A Machine Learning / AI Drug Discovery Company

Shortening the Drug Discovery timeline through advanced machine learning techniques

ML/AI-based Drug Design

As an extension of our relationship with the National Center for Toxicological Research, Spektron Systems has developed a platform for AI-assisted drug design.  The goal of the platform is to shortens discovery timelines in developing preclinical candidates from 4.5+ years to approximately 1 year. We target a $10M cost of discovery opposed to costs that can reach $100M+. Our approach shows a strong potential to disruptively improve pharmaceutical pipeline success.

A Platform for Virtual Screening

Spektron Systems has developed a platform, Q-MAP™, for designing new molecules which we will carry through pre-clinical testing.  Q-MAP™ combines modern methods of machine learning and computational chemistry to build models using available training data on multiple endpoints.  We have developed predictive models for virtual screening of molecules on a variety of endpoints toxicity, efficacy, and side-effects.  This ability to screen molecules in-silico is available for Spektron’s drug discovery pipeline as well as for our partners. 

Development Drug Pipeline

In the course of developing our platform, we necessarily selected a target therapeutic area.  Our initial datasets were built in the area of psychiatric drugs targeting relief for symptoms of schizophrenia, depression, and bipolar disorder.  In the first iteration of the platform, we developed over 15,000 novel molecules and processed them through our in-silico pipeline arriving at ~60 Q-MAP™ Optimized Leads for an atypical antipsychotic with reduced risk and tolerability issues.  As we continue development on the platform, a second iteration will build confidence in the designed molecules we intend to carry forward into preclinical testing.

The Leadership Team

David Wolf, M.D.

President / Chief Medical Officer

David Wolf, M.D.

He is a licensed physician, astronaut, electrical engineer and life sciences, inventor/researcher. His experience includes leadership positions as Chief Engineer and Program Manager for NASA programs, board level program management responsibility and influence and oversight for multiple, national and international, endeavors within NASA. Dr. Wolf was the recipient of the NASA Inventor of the Year in 1992. He has a B.S. in Electrical Engineering from Purdue University and an M.D. from Indiana University School of Medicine.

Robert Cain

EVP / Chief Strategy Officer

Robert Cain

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. He holds a B.A. from Harvard and an MBA from The Wharton School of the University of Pennsylvania.

Tim Dockins

Chief Operating Officer

Tim Dockins

Tim Dockins is a polyglot programmer with an emphasis on complex data manipulation and visualization. He has an extensive background in several specialty areas including predictive modeling, machine learning and pattern classification, UI design and implementation, data science, and program management. Tim received a B.S. in Computer Science from the University of Texas at Arlington. He is a Ph.D. Candidate in the Computer Science and Engineering department at the same university where he studies intelligent systems and machine learning.

Bob D'Agostino, M.Sc.

Director, Machine Learning

Bob D'Agostino

Bob D'Agostino leads the ML/AI platform development. He has over 15 years of experience leading/implementing scientific computing projects for a variety of industries and applications, including drug discovery, defense, chemical detection, semiconductors, and the automotive industry. He has a B.S. in Physics from Syracuse University and an M.S. in Applied Mathematics from the University of Massachusetts Lowell.

W. Ken Fang, Ph.D.

Senior Vice President for Drug Discovery

W. Ken Fang, Ph.D.

A research scientist with over 20 years of experience of designing new innovative drugs for treating neuropathic pain, CNS diseases, inflammation, ocular and skin diseases. Dr. Fang has comprehensive expertise in small molecule HTS hit identification, lead generation, and optimization based on target selectivity, ADME, toxicology, preclinical safety, and in vivo pharmacology. He has extensive experience in parallel synthesis techniques, and broad experience in preparation of patent applications and publication manuscripts with over 52 patents.

Ashley Meyer, M.Sc.

Principal Informatics Scientist

Ashley Meyer, M.Sc.

Ashley Meyer acquired her B.S. in Biology from the University of Arkansas at Little Rock (UALR), and her M.S. in Biology with an emphasis in Evolutionary Ecology from the University of Louisiana at Monroe (ULM). Her varied specialties include bacterial culturing and testing, radio telemetry, transmitter and PIT tagging, and collection and curation of morphometric data for invertebrates. Meyer served as an instructor of Microbiology and Human Anatomy and Physiology at ULM and Pulaski Technical College.

Max Sharifi, Ph.D.

Principal Computational Chemist

Max Sharifi, Ph.D.

As a Principal Computation Chemist, Dr. Sharifi leads computational modeling and molecular design operations at Spektron. His expertise is in developing computational models of efficacy and toxicity models using a variety of machine learning techniques. He works closely with Medicinal chemists and pharmacologists to utilize Spektron’s Q-MAP platform to develop computational chemistry-driven decision-making for molecular generation.

Iva Stoyanova-Slavova, Ph.D.

Principal Computational Chemist

Iva Stoyanova-Slavova, Ph.D.

As a principal computational chemist, Iva is responsible for computational modeling on various activity endpoints including human-level toxicities and efficacies. She works directly with machine learning experts to develop tools for discovering toxicophores and pharmacophores. Dr. Slavova’s expertise makes her a leader in developing the processes and technologies utilized at Spektron.
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Jessica Heidrich

Welcome Iva!

We would like to give a big welcome to Iva for joining Spektron full time! Iva is one of our Computational Chemists and a great

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News
Tim Dockins

Exploring Charles River Laboratories

Spektron Systems has actively engaged in developing synthesis and test plans on five representative molecules representing designs for an antipsychotic medication. Primarily for the purposes

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Tech
Tim Dockins

Tox21 Data Challenge Results

This page contains a summary and description of Q-MAP™ modeling results against the twelve Tox21 data challenge endpoints. It also compares results from competitors (to the extent they are known). All results here have been compiled against the predefined set of test molecules that were used in the Tox21 data challenge. The Tox21 data challenge is the most commonly referenced modeling challenge in predictive toxicology. As such, achieving a good performance against the Tox21 test set is a powerful statement about our platform’s predictive modeling skills.

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Tech
Tim Dockins

Platform Substantiation – an analogy

Our initial test results suggest that the platform will produce leads to pre-clinical testing and clinical trials that have a strong success profile. Another way to look at it is that our race car has the power, the handling, and the drive to cross under the checkered flag.

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Contact Us

Mailing Address
400 West Capitol Ave
17th Floor
Little Rock, AR 72201
(800) 972-0751

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