Life sciences strategy
Jorge Emilio Alfonso, MBA
Founder and CEO
Target-based • Small molecules • Automated • No-code
From protein target to prioritized small-molecule candidates.
PreditX® is a cloud-native, no-code AI discovery platform that helps teams move from a biological target to ranked, ADMET-informed candidate shortlists through automated target-data retrieval, scaffold-aware machine learning, predictive screening, and developability triage.
Built to reduce manual screening, focus experimental resources, and help teams identify which candidates deserve follow-up earlier.
Target-based
Start from a biological target
Small molecules
Focus on candidate compounds
No-code
No internal ML team required
Small-molecule AI discovery
Automated discovery decision support
From biological target selection to ranked, reviewable small-molecule candidates.
Target-driven workflow
Automated candidate prioritization
Decision-ready outputs
The value of better prioritization
PreditX® helps teams focus experimental resources earlier by ranking candidates according to predicted activity, model confidence, consensus signals, and developability context.
Early discovery challenge
Early small-molecule discovery is constrained by scale, cost, time, and attrition.
Drug discovery often starts with a promising biological target, but teams quickly face a practical question: which small molecules are worth testing? PreditX® is designed to help teams move from broad scientific possibility to a smaller, more reviewable set of candidates for follow-up.
Target complexity
A promising target is only the beginning.
The bottleneck is deciding which compounds deserve experimental attention before cost, time, and biology narrow the opportunity space.
Vast chemical space
The number of possible small molecules is too large for manual exploration or broad experimental testing.
High attrition
Many candidates fail after resources have already been committed to synthesis, screening, or follow-up.
High validation cost
Testing low-priority compounds increases experimental burden and can slow down better opportunities.
Long timelines
Disconnected tools, manual data preparation, and slow triage delay early discovery decisions.
Cloud-native discovery engine
A cloud-native engine for target-to-candidate prioritization.
PreditX® transforms early discovery from a fragmented sequence of manual tasks into a structured cloud workflow. The platform connects target intelligence, scaffold-aware model development, automated ML training, consensus screening, ADMET-informed triage, and direct compound sourcing to help teams move from a biological target to review-ready small-molecule candidates.
Discovery flow
Target→Evidence→ML training→Consensus screening→PAINS-free purchasable compounds→ADMET triage→Candidate shortlist
What the engine enables
- Move from a selected protein target to candidate prioritization
- Train and compare target-specific ML models without coding
- Screen user molecules or curated purchasable compound space
- Use consensus prediction to improve prioritization confidence
- Combine activity prediction with ADMET and developability context
Target biology
Start from a biological target
PreditX® begins with the biological question that matters most: which small molecules are worth exploring for a selected target?
Target-centered project setup
Relevant compound evidence gathered around the target
Clear starting point for prediction and prioritization
Why this matters
Target-based discovery keeps the workflow focused on a defined biological mechanism instead of starting from random compound exploration.
Molecule space
Explore chemical possibilities
The platform helps move from a broad molecule universe to a focused set of small-molecule candidates that can be predicted, profiled, and reviewed.
Screen existing compound libraries
Support new molecule exploration
Focus on candidates with stronger follow-up potential
Discovery value
PreditX® helps reduce chemical search space by turning broad molecular possibilities into candidates that can be ranked and reviewed.
Decision support
Turn candidate lists into clear follow-up decisions
PreditX® helps teams interpret candidate outputs in a practical way: which molecules should move forward, which need review, and which should be deprioritized before experimental testing.
Advance
Higher priorityCandidates with stronger predicted target relevance and a more attractive early developability profile.
Review
Needs expert reviewMolecules that remain interesting but need closer scientific, medicinal chemistry, or project-specific assessment.
Deprioritize
Lower priorityCandidates with weaker prioritization signals or early concerns that may make them less suitable for immediate follow-up.
Practical output
A ranked and reviewable candidate shortlist that supports internal project meetings, experimental planning, partner discussions, and early go / no-go decisions.
Modules
Active discovery modules today, expandable intelligence tomorrow.
PreditX® currently supports predictive screening and ADMET-informed developability review. Generative AI is positioned as a forward module in development, designed to expand the platform beyond screening toward new molecule ideation.
Decision-ready outputs
What the current PreditX® workflow delivers.
The current PreditX® workflow focuses on practical early discovery outputs: target-specific predictive models, ranked molecule shortlists, consensus prediction reports, ADMET-informed workbooks, and structured files for scientific review.
Platform expansion roadmap
From prediction to design, refinement, and optimization.
PreditX® will continue to expand around its predictive core. The roadmap adds Gen-AI molecular proposal, docking-based refinement, derivative optimization, and advanced generator upgrades in a phased architecture.
Long-term vision
Toward physical validation and a closed discovery loop.
Pharmaeconomica’s long-term vision is to connect PreditX® cloud prediction with automated experimental validation, enabling model outputs and laboratory results to reinforce each other over time.
The goal is to evolve from digital prioritization toward a self-improving discovery flywheel where prediction, experimental evidence, and model refinement continuously inform each other.
PreditX Cloud Engine
Generates, ranks, and triages candidate molecules.
Experimental validation
Future physical testing can generate evidence for model refinement.
Learning feedback loop
Results can feed back into cloud models to improve future prioritization.
Team
Built by a multidisciplinary leadership team.
PreditX® combines life sciences strategy, computational chemistry, and AI/ML expertise to translate target-based discovery workflows into a practical cloud platform.
