Q-MAP Adds Quantum
Our methods tractably inject quantum information about molecules into our modeling enabling complementary and explainable models related to biological effect.
Best-in-class drug discovery advanced by AI/ML
Fusing machine learning with human expertise for target-based drug design to dramatically reduce time and cost to novel lead generation and optimization.
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Causal Inference driven by multiple endpoints
A multi-model approach to the design of novel molecules combining multiple endpoints including efficacy, toxicity, and ADME.
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Seeking Strategic Partnerships

Spektron Systems seeks a select few strategic partners to collaboratively deploy Q-MAP’s powerful, AI-based Drug Discovery capacity.

  •  Novel lead generation from data associating outcomes with structures
  •  Moiety and sub-moiety causal inference with efficacy, toxicity, and ADMET outcomes
  • De-risking of leads through sales/marketing via multi-parameter optimization
  • Predictive ADMET modeling and off-target identification
  • ID of candidates for drug repurposing

Extending our collaborative work with the National Center for Toxicological Research, Spektron Systems has developed Q-MAP™ – a platform for AI-assisted, target-based drug design.  This platform designs best-in-class novel molecules at 25% of the cost and time of conventional drug discovery.

AI for Target-based Drug Design

Fast, Iterative Drug Design

The Q-MAP™ platform takes a multi-model approach to reduce uncertainty in predictions for clinical results; modeling across multiple endpoints from proposed mechanism of action to measured clinical metric.  With active moiety inference and predictive ADMET models, Q-MAP can generate novel molecules optimized for multiple parameters before synthesis.  By prioritizing in silico leads and de-risking them before synthesis, we can reduce costs and shorten discovery timelines from biology to preclinical candidates (PCC) from 4.5+ years to approximately 1 year and to Investigational New Drug (IND) within 3.5 years.

Multi-Endpoint Design

Q-MAP models multiple endpoints, both on-target and off-target, to pre-optimize molecule design and identify related structural moieties.

Novel Compounds Generation

Our approach enables an informed building block method for generating a library of novel molecules enriched by modeling on relevant endpoints.


In a rapid and lean Design-Make-Test-Analyze approach, Q-MAP actively learns from results as molecules are designed, synthesized, and tested physically against a key profile.

The Q-MAP platform, not based on chemical structures, is able to create novel, potent drug candidates which are structurally unrelated to the existing drugs and, simultaneously have adequate or superior biological properties, including receptor affinities.

Tractable qHTS Data Analysis

Qualitative data meets quantum data

Q-MAP™ methodology is tractable without expensive computational costs.  This allows for fast analysis of large bespoke data sets generated from qHTS assays.  Our methods identify active moieties related to custom endpoints and help establish a vocabulary for building novel molecules.

Predictive Modeling and Virtual Screening

Machine learning applied to curated, small-molecule data

Data - Big and Small

The world of chemistry and pharmaceutical development generates many data sets.  Some are Big Data and some are small data sets. Being able to work with small datasets where Big Data algorithms fail is crucial to modeling against endpoints where tests are invasive or costly.

Machine Learning

Q-MAP’s predictive modeling is based on applied machine learning theory combined expertise in computational chemistry, pharmacology, and software development.  


Q-MAP is built on tractable learning methods using best practices in applied machine learning, software engineering, and computational chemistry.


First Synthesized Compound

Spektron’s first compound, designed through our Q-MAP™ has been synthesized by one of our trusted CRO partners. This compound SPK003583 was designed based on bispecific

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Spektron Systems in NYC panel at BIO CEO Summit

Dr. David Wolf will be speaking on a panel presentation about AI in Healthcare and Spektron’s unique approach to medicinal molecule design. This invite-only presentation will be attended by investors looking to learn more about AI in this industry. Hosted by IMPACTIVE BioConsult, this presentation appends the 2019 BIO CEO SUMMIT and is being held 7:00 pm at Morgan Stanley.

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Dr. Wolf backstage at a Beryl Elites Investor Conference

Dr. David Wolf, the President and Chief Medical Officer for Spektron Systems, sat on the panel with other distinguished speakers talking about Artificial Intelligence. In this backstage interview clip, he about the revolutionary impact that AI is having in many industries. He addresses one of the key components of any good machine learning implementation – “Curation! Curation! Curation!”

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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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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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Contact Us

Mailing Address
417 Main St, Suite 129
Little Rock, AR 72201
(800) 972-0751

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