Now Enrolling — Pilot Cohort Q2 2026

The Fast
and the
Curious

AI Upskilling for Biotech Scientists

An 8-week, hands-on program that transforms bench scientists into confident AI practitioners — no CS degree required. Predict protein structures, design molecules with generative AI, and build a real portfolio.

8Weeks
~5 hrsPer week
$2,500Full program
20Max cohort
Brought to you by Scriptome.AI
Merelogic
Pilot cohort capped at 15–20 scientists

Reserve Your Spot

Join the waitlist for our first cohort — launching Q2 2026.

The Problem

AI is reshaping biotech.
Most scientists are getting left behind.

The tools are here — AlphaFold, Boltz, generative molecular design, LLM-powered literature mining. They’re already in leading labs, and the skills gap is growing fast.

+35%
Computing & IT growth in MA biotech

The fastest-growing segment in life sciences

Demand for AI-fluent scientists is outpacing supply across every therapeutic area. Companies are hiring for skills most bench scientists have never been taught.

4–5×
Reskilling demand vs. new hiring

The math doesn’t work without training existing talent

2,400+ MA bench scientists need reskilling. Only 512 new hires can fill that gap. That means you need these skills — and you need them now.

$15K+
Average cost of existing AI programs

Built for the wrong audience

Most courses take 6–12 months, assume a CS background, and cost a small fortune. That doesn’t work for someone running experiments full-time.

What You’ll Learn

Six skills. Eight weeks.
Zero CS background required.

Every module is built around real drug R&D workflows. You leave with working skills — not just a certificate.

Weeks 1–2

Protein Structure Prediction

Use Boltz and AlphaFold hands-on. Build a structure prediction portfolio piece using your own research targets — no coding required.

Week 3

Generative Molecular Design

Design novel drug-like compounds with generative AI tools. Understand how they fit your discovery pipeline from day one.

Week 4

ADMET Prediction & Drug-Likeness

Evaluate compound viability early using AI to predict absorption, distribution, metabolism, excretion, and toxicity.

Week 5

LLM-Powered Literature Mining

Find research gaps 10x faster than manual search. Synthesize hundreds of papers in the time it takes to read ten.

Week 6

Data Analysis & Visualization

Turn raw experimental data into insights using low-code Python notebooks — copy, paste, and modify. No from-scratch coding.

Weeks 7–8

Vendor Evaluation & Capstone

Evaluate AI tools critically, then apply everything to a real problem from your own research for your final portfolio piece.

Full Curriculum

Week-by-week breakdown

No-code to low-code progression. Each week has a concrete deliverable — you build a portfolio as you go.

Phase 1 · Weeks 1–3 No-code tools — web interfaces & Google Colab
Week
Topic
Format
Deliverable
01
AI Foundations for Drug Discovery
In-person workshop
AI readiness assessment
02
Protein Structure Prediction (Boltz & AlphaFold)
Hands-on lab session
Structure prediction portfolio piece
03
Molecular Design with Generative AI
Hands-on lab session
Novel molecule design
Phase 2 · Weeks 4–6 Low-code Python notebooks — copy / paste / modify
Week
Topic
Format
Deliverable
04
ADMET Prediction & Drug-Likeness
Online + office hours
Compound evaluation report
05
Literature Mining with LLMs
Hands-on lab session
Research gap analysis
06
Data Analysis & Visualization
Online + practice
Data visualization project
Phase 3 · Weeks 7–8 Real projects, industry panel & capstone showcase
Week
Topic
Format
Deliverable
07
AI Tool Evaluation & Vendor Selection
Workshop + case studies
Vendor evaluation rubric
08
Capstone Project Presentations
In-person showcase
Final portfolio presentation
Why It Works

Built for biologists learning AI —
not data scientists learning biology.

🎯

Problem-First, Not Theory-First

We start with “how do I predict this protein structure?” not “what is a neural network?” Theory only when you need it to solve a real problem.

📈

No-Code to Low-Code Progression

Weeks 1–3: web interfaces only. Weeks 4–6: Python notebooks (copy/paste). Weeks 7–8: customize. Confidence builds naturally.

🔬

Bring Your Own Research Problem

Apply every tool to challenges from your actual job. Peer learning across therapeutic areas. Your capstone impresses your current employer.

🛡

Vendor-Agnostic & Open-Source

We teach critical evaluation, not platform dependence. No lock-in, no upsells. Everything runs on free-tier tools including Google Colab.

🏭

Industry Guest Instructors

Real data scientists and AI leads from MA biotechs join as guest instructors. Hear what companies actually want — from the people doing the hiring.

⚡

50%+ Hands-On Time

Every concept is demonstrated with real drug discovery tools. Fail-forward culture. Real datasets, real problems, real portfolio pieces.

How We Compare

83% less than Stanford.
Twice the hands-on time.

ProgramDurationCostHands-On?Biotech-Specific?
MIT AI in Pharma6 weeks$2,800LimitedYes
Stanford AI Certificate6–12 months$15,000+ModerateNo
Coursera BioinformaticsSelf-paced$49–79/moLimitedPartial
▶ The Fast & The Curious8 weeks$2,500Extensive (50%+)100% Biotech
Pricing

Priced for real scientists,
not corporate training budgets.

Transparent pricing, no hidden fees.

Individual

$2,500

per participant · full 8-week program

Complete curriculum, hands-on sessions, industry panel, and certificate of completion.

  • 8 weeks of live instruction
  • All tools & materials included
  • Certificate of completion
  • Capstone portfolio project
  • Slack community access
Join Waitlist
Best Value

Corporate Team

$10,000

for 5 employees · 20% team discount

Upskill your team together. Shared cohort experience builds internal AI culture at your company.

  • Everything in Individual
  • 20% savings vs. individual rate
  • Dedicated team office hours
  • Manager progress reporting
  • Priority access to future cohorts
Join Waitlist

Need-Based Scholarship

Free

3 full scholarships per cohort

Funded by industry partners. Cost should never be a barrier for scientists who need these skills most.

  • Full program access
  • Industry-sponsored
  • Application-based selection
  • Same curriculum & support
Apply for Scholarship
Your Instructors

Taught by practitioners.
Not academics.

Both instructors have spent years in the field — in labs, in startups, and building AI tools for real drug discovery.

Stu Angus

Stu Angus

Founder & Lead Instructor

Scriptome.AI · Cambridge, MA

15+ years in drug development at Sanofi Genzyme and Hopewell Therapeutics. MBA in Business Analytics (Isenberg). Host of the Scriptome Podcast and speaker at MIT’s Internet of AI Agents Conference.

Jesse Johnson

Jesse Johnson

Co-Instructor & Co-Promoter

Merelogic

Jesse brings deep expertise in computational biology and practical AI applications across the drug discovery pipeline — adding industry depth and a complementary perspective on AI tools for life sciences professionals.

“The talent supply-demand imbalance persists — especially in AI integration, technology, and machine learning. A clear need for the industry.”

— Massachusetts Life Sciences Executive, MassBioEd Report 2025

Pilot Cohort · Q2 2026

Ready to become an
AI-fluent scientist?

Join the waitlist. Get first access to enrollment and early-bird pricing.

Pilot cohort capped at 20 scientists