A Machine Learning / Artificial Intelligence Drug Design Company

NON CONFIDENTIAL OVERVIEW

Spektron Systems is disruptively…

Converting drug discovery into directed drug design and drug engineering.

Curtailing by 75% the conventionally required amounts of time and money to develop medicinal molecules for delivery to the market.

Dramatically reducing the risks of clinical trial and market failure.

The Spektron Advantage: Q-MAP™: AI/ML methods applied to early stage drug design.

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Drug Discovery is No Longer Viable in Big

Pharma• Average cost of introducing a new drug has

reached $2.6 billion*

• Pre-clinical trial cost alone is $100M+

• Glacially slow R&D: 8-11+ years per drug

• The low-hanging fruit has been plucked

• Investment returns are low and dropping

• Financiers, not scientists, now drive most R&D decisions; too often they back the wrong picks

• Huge, critical patient needs going unmet

*Total capitalized costs per new drug approval (in 2013 dollars) according to ”Journal of Health Economics,” May 2016

The Problem

VALUE OF SPEKTRON’S APPROACH

in vitro & Animal Testing Human

Q-MAP™ models end-to-end in silico prior to the synthesis of any molecules

Spektron’s Q-MAP™ platform reduces the time, effort, and risks involved in developing a viable pre-clinical drug candidate

CUSTOMARY INDUSTRY PRACTICE

Biology Target ID Target Validation

Lead Discovery

Lead Optimization

Preclinical Testing

Phase I Clinical

Phase II Clinical

Phase III Clinical

Q-MAP™ in silico

in vitrovalidation

Preclinical Testing

Chief Operating OfficerTim Dockins is a PhD candidate in Computer Science with an emphasis in Machine Learning. He has industry experience in program management and software development dealing with complex data.

Chief Strategy OfficerRobert Cain is an MBA with background in investment banking with JP Morgan and pharmaceuticals with Sandos. Mr. Cain has experience in strategy, finance and international business development for companies ranging from startups to Fortune 500.

President / Chief Medical OfficerLed by founder David Wolf, MD, recognized expert in taking innovation to operations; pioneer in ultrasound imaging and tissue engineering; professor of biomedical engineering and aeronautics; physician, astronaut; White House advisor.

The Management Team

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Senior Vice President of Drug Discovery W. Ken Fang, Ph.D. is a veteran in the pharmaceutical industry with over 17 years at Allergan leading early stage drug discovery efforts. With broad experience in developing and testing new chemical entities, Dr. Fang leads our optimization and physical validation strategy.

THE CORE TEAM

Bob D’Agostino, MSc.

DirectorMachine Learning

Max Sharifi, Ph.D.

Principal ComputationalChemist

Ashley Meyer, MSc.

Principal InformaticsScientist

Asawari Kharkar, Ph.D.Sr. Informatics Scientist

Ashley Flory, M.Sc.Sr. Informatics Scientist

Jessica HeidrichInformatics Scientist

Iva Stoyanova-Slavova, Ph.D.FDA – NCTR – ORISE Fellow assigned to CRADA

Shayne Cox-Gad, Ph.D. DABTKey Advisor – FDA-IND Process

EXTENDED TEAM

! ! ! !Executive Management

! ! ! !AI/ML & Platform Development

! ! ! !Informatics / Data Science

! ! !Drug Design / Chemistry

! ! ! ! !Executive Management

! ! ! ! ! !AI/ML & Platform Development

! ! ! ! ! !Informatics / Data Science

! ! !Administrative

! ! ! ! !Drug Program 1

! ! !Administrative

Current Staffing

! Spektron FTE! Shared Resource (Spektron FTE)! Contractor/Consultant

! ! ! !Drug Design / Chemistry

Post-Equity Buildup

6 FTE5 Contractors/Consultants

15 FTE14 Contractors/Consultants

The Business Model

Multiple Paths to Revenue & Profit• Sell clinical trial-ready investigational new drugs

(IND) to other pharmaceutical companies• Collect upfront payments, progress payments &

ongoing royalties

• Provide toxicity screening services

• Develop drugs initiated by JV partners in exchange for ownership share

• Fix “broken” or withdrawn drugs

Q-MAP technology/trade secrets remain in-house and proprietary to Spektron Systems

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Target Customers

New drug molecules $

Upfront &Ongoing

Payments

SPEKTRON MOLECULE DEAL STRUCTURE

$10MEarly stageBefore preclinical testing

1) Licensed at 2) Initial Payments 3) Milestone Payments

$20M $370M

PreclinicalInvestigational New Drug

$30M $370M

Phase 1aHuman Safety $100M $500M

Early Stage Preclinical Testing Phase I Clinical Phase II Clinical Phase III Clinical

COMPARABLE DEALS

Early Stage Preclinical Testing Phase I Clinical Phase II Clinical Phase III Clinical

$14.2MBicycle Therapeutics & Bioverativ $410M

$300MIFM Therapeutics & Bristol Meyers $1B

$150MNektar Therapeutics &Eli Lilly $1B

2017 – Hemophilia / Sickle Cell Anemia

2017 – Oncology (STING)

2017 – Autoimmune (Tregs)

Psychiatric

Schizophrenia

Depression

Anxiolytics

Drug PipelinesCardiovascular

Bipolar Disorder

Antiarrythmic

Hypercholestoreolemia

Hypertension

Analgesics

Platform Development used Schizophrenia as a target disease state with lower safety and tolerability issues.

Data in other Psychiatric areas was available and overlapping suggesting the possibility of repurposing or separate programs.

Other drug classes are being explored.

Hypoglycemics

Antiemetics

Neurology

Respiratory

OPERATING STRUCTURE

Subsidiaries hold compound and target data, IP, know how, IND, etc.These can be sold separately

Investors

Funding/services flow from Investors to Spektron

Distributions and capital gains flow to Investors

Spektron Systems, Inc.

Compound IP, know how, data, etc. flow to Sub (funded by Parent)

Sub 1 LLC Sub 2 LLC

Compound gains/losses flow to Parent

Sub 3 LLC

COMPETITION

Q-MAP™ models end-to-end in silico prior to the synthesis of any molecules

MOA asserted through

existing human clinical

endpoint data

Some companies focus on aselect portion of the traditionaldrug discovery process

Some companies focus on certain drug classes outside of Spektron’s arena

Biology Target IDTarget

Validation

Lead

Discovery

Lead

Optimization

Preclinical

Testing

Phase I

Clinical

Phase II

Clinical

Phase III

Clinical

Q-MAP™

in silico

in vitro

validation

Preclinical

Testing

IP STRATEGY

• Protect core platform technology with:• Trade secrets• Provisional patents• PCT filings

• Protect drug molecules with full patent protection on molecules and Markush structures

• Protect other important IP through trademarks and copyrights

Introducing Q-MAP™The Quantum-Molecular Activity Predictor

An extension of research from the FDA’s National Center for Toxicological Research through licensing and a Collaborative Research and Development Agreement

Combines modern methods of molecular fingerprinting to reveal key characteristics involved in biological activity.

A key component in Spektron’s platform for new drug discovery

PUBLIC HEALTH SERVICE

COOPERATIVE RESEARCH AND DEVELOPMENT AGREEMENT

PHS CRADA Agreement Ref. No. 2017-0027-CRD A1 MAY 15, 2018 Page 18 of 25 Confidential Revised August 1, 2012

SUMMARY PAGE

EITHER PARTY MAY, WITHOUT FURTHER CONSULTATION OR PERMISSION, RELEASE THIS SUMMARY PAGE TO THE PUBLIC.

TITLE OF CRADA: Advancing SDAR modeling for predictions of drug biological activity

FDA Component: National Center for Toxicological Research

FDA Principal Investigator: William Mattes, Ph.D.

Collaborator: Spektron Systems, LLC.

Collaborator Principal Investigator(s): Tim Dockins

TERM OF CRADA: Three (3) years from the Effective Date

ABSTRACT OF THE RESEARCH PLAN:

Spectral Data-Activity Relationships (SDARs) is an FDA-patented molecular modeling technology that can generate accurate chemical and biological predictive data. The technology has been demonstrated to discern important chemical features for a variety of molecules as related to biological effects and toxicity. The SDAR method utilizes NMR chemical shifts related to, but independent of, specific chemical structure. SDAR molecular fingerprints are used in conjunction with their associated biological endpoints and modeled using various pattern recognition methods to create predictive models. These mathematical associations between the fingerprint characteristics produce predictions for unknowns. Although SDAR predictive models have been demonstrated for several relevant toxicities, the limitations of the method relative to small (<70 molecules) and large (>500 molecules) datasets have not been determined. The collaborators will seek to 1) produce a robust, user-friendly SDAR modeling software; 2) identify and explore limitations of the method; and 3) automate/replace expert user functions. Collaboratively created in silico models will be available to Collaborator and FDA as shared work products.

COLLABORATION

Spektron Systems & FDA Cooperative Research and Development Agreement (CRADA)

Spektron Systems maintains a CollaborativeResearch And Development Agreement with theFDA – National Center for Toxicological Research

This collaboration provides bi-directional accessto data, methodology, and expertise enabling akey technology transfer into commercialization

Proprietary & Confidential

Molecular Design Generator

AI/ML Modeling

Expertise Expertise

Molecular Design Evaluator

AI/ML Modeling

Public and PrivateTraining Data

producesQ-MAP™ Qualified

Lead Molecules

Q-MAP™ - AI-assisted Drug Design

TECHNOLOGY VIABILITY DEMONSTRATIONS

Benchmarking

Toxicity

Models for all

Major Organ

Systems

Corroboration

with Published

Research

Engineering

Novel Molecules

PREDICTION DASHBOARDS000001 S000002 S000003 S000004 S000005 S000006 S000007

Tar

gets

Efficacy OK UNDF OK OK FAIL OK OK

Relative Ranking OK UNDF OK OK FAIL OK OK

Pharmacological Class OK OK OK OK FAIL OK OK

Animal Models OK FAIL OK UNDF FAIL UNDF OK

Ant

itar

gets

Blood Dyscrasias OK OK OK OK OK OK OK

Cardiotoxicity OK OK OK FAIL OK UNDF OK

Hepatotoxicity OK OK UNDF OK OK OK UNDF

Nephrotoxicity OK OK OK OK OK OK OK

Neurotoxicity OK UNDF OK UNDF OK UNDF UNDF

Phospholipidosis UNDF OK OK FAIL OK OK OK

Mutagenicity FAIL OK OK OK OK OK OK

Carcinogenicity OK OK OK OK OK OK OK

Pro

file

Phase 1 P450 Inhibition NON INHIBIT NON NON NON NON NON

Receptor Binding TARGET TARGET TARGET OFF-TGT OFF-TGT TARGET TARGET

Receptor Action TARGET TARGET TARGET TARGET OFF-TRGT TARGET TARGET

Q-MAP’s virtual screening toolbox provides a range of predictive models employed to predict biological and in-vitro activity.

Predictions are combined into target, off-target, and profile to create a composite score.

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Tox21 Data ChallengeThe 2014 Tox21 data challenge is designed to help scientists understand the potential of the chemicals and compounds being tested through the Toxicology in the 21st Century initiative to disrupt biological pathways in ways that may result in toxic effects.

The goal of the challenge is to "crowdsource" data analysis by independent researchers to reveal how well they can predict compounds' interference in biochemical pathways using only chemical structure data. The computational models produced from the Challenge could become decision-making tools for government agencies in determining which environmental chemicals and drugs are of the greatest potential concern to human health.

Spektron operates at the top of the field of challengers for a balanced metric and in the crucial metrics for screening for toxicity.

Specificity

hERG InhibitionhERG blocking has been one of the leading causes for withdrawal from the market of drugs approved by the FDA and is implicated as leading to cardiac arrhythmia.

Spektron has developed in silico models for predicting

drug-induced hERG Inhibition achieving high accuracy for identifying

active molecules.

Stoyanova-Slavova, Iva B., Svetoslav H. Slavov, Dan A. Buzatu, Richard D. Beger, and Jon G. Wilkes. 2017. “3D-SDAR Modeling of HERG Potassium Channel Affinity: A Case Study in Model Design and Toxicophore Identification.” Journal of Molecular Graphics and Modelling 72. Elsevier Inc.: 246–55

Q-MAP™ corroborates toxicophore identification using

the same methodology as developed at the FDA – National Center for Toxicological Research

Psychiatric Schizophrenia

Antipsychotics

Novel Molecules Q-MAP Qualified Leads Q-MAP Optimized Leads Validated Leads Pre-Clinical

> 15,000Novel Molecules

generated by Q-MAP™

~100Leads qualified by

Virtual Screening and Multi-Parameter

Optimization

~60Leads optimized for

patents and synthesis

-Small scale synthesis

and validation for efficacy and pre-IND

readiness

-In pre-clinical testing

targeting FDA Investigational New

Drug Process

Q-MAP™ has generated over 15,000 novel molecules targeting improved atypical antipsychotics in a first iteration.

Next Step: revalidate Q-MAP™ leads through Version 2 of the platform and prepare for small-scale synthesis.

FINANCING OVERVIEWSpektron is

presently seeking Series A equity

financing with follow-on Rounds totaling

$40M

Concurrently, Spektron is filling

$2.2M in Convertible Debt via Friends & Family and Angels for a total of $5M

CDNs

To date, all CDN financing has come

from investors outside of Arkansas

Proprietary & Confidential

$2.8MConvertible Debt

$2.2MConvertible Debt

$10MSeries A Equity

2016 Mid 2018 Late 2018

Terms:1.5x on Equity36mo MaturitySEC Form D Filing

Terms:1.5x on Equity36mo MaturitySEC Form D Filing

Terms:Institutional and Venture Capital

EQUITY RAISE HIGHLIGHTS

$10M+ $30M $370M

Current Funding Round Upfront Payment Milestone Payments

Spektron Systems is seeking 24mos financing to carry its first drug target through preclinical trials to submit to the FDA as an IND

Estimated proceeds from first program

Used to expand Spektron’s platform and complete the first program milestones

Proprietary & Confidential

The FinancialsInvestment Thesis

• Deploy $40 million to complete tech platform and develop first 3 drugs

• License each drug to Big Pharma companies to manage FDA clinical trials

• Collect payments as drugs move toward and beyond commercial launch: • Up-front payments: est. $30M per drug

• Milestone payments: est. $200M to $1B+ per drug

• Ongoing royalties: est. 5% - 20% of sales revenue

• Aim for USD multi-billion sale or IPO after 2nd

successful licensing agreement

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Pro Forma Financial Projections$37m invested in 3-drugs, 2nd & 3rd drugs succeed

2018 2019 2020 2021 2022 2023

$10M $14M $13.2M

Drug 1Sale

Drug 2Sale

Drug 3Sale

Anticipated Financing & Liquidity Events

Use of Funds

Liquidity

Proprietary & Confidential

FUNDING / VALUATION COMPARABLE

Total FundingLatest Round

ValuationSeed Round Average

Debt Financing

AverageSeries A Average Series B Average Series C Average

twoXAR $14.3M $66.67M

Spektron is on target with seed round and is expected to close a series A in the same range as

these comparable companies..

Recursion Pharmaceuticals $84.3M $400M

Numerate $17.4M Unknown

NuMedii $5.5M $23.33M

Atomwise $51.6M $300M

Verge Genomics $4M $26.67M

Average $29M $163M $2M $2.5M $10M $33M $8M

Spektron’s Valuation Milestones

Year 1 Year 2 Year 3 Year 4 Year 5

~ $100 M

Q-MAP fully operational

1st Q-MAP Optimized Lead

Selected

~ $150 M

1st lead molecule validated

2nd Q-MAP Optimized Lead

Selected

~ $300 M

1st pre-clinical candidate

2nd lead molecule validated

3rd Q-MAP Optimized Lead

Selected

~ $500 M

1st Investigational New Drug

2nd pre-clinical candidate

3rd lead molecule validated

~ $1.2 B

1st drug passes Phase 1 CT

2nd Investigational New Drug

3rd pre-clinical candidate

Investor Exit Strategies

Shareholder Buyout options

with proceeds from each Drug Program

(any 2 out of 3 = Grand Slam)

Mergers / Acquisitions / IPO

Positive Business Environment

Strong Market for medicinal molecules

Strong merger/acquisition and liquidity options

Big Pharma Exiting Discovery

2025202420232022

@SpektronSystems

www. linkedin.com/company/spektron-systems-llc.

www.spektronsystems.com

Contacts:

Spektron Systems400 W. Capitol Ave., 17th fl.Little Rock, AR 72201

Tim Dockinstdockins@spektronsystems.com+1.469.337.0788

David Wolfdwolf@spektronsystems.com+1.832.372.3834

Robert Cainrcain@spektronsystems.com+1.310.663.8811