption "Risk Factors" and elsewhere in our Securities and Exchange Commission ("SEC") filings and reports, including our Annual Report on Form 10-K for the fiscal year ended December 31, 2023, filed with the SEC on February 28, 2024, as well as future filings and reports by us. Any forward-looking statements contained in this presentation speak only as of the date hereof. Except as required by law, we undertake no duty or obligation to update any forward-looking statements contained in this presentation as a result of new information, future events, changes in expectations or otherwise.

This presentation includes statistical and other industry and market data that we obtained from industry publications and research, surveys, and studies conducted by third parties as well as our own estimates of potential market opportunities. All of the market data used in this presentation involves a number of assumptions and limitations, and you are cautioned not to give undue weight to such data. We have not independently verified such third-party data, and we undertake no obligation to update such data after the date of this presentation.

2

Multi-Pronged Business Enabled by Highly Differentiated Computational Platform

SOFTWARE LICENSING
• Life Sciences	COLLABORATIONS
• Materials Design	• Drug Design	PROPRIETARY PIPELINE
~1,785 customers worldwide§
• Materials Design
	17 drug discovery collaborators*	• Drug Discovery & Development
		7+ active programs

LEADING COMPUTATIONAL PLATFORM

• Active Customers (# of customers who had an ACV >$1000) as of Dec. 31, 2023

* Cumulative since 2018

3

Software Business Highlights

$159.1M	17.4%
2023 software revenue	2023 software revenue
	growth vs. 2022
54	98%
Number of customers with	2023 software customer
ACV ≥$500,000	retention rate with ACV ≥
	$500K
27	~20X
Customers with ACV ≥	Difference in ACV between #1 and #10
$1M* vs.18 in 2022	ranked (by revenue) pharma companies

$6.7M

$3.5M

$1.8M $1.4M

1-56-1011-1516-20

Average ACV of top 20 customers

*As of Dec. 31, 2023; see Appendix for additional information relating to ACV and customer retention rate

4

Designing Drugs Is a Challenging Multi-Parameter Optimization Problem

Need to identify a molecule that balances many anti-correlated properties:

Potency

Selectivity

Solubility

Bioavailability

Clearance / Half-life

Permeability

Drug-drug interactions

Synthesizability

✓	✕	✓	✕	✓	✓
✕	✓ ✓	✓	✕	✓
✕	✕	✕	✓	✓	✕
✕	✕	✕	✕	✕	✕
✕	✕	✕	✕	✕	✕
✕	✕	✕	✕	✕	✕
✕	✕	✕	✕	✕	✕
✕	✕	✕	✕	✕	✕

…

33%

success

IND delivery

66%

failure

5

Vision for the Future of Drug Discovery

If all properties can be calculated with perfect accuracy, designing drugs would have a much higher success rate, be much faster and cheaper, and would produce much higher-quality molecules.

Select THE best molecule

"All"

synthesizable

molecules

Potency	✓	Clearance / Half-life	✓
Selectivity	✓	Permeability	✓
Solubility	✓	Drug-Drug Interactions ✓
Bioavailability	✓	Synthesizability	✓

6

Physics & Machine Learning Are Complementary

Physics-based

Methods

• No training set required
• Can extrapolate into novel chemical space
• Accurate

• Slow

Physics + Machine Learning
Training set for ML	Machine Learning /
generated using Physics	Artificial Intelligence
✓ Fast	✓ Effective at interpolation
✓ Accurate	✓ Fast
✓ Can handle very large datasets	✓ Can handle very large datasets
✓ Can extrapolate into	✘ Requires massive training sets
novel chemical space	✘ Cannot extrapolate

7

Physics & Machine Learning Are Complementary

Physics used to produce sufficiently large representative training set for Machine Learning

Design

1 billion

molecules w/

Generative

AI & De Novo

Design

~8

molecules

advance program

Select

1,000

random

molecules

Synthesize

10

best

molecules

Compute

properties of

1,000

molecules w/

Physics

1 day1

Compute

properties of

5,000

molecules w/

Physics

Build

Machine

Learning

model

	Score
	1 billion
	molecules w/
	ML model
Select	~1 minute
5,000
best
molecules

1-2days2

• Would take ~1 year to do experimentally

2 Would take ~5 years to do experimentally

8

A History of Scientific Innovation & Platform Advancement

	Protein
	Refinement
Quantum	Enabled accurate
Mechanics	prediction of local protein
structure
Enabled accurate
calculation of small

Molecular Dynamics

Enabled platform to simulate molecular

	Next Gen
	Protein
Free Energy	Refinement
Calculations	Enablement of
Broadly applicable and	proteins without
experimental
accurate calculation of
structures
potency, selectivity, and
solubility

molecule solution
structures	Molecular
	Mechanics
Schrödinger	Enabled fast calculation
Founded	of small molecule solution
structures

motion

Docking

A breakthrough in virtual screening for Hit Identification

Comprehensive Force Field

Enabled accurate description of atomic interactions

Active Learning

Enabled accurate large-scale property calculations

1990

1995

2000

2005

2010

2015

2020

~500 publications in peer-reviewed journals

9

Platform Validated by Advancing Collaboration Programs(1)(2)

8 programs in the clinic (+ 5 in IND-enabling studies)

Phase 1

Phase 2

Phase 3

Undisclosed

Immuno-oncology

Pulmonary Arterial Hypertension

Oncology**

Oncology

Undisclosed

Metabolic Diseases***

Psoriasis****

Inflammatory Bowel Disease

FDA-Approved

TIBSOVO*

IDHIFA*

Additional programs in discovery and preclinical development with:

(1)	Based on publicly available information or information disclosed to us
(2)	All of the programs being pursued under these collaborations are owned and controlled by each respective collaborator	10
*Acquired by Servier **Acquired from Petra Pharma ***Acquired from Nimbus ****Acquired from Nimbus