Drug Safety · Explainable AI

AI that predicts drug allergy risks — before they happen.

Healware gives clinicians molecular-level risk scores with visual explanations, before any prescription is ever made.

The Problem

A gap doctors cannot close with today's tools.

Hidden cross-allergies between structurally similar drugs are impossible to predict reliably without molecular-level AI analysis. The consequences are fatal.

1.25M

serious adverse drug reaction cases reported every year worldwide

175K

preventable deaths annually from drug reactions, many involving undetected cross-allergies

proactive, explainable, molecular-level allergy prediction tools available to clinicians today

How It Works

Three steps to proactive safety.

Chemical Structure Analysis

The AI scans each drug's molecular structure for hidden similarities with known allergens — like beta-lactam rings or sulfonamide groups that trigger cross-reactions.

Protein Interaction Modeling

We model how the immune system could respond at the biological level — identifying high-risk protein binding patterns before any drug is administered.

Explainable Risk Score

Clinicians receive a visual risk score with molecular highlights — showing exactly why a drug may be dangerous and which safer alternatives exist.

Capabilities

Built for real clinical decisions.

No existing tool combines proactive molecular analysis, explainable AI, and clinical workflow integration — Healware does all three.

HEALWARE RISK ANALYSIS

Known Allergen

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Candidate Drug

ALLERGY RISK SCORE

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Select a combination above to see cross-allergy risk analysis.

Molecular-level cross-allergy prediction powered by structural drug analysis

Healware analyses drugs using molecular fingerprints and structural similarity algorithms to identify cross-reactivity risks before a prescription is made.

Hybrid decision engine combining clinical guidelines with AI prediction

The system first checks evidence-based clinical rules and, when data is missing, falls back to machine learning models trained on molecular patterns.

Explainable predictions with visual molecular reasoning

Each risk score is supported by interpretable outputs, highlighting specific substructures and features that contribute to the prediction.

Real-time safe alternative generation based on structural dissimilarity

Healware suggests safer medications by comparing molecular distance from the known allergen and ranking viable alternatives.

Interactive molecular exploration for deeper clinical understanding

Doctors can inspect 3D structures and feature-level explanations to understand why a drug is safe or risky.

Region-aware system with local drug mapping and guideline integration

The platform resolves active compounds and maps them to local drug names, ensuring recommendations match regional clinical practice.

Pricing

The cost of
using Healware?

We monetize through a scalable SaaS and API model — built for individual clinicians up to entire hospital systems.

Starter

Individual clinicians · Up to 500 checks/month

Get started

Professional

Hospital departments · Unlimited checks + EMR integration

Get started

Enterprise

Hospital systems & pharma · White-label + API + SLA

The Team

Built by researchers
who care about patients.

Our story

"Friends since the day we got our UCL offers — three years of building together." From late-night study sessions to building Healware, Kate, Joyce and Victoria have shared every step. That history means deep trust, fast decisions, and a founding team that doesn't fracture under pressure.

Kate Baleiko

Co-Founder · CEO

UCL Computer Science · Research at Microsoft

Joyce Kong

Co-Founder · CTO

UCL Computer Science · Research at HK Robotics Institute

Victoria Smirnova

CPO & Co-Founder

BSc Computer Science, UCL · LLM research (Intel Capital) · Product & roadmap

Advisors

Healware is backed by practicing physicians, allergists, and immunologists who validate our science — alongside experienced startup and cleantech advisors who guide our growth strategy.