A Machine-Learning Drug Design Platform Company BUSINESS OVERVIEW

A Machine-Learning Drug Design Platform Company BUSINESS OVERVIEW

Introduction Revolutionizing Drug Discovery and Design Through Artificial Intelligence The $1 trillion global pharmaceutical industry faces a growing crisis Traditional methods for developing new drugs are no longer financially viable New, faster, cheaper methods are essential for Big Pharma to sustain profitability Artificial intelligence (AI) and Machine Learning (ML) based approaches can replace brute force experimentation and needle in a haystack luck with precise and vastly more efficient drug engineering solutions Only one company Spektron Systems works closely with the FDA to establish reliable, repeatable methods for cost-effective new AI/ML drug discovery 1

Drug Discovery is No Longer Viable in Big Pharma The Problem Industry-wide Internal Rates of Return Are Rapidly Heading Toward 0% Average cost of introducing a new drug has reached $2.6 billion* Pre-clinical trial cost alone is $1+ billion 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 Solution Spektron has leveraged decades of FDA tech investments* and knowledge to create a new drug discovery platform: Q-MAP Q-MAP applies proprietary AI/ML tools to replace costly trial-and-error R&D with precise drug molecule engineering With Q-MAP, Spektron can: Reduce new drug discovery cycle from 8-11+ years to 3.5 years or less Slash new drug discovery investment from over $1 billion to approximately $10 million Q-MAP is a game changer for new drug discovery and design *Approx. $20M in FDA funding has built technologies licensed by Spektron Wet Lab Experimentation AI Molecule Engineering 3

The Company Operating in Little Rock, Arkansas, Spektron s foundational technology emerged from the nearby National Center for Toxicological Research (NCTR), a world-renowned FDA facility Decades of NCTR research and tens of millions invested into tools/methods to support public health led to unique computational chemistry expertise now licensed by Spektron Founded in late 2016, Spektron raised seed funding and established a team of scientists to further develop the FDA technologies into a commercial platform for designing new drugs Spektron is now seeking Series A financing to complete its Q-MAP platform and engineer its first three marketable drug molecules for sale to big pharma companies Spektron s mission is to create exponential value for investors 4

Q-MAP vs. Conventional Discovery The Technology Spektron s unparalleled competitive advantage is its Q-MAP technology Begin with smaller, curated set of ~10k molecules Begin with large set of ~100k molecules A unique, disruptive drug discovery method In silico front-end design of molecules Computer prediction of efficacy, toxicology, receptor binding & ADMET Uses molecule Spectral and Quantum Descriptors to outperform methods that use Structure alone Doesn t require costly lab space Massively scalable, including across multiple drug classes In silico screening & design 1 IND to clinical trials Wet-lab, HTS and other in vitro assays ~ 3 yrs ~ 10 yrs > 50% chance of success 1 IND to clinical trials < 10% chance of success

Global Spending by Geography The Market Pharmaceutical business is among world s largest with over $1 trillion in annual sales U.S. leads with 40% of global drug spending and huge share of R&D investment Growth has been brisk at 6%+ CAGR, but is slowing rapidly as major patent expirations far outpace new drug introductions Shrinking Big Pharma investment threatens discovery of new drugs for a wide range of pressing needs Global Spending by Disease Area 5 5 5 5 / Source: QuintilesIMS Institute (2015)

Design in silico Efficacy, Toxicology, Receptor Binding, ADMET The Competition Competition is robust, with about 10 key players But industry rivalry is modest Spektron occupies uncontested space in AI/ML drug design Shortage of new drug rollouts leaves ample room for multiple competitors Structural Design Spectral and Structural Activity Design Design in silico Receptor Binding 7

The Business Model Multiple Paths to Revenue & Profit Sell clinical trial-ready drug molecules (INDs) to Big Pharma 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 New drug molecules Target Customers $ Upfront & Ongoing Payments 8

The Management Team Multi-disciplinary technical, scientific, and business team Led by founder David Wolf, MD, recognized expert in taking innovation to commercial operation; pioneer in ultrasound imaging and tissue engineering; professor of biomedical engineering and aeronautics; physician, astronaut COO Tim Dockins is experienced in programming and software product development with a background in machine learning and intelligence systems CSO Robert Cain is an MBA with background in investment banking, IPOs and pharmaceuticals, and experience in strategy, finance and business development for companies ranging from startups to Fortune 500 Technology team includes experts in computational chemistry, bioinformatics, medicinal chemistry, and scientific chemistry, with backgrounds in pharma and at the FDA 9

Anticipated Financing & Liquidity Events 2018 2019 2020 2021 2022 2023 The Financials Investment Thesis $10M $14M $13.2M Deploy $40 million to complete tech platform and develop first 3 drugs Financing Drug 1 Sale Drug 2 Sale Drug 3 Sale License each drug to Big Pharma companies to manage FDA clinical trials Liquidity Collect payments as drugs move toward and beyond commercial launch: Pro Forma Financial Projections $37m invested in 3-drugs, 2 nd & 3 rd drugs succeed 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 2 nd successful licensing agreement 10

Contacts: Spektron Systems 400 W. Capitol Ave., 17 th fl. Little Rock, AR 72201 David Wolf dwolf@spektronsystems.com +1.832.372.3834 Robert Cain rcain@spektronsystems.com +1.310.663.8811